Road emission cooperative control method and system based on traffic flow prediction

Abstract

The application provides a road emission collaborative control method and system based on traffic flow prediction, relates to the technical field of traffic management and environmental control, and anchors road emission concentrated superposition risk scenarios based on traffic flow prediction data, generates a risk scenario space-time identifier containing a potential road section associated link, a high-occurrence time period sequence and an influence range; generates a multi-road section intersection regulation and control action linkage scheme integrating various operation sequences and associated relationships based on the risk scenario space-time identifier; generates a time period execution process according to the multi-road section intersection regulation and control action linkage scheme; starts a multi-agent synchronous execution program and collects actual execution and real-time operation feedback data; generates updated regulation and control instructions and continuously executes to form a control cycle according to the data adjustment scheme and the process. The application realizes collaborative optimization control of road traffic flow and emission, and effectively reduces vehicle emission pollution.

Description

Technical Field

[0001] This invention relates to the field of traffic management and environmental control technology, and more specifically, to a method and system for coordinated control of road emissions based on traffic flow prediction. Background Technology

[0003] Currently, traffic flow management and vehicle emission control are mostly implemented independently. Traffic flow management primarily relies on traffic signal control systems, which regulate vehicle passage at intersections according to pre-set signal timing schemes. However, these control methods often lack the ability to predict future changes in traffic flow and struggle to cope with complex and ever-changing traffic conditions. Vehicle emission control typically involves setting strict emission standards and restricting high-emission vehicles. However, these measures do not fully consider the dynamic relationship between traffic flow and emissions, and cannot effectively address the problem of concentrated emissions accumulation.

[0004] Existing traffic management and emission control methods lack multi-stakeholder collaboration and dynamic adjustment mechanisms. Poor information communication between departments makes it difficult to form a unified control strategy, resulting in low efficiency when dealing with the risk of concentrated road emissions and failing to achieve coordinated and optimized control of road traffic flow and emissions. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a road emission coordinated control method based on traffic flow prediction, the method comprising: Based on traffic flow prediction data, road emission concentration superposition risk scenarios are identified, and spatiotemporal identifiers of risk scenarios are generated. The spatiotemporal identifiers of risk scenarios include the associated links of potential road segments with concentrated emission superposition, the high-incidence time sequence of concentrated emission superposition, and the impact range of concentrated emission superposition. Based on the spatiotemporal identifier of risk scenarios, a multi-segment intersection control action linkage scheme is generated. The control action linkage scheme integrates the operation sequence and correlation of intersection signal control parameter adjustment operation, road segment traffic guidance path planning scheme and traffic information push execution operation. The time-segmented execution process is generated according to the control action linkage scheme. The time-segmented execution process includes a list of road sections and intersections participating in the control in each time period, specific control action content, and action linkage trigger sequence. The multi-entity synchronous execution procedure is initiated according to the time-segmented execution process, and the actual execution data of each control action and the real-time operation feedback data of road sections and intersections are collected. Based on actual execution data and real-time operational feedback data, the control action linkage scheme and time-segmented execution process are adjusted, updated control instructions are generated and continuously executed, forming a road emission coordinated control cycle.

[0006] Furthermore, embodiments of the present invention also provide a road emission collaborative control system based on traffic flow prediction, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described road emission coordinated control method based on traffic flow prediction by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions stored in a computer-readable storage medium, a processor of a road emission cooperative control system based on traffic flow prediction reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the road emission cooperative control system based on traffic flow prediction to execute the above-described road emission cooperative control method based on traffic flow prediction.

[0008] Based on the above, and guided by traffic flow prediction data, this approach accurately identifies and generates spatiotemporal identifiers for concentrated road emission risk scenarios. A multi-segment intersection control action linkage scheme, generated based on these identifiers, integrates the operational sequence and relationships of various operations, including intersection signal control parameter adjustment, road segment traffic flow guidance path planning, and traffic information dissemination. Following the time-segmented execution flow generated by the control action linkage scheme, the scheme clearly defines the list of participating road segments and intersections, specific control actions, and action linkage trigger sequences for each time period. This ensures a clear temporal and spatial sequence for the control process. By initiating a multi-entity synchronous execution procedure and collecting actual execution data and real-time operational feedback data, the control effect and road operation status can be promptly assessed. Adjustments to the control action linkage scheme and time-segmented execution flow based on this data generate updated control instructions for continuous execution. This allows for real-time optimization of control strategies based on actual conditions, effectively addressing dynamic changes in traffic flow and road emissions. This significantly improves the accuracy, timeliness, and effectiveness of coordinated road emission control, achieving optimized coordination between road traffic flow and emissions, and reducing vehicle emissions pollution to the environment. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the road emission collaborative control method based on traffic flow prediction provided in an embodiment of the present invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of a road emission collaborative control system based on traffic flow prediction provided in an embodiment of the present invention. Detailed Implementation

[0011] Figure 1This is a flowchart illustrating a road emission coordinated control method based on traffic flow prediction provided in one embodiment of the present invention, which will be described in detail below.

[0012] Step S110: Using traffic flow prediction data as a guide, identify the risk scenarios of concentrated road emissions and generate a spatiotemporal identifier for the risk scenario. The spatiotemporal identifier for the risk scenario includes the associated links of potential road sections with concentrated emissions, the high-incidence time sequence of concentrated emissions, and the impact range of concentrated emissions.

[0013] In urban traffic management scenarios, traffic flow forecasting data is needed to identify potential risk scenarios of concentrated road emissions. First, traffic flow forecasting data for a future period is acquired, encompassing changes in traffic volume, vehicle movement patterns, and turning traffic at intersections for various road segments at different times. Next, combined with basic road data, such as the number of lanes, the distribution of surrounding buildings, and intersection connections, the traffic flow data is analyzed in depth. By analyzing the relationship between traffic volume changes and lane capacity, the traffic pressure on road segments is assessed; based on the spatial relationship between vehicle movement patterns and the distribution of surrounding buildings, emission characteristics are extracted; and based on the compatibility between the scale of turning traffic at intersections and the intersection connection structure, traffic convergence data is generated. Combining these analytical results, potential initial road segments for concentrated emissions are selected. Starting from these segments, directly and indirectly related road segments are integrated to form a link between potential concentrated emission aggregation road segments. Simultaneously, peak traffic volume data for each road segment at different times is extracted from the traffic flow forecasting data, and the high-incidence time sequence for concentrated emissions is determined based on the frequency and duration of peak occurrences. Finally, by combining the coverage of the road segment's associated links and the time span of the high-incidence period sequence, the range of concentrated and superimposed emission impact is determined. Integrating these three parts generates a complete spatiotemporal identifier for the risk scenario.

[0014] Step S111: Obtain traffic flow prediction data, which includes traffic flow change data, vehicle driving status data, and intersection turning traffic data for each road segment at different times.

[0015] Traffic flow prediction data is acquired through intelligent traffic monitoring systems deployed in urban road networks. These systems integrate historical traffic data, real-time road condition information, and influencing factors such as weather and holidays, generating traffic flow prediction results for specific future time periods through traffic flow prediction models. Traffic flow change data is a three-dimensional array structure. The first dimension is the road segment number (M road segments in total), the second dimension is the time slice (N time slices are defined by fixed time intervals), and the third dimension is the traffic flow value (unit: vehicles / hour). For example, the traffic flow change data for road segment Lx in time slice Ty is represented as Lx_Ty_flow value. Vehicle driving status data includes four features for each road segment in each time slice: average vehicle speed, standard deviation of vehicle speed, percentage of rapid accelerations, and percentage of rapid decelerations. The data format is an M×N×4 matrix, where the average vehicle speed is in kilometers per hour, and the percentage of rapid accelerations / decelerations is in percentage form. The intersection turning traffic flow data is based on the intersection. Each intersection includes four entrance directions: east, south, west, and north. Each entrance direction includes three turns: left turn, right turn, and straight ahead. Each turn corresponds to the predicted traffic flow for N time slots. The data structure is a four-dimensional array of intersection number × entrance direction × turn type × time slot, with the unit being vehicles / time slot.

[0016] Step S112: Collect basic road data for each road segment, including data on the number of lanes in the road segment, data on the distribution of surrounding buildings, and data on the connection forms of intersections.

[0017] Road infrastructure data is retrieved from a city geographic information system (GIS) database using a standardized data interface. Lane count data for each road segment is assigned an integer parameter; for example, lane count C for arterial road A indicates that the segment contains C motor vehicle lanes (excluding non-motorized vehicle lanes and sidewalks). Surrounding building distribution data is obtained using a buffer analysis method. Based on the road segment centerline, a buffer zone is formed by extending a specific distance to both sides. The building type and area percentage within the buffer zone are statistically analyzed. The data format is a quadruple representing the percentage of commercial buildings, residential buildings, public facilities buildings, and industrial buildings corresponding to the road segment number, with the sum of each percentage being 100%. Intersection connection data is represented using topological structure coding, including three attributes: intersection type (e.g., cross, T, Y), number of approach lanes, and number of exit lanes. For example, the connection data for cross intersection D is coded as "cross-EF," indicating that the intersection has a cross structure with E approach lanes and F exit lanes.

[0018] Step S113: Match the traffic flow change data of road segments in the traffic flow prediction data with the number of lanes of road segments in the road basic data, and generate road segment traffic pressure distribution data according to the correspondence between the traffic flow change magnitude and the lane carrying capacity.

[0019] First, a correlation index is established between traffic flow prediction data and basic road data using unique road segment identifiers (such as road segment IDs) to ensure that the traffic flow change data for each road segment can be accurately matched with the corresponding lane number data. Lane capacity is calculated using the formula: Lane capacity = Number of lanes × Baseline lane capacity × Lane type coefficient, where the baseline lane capacity is the standard value in urban road design specifications, and the lane type coefficient is determined according to the road segment level (G for arterial roads, H for secondary arterial roads, and I for local roads). The traffic flow change amplitude is calculated as follows: the absolute value of the difference between the current time segment traffic flow and the previous time segment traffic flow is divided by the previous time segment traffic flow to obtain the relative change amplitude (in percentage form). The calculation rule for the road segment traffic pressure index is as follows: when the traffic flow change amplitude > lane capacity × J, pressure index = traffic flow change amplitude / (lane capacity × K), with the result rounded to two decimal places; when the traffic flow change amplitude ≤ lane capacity × J, pressure index = L × (traffic flow change amplitude / (lane capacity × J)). The traffic pressure distribution data for road segments is an M×N matrix. Each element represents the traffic pressure index of the corresponding road segment in the corresponding time slot. The index ranges from 0 to P, and the higher the index, the greater the traffic pressure.

[0020] Step S114: Correlate the vehicle driving status data in the traffic flow prediction data with the surrounding building distribution data in the road basic data, and extract emission generation feature data according to the spatial relationship between driving status and building distribution.

[0021] A feature fusion method is used for correlation processing. First, the three features in the traffic flow data—average speed, percentage of rapid accelerations, and percentage of rapid decelerations—are standardized (each feature value is subtracted from the mean of all time slices for that road segment and then divided by the standard deviation). The percentages of commercial buildings, residential buildings, and public facilities in the surrounding building distribution data are multiplied by weighting coefficients (commercial buildings Q, residential buildings R, public facilities S) to form building impact factors. Emission generation feature data includes three dimensions: idling emission factor, acceleration emission factor, and overall emission intensity. The idling emission factor is calculated as: (1 - standardized average speed) × T + percentage of residential buildings × U; the acceleration emission factor is calculated as: standardized percentage of rapid accelerations × V + percentage of commercial buildings × W; the overall emission intensity is the weighted sum of the idling emission factor and the acceleration emission factor (weights are X and Y, respectively). The emission generation feature data structure is an M×N×3 matrix, where each element is a dimensionless value between 0 and 1.

[0022] Step S115: Combine the intersection turning traffic flow data in the traffic flow prediction data with the intersection connection form data in the road basic data, and generate traffic flow convergence data according to the adaptation relationship between the turning traffic flow scale and the intersection connection structure.

[0023] First, the intersection turning traffic flow data is normalized. The number of each turning traffic flow is divided by the total number of turning traffic flows at that intersection to obtain the relative turning ratio. The intersection connection structure adaptation coefficient is determined based on the intersection type and the number of approach lanes. The basic adaptation coefficient is Z for cross-shaped intersections, AA for T-shaped intersections, and AB for Y-shaped intersections. For each additional approach lane, the adaptation coefficient increases by AC (up to AD). The traffic convergence index is calculated as follows: the sum of the products of the relative ratio of each turning direction and the number of lanes corresponding to that turning direction, multiplied by the intersection connection structure adaptation coefficient. For example, if a cross-shaped intersection has AE approach lanes, the relative ratio of left-turning traffic flow at the east approach is AF, and the corresponding number of left-turning lanes is AG, then the contribution value of this turning direction is AF × AG. The sum of all turning contribution values ​​multiplied by Z (cross-shaped intersection adaptation coefficient) yields the traffic convergence index. The traffic convergence data is a matrix of the number of intersections × N. Each element represents the traffic convergence index of the corresponding intersection in the corresponding time slice. The higher the index, the more severe the traffic convergence.

[0024] Step S116: Based on the road segment traffic pressure distribution data, emission generation characteristic data and traffic flow convergence data, select and generate potential initial road segments with concentrated emission overlay.

[0025] Three screening thresholds are set: traffic pressure index threshold AH (value AI), comprehensive emission intensity threshold AJ (value AK), and traffic convergence index threshold AL (value AM). For each road segment, the following conditions must be met simultaneously within each time slot: the road segment's traffic pressure index > AH, the comprehensive emission intensity > AJ, and the traffic convergence index of the road segment's associated intersections (the upstream and downstream AN intersections) > AL. Road segment-time slot combinations that meet the conditions are marked as potential risk points. Road segments with potential risk points appearing in more than AO consecutive time slots are marked as potential initial road segments with concentrated emission overlays. A sliding window method is used during the screening process, with a window size of AP time slots. When the proportion of potential risk points within the window exceeds AQ, the road segment is included in the initial road segment list.

[0026] Step S117: Starting from the initial potential road segment with concentrated emissions, obtain the traffic flow path of adjacent road segments, integrate directly related road segments and indirectly related road segments, and generate the associated link of the potential road segment with concentrated emissions.

[0027] First, obtain the road topology data of the potential initial road segment for emission concentration overlay, including the starting and ending intersections of the segment, as well as other road segments connected through these two intersections. Directly adjacent road segments are defined as those sharing an intersection with the initial road segment; these segments are identified by querying the intersection connection table. Traffic flow prediction data for directly adjacent road segments is collected, and the traffic flow exchange scale between the initial road segment and the directly adjacent road segment is calculated, i.e., the ratio of the traffic flow of the two road segments in the same time slice (the smaller of the two-way traffic flow is divided by the larger value). When the ratio > AR, it is determined that there is a high-frequency traffic flow exchange, and the directly adjacent road segment is included in the set of directly associated road segments. For each road segment in the set of directly associated road segments, its neighboring road segments (excluding those already included in the set) are found as indirectly adjacent road segments using the same method. The traffic flow transfer scale between the indirectly adjacent road segments and the directly associated road segments is calculated (using the same method as the traffic flow exchange scale calculation). When the transfer scale > AS, it is included in the set of indirectly associated road segments. Then, a directed graph structure is constructed to represent the traffic flow transmission path between road segments. Nodes represent road segments, directed edges represent traffic flow direction, and edge weights represent the scale of traffic exchange / transmission. Based on path length and weight, the main transmission paths are selected, and directly related road segments, indirectly related road segments, and their connections are integrated to form a link between potential road segments with concentrated emission overlay. The link data is stored in the form of an adjacency list, and each node contains a road segment ID, a list of related road segments, and corresponding weights.

[0028] Step S1171: Obtain the basic road data of the potential initial road segment of the emission concentration superposition, extract the connecting road segment information of the intersections at both ends of the potential initial road segment of the emission concentration superposition, and determine the directly adjacent road segments.

[0029] Detailed records of potential initial road segments overlaid in the emission set are retrieved from the road infrastructure database. These records include the starting intersection ID (AT intersection) and ending intersection ID (AU intersection) of each segment. The intersection connection table is used to query all road segments connected to each of the AT and AU intersections. After excluding the initial road segment itself, the remaining segments are considered directly adjacent. For example, if the initial road segment is AV, and its AT intersection connects to AW, AV, and AX, then after excluding AV, the directly adjacent segments are AW and AX. Similarly, if the AU intersection connects to AV, AY, and AZ, then after excluding AV, the directly adjacent segments are AY and AZ. The IDs of all directly adjacent road segments are stored in a temporary list, forming a candidate set of directly adjacent road segments.

[0030] Step S1172: Collect traffic flow prediction data of directly adjacent road segments and analyze the traffic flow exchange scale data of directly adjacent road segments and potential initial road segments with concentrated emissions.

[0031] Extract the traffic flow change data of all road segments in the candidate set of directly adjacent road segments from the traffic flow prediction database, and the time range is consistent with the risk period of the initial road segment (i.e., the set of time slices when the initial road segment is marked as a potential risk point). For each directly adjacent road segment BA and the initial road segment AV, calculate the traffic flow exchange volume for each time slice: when BA and AV are connected through the AT intersection, the exchange volume is the smaller value of the end flow of BA and the start flow of AV in this time slice; when connected through the AU intersection, the exchange volume is the smaller value of the start flow of BA and the end flow of AV in this time slice. The traffic flow exchange scale data is defined as the ratio of the average value of the exchange volumes of each time slice to the average flow of AV, and the formula is: traffic flow exchange scale = (sum of exchange volumes of each time slice / number of time slices) / (sum of flows of each time slice of AV / number of time slices). The calculation result is reserved to two decimal places, and a list of traffic flow exchange scale data between directly adjacent road segments and the initial road segment is formed.

[0032] Step S1173: Based on the traffic flow exchange scale data, determine the directly adjacent road segments with high-frequency traffic flow exchange with the potential initial road segments in the emission concentration superposition, and load them into the directly associated road segment set.

[0033] Set the traffic flow exchange scale threshold BB (value AR), traverse each road segment in the candidate set of directly adjacent road segments. When the traffic flow exchange scale between this road segment and the initial road segment > BB, it is determined that there is high-frequency traffic flow exchange. Store the road segment ID that meets the conditions and its traffic flow exchange scale value into the directly associated road segment set together. The data structure of the set is in dictionary form, with the key being the road segment ID and the value being the traffic flow exchange scale. For example, if the traffic flow exchange scale of the directly adjacent road segment AW is BC (> AR), then {AW: BC} is added to the directly associated road segment set; while the traffic flow exchange scale of AX is BD (< AR), it is not included in this set.

[0034] Step S1174: Extract the road basic data of each road segment in the directly associated road segment set, determine the adjacent road segments of the directly associated road segments, and obtain the indirectly adjacent road segments.

[0035] For each road segment BE in the directly associated road segment set, repeat the processing process of step S1171: retrieve the road basic data of BE, obtain its start intersection and end intersection, query all road segments connected by these two intersections, exclude BE itself and the road segments that are already in the initial road segment or directly associated road segment set, and the remaining road segments are the candidate set of indirectly adjacent road segments. For example, the road segments connected to the start intersection of the directly associated road segment AW are BF, AW, AV (AV is the initial road segment), and the road segments connected to the end intersection are AW, BG, BH. After excluding AW and AV, the candidate set of indirectly adjacent road segments is BF, BG, BH.

[0036] Step S1175: Collect traffic flow prediction data of indirectly adjacent road segments and analyze the traffic flow transfer scale data between indirectly adjacent road segments and directly related road segments.

[0037] Using a method similar to step S1172, traffic flow change data for all road segments in the candidate set of indirectly adjacent road segments are extracted (the time range is consistent with the risk period). For each indirectly adjacent road segment BI and directly related road segment BE, the traffic flow transfer volume is calculated: when BI and BE are connected through the starting intersection of BE, the transfer volume is the smaller value between the ending traffic volume of BI and the starting traffic volume of BE; when connected through the ending intersection of BE, the transfer volume is the smaller value between the starting traffic volume of BI and the ending traffic volume of BE. The traffic flow transfer scale is defined as the ratio of the average transfer volume of each time slot to the average traffic volume of BE, and the formula is: Traffic flow transfer scale = (sum of transfer volumes of each time slot / number of time slots) / (sum of traffic volumes of each time slot of BE / number of time slots), and the calculation result is rounded to two decimal places.

[0038] Step S1176: Based on the traffic flow transmission scale data, determine the indirect adjacent road segments that have continuous traffic flow transmission with the directly related road segments, and load them into the indirect related road segment set.

[0039] Set a traffic flow transfer scale threshold BJ (value AS). Iterate through each road segment in the candidate set of indirectly adjacent road segments. If the traffic flow transfer scale between the road segment and its corresponding directly associated road segment is greater than BJ, it is determined that there is continuous traffic flow transfer. Store the road segment ID that meets the condition, the corresponding directly associated road segment ID, and their traffic flow transfer scale value into the indirectly associated road segment set. The set data structure is in dictionary form, with the key being (indirect road segment ID, directly associated road segment ID) and the value being the traffic flow transfer scale. For example, if the traffic flow transfer scale between the indirectly adjacent road segment BG and the directly associated road segment AW is BK (>AS), then {(BG,AW):BK} is added to the indirectly associated road segment set.

[0040] Step S1177: Obtain traffic flow direction data for each road segment in the directly associated road segment set and the indirectly associated road segment set to form the traffic flow transmission path between road segments.

[0041] Traffic flow direction information (one-way or two-way) for each road segment is extracted from traffic flow prediction data. For two-way road segments, the flow data in the up and down directions are distinguished. Based on the connectivity of road segments and the scale of traffic exchange / transfer, directed transmission paths are constructed: For directly related road segments, the direction from the initial road segment to the directly related road segment is the forward path (weighted by the scale of traffic exchange), and the opposite direction is the reverse path (the weight needs to be recalculated based on the scale of reverse traffic exchange between the initial road segment and the directly related road segment); for indirectly related road segments, the direction from the directly related road segment to the indirectly related road segment is the forward path (weighted by the scale of traffic transfer). For example, the forward path weight from the initial road segment AV to the directly related road segment AW is BC, and the reverse path weight from AW to AV needs to be calculated based on the scale of traffic exchange between AV and AW; the forward path weight from AW to the indirectly related road segment BG is BK. All paths are stored in the format of starting road segment, ending road segment, direction, and weight to form a path list.

[0042] Step S1178: Generate a connection relationship map between road segments according to the order of traffic flow transmission paths. The connection relationship map includes the traffic flow input and output directions of each road segment.

[0043] A tree-like connection graph is constructed with the initial road segment as the root node, directly associated road segments as first-level child nodes, and indirectly associated road segments as second-level child nodes. Each node contains a road segment ID, a list of input road segments (the starting road segments of the paths pointed to by this road segment), and a list of output road segments (the ending road segments of the paths pointed to by this road segment). For example, the output road segment list of the initial road segment AV is [AW, AY] (directly associated road segments), the input road segment list of AW is [AV], and the output road segment list is [BG, BH] (indirectly associated road segments). The graph is stored using a combination of an adjacency matrix and a node attribute table. The adjacency matrix records the connection relationships between road segments (1 indicates a connection exists, 0 indicates no connection exists), and the node attribute table records the input and output directions and corresponding path weights of each road segment.

[0044] Step S1179: Based on the connection relationship map, determine the role of each road segment in traffic flow transfer as either a core transfer road segment or an auxiliary transfer road segment.

[0045] Calculate the betweenness centrality value of each road section. The betweenness centrality reflects the importance of a road section as an intermediate node in the entire transmission network. The calculation formula is the proportion of the road section located between all pairs of shortest paths. Set the betweenness centrality threshold BL (with a value of BM). When the betweenness centrality value of a road section > BL, it is determined as a core transmission road section; otherwise, it is an auxiliary transmission road section. For example, the betweenness centrality value of the directly associated road section AW is BN (> BM), then it is marked as a core transmission road section; the betweenness centrality value of the indirectly associated road section BG is BO (< BM), then it is marked as an auxiliary transmission road section. Add a "road section type" field to the node attribute table of the connection relationship graph to record the identification of core or auxiliary transmission road sections.

[0046] Step S11710: Integrate the set of directly associated road sections, the set of indirectly associated road sections, the vehicle flow transmission paths, the connection relationship graph, and the annotation results of core and auxiliary transmission road sections to generate a complete emission concentration superimposed with potential road section association links.

[0047] All the above data is integrated into a single structured data object containing the following fields: initial road segment ID, dictionary of directly associated road segments (including traffic flow exchange scale), dictionary of indirectly associated road segments (including corresponding directly associated road segments and transmission scale), list of directed traffic flow transmission paths (including start point, end point, direction, and weight), connection graph (adjacency matrix and node attribute table), list of core transmission road segments, and list of auxiliary transmission road segments. This data object is stored in JSON format to ensure that each part of the data is linked by a road segment ID, facilitating subsequent steps. For example, the JSON structure of the link data is: {"initial_road":"AV","direct_related_roads":{"AW":BC,"AY":BP},"indirect_related_roads":{"(BG,AW)":BK,"(BH,AW)":BQ,"(BR,AY)":BS},"traffic_flow_paths":[{"start":"AV","end":"AW","direction":"forward","weight":BC},...],"con nection_graph":{"adjacency_matrix":[[0,1,0,...],...],"node_attributes":[{"road_id":"AV","input_roads":[],"output_road s": ["AW", "AY"], "type": "initial"},...]}, "core_transfer_roads": ["AW", "AY"], "auxiliary_transfer_roads": ["BG", "BH", "BR"]}.

[0048] Step S118: Extract peak traffic flow data for different time periods from traffic flow prediction data for each road segment, and generate a sequence of high-incidence emission periods by summarizing peak occurrence frequency and duration.

[0049] Peak detection is performed on the traffic flow change data for each road segment using a sliding window maximum method. The window size is BT time slices. When the traffic flow value in a certain time slice is greater than the traffic flow value in the adjacent time slices, it is marked as a potential peak point. Potential peak points are filtered, and points with traffic flow values ​​exceeding BU times the daily average traffic flow of the road segment are retained as valid peak points. The number of road segments with valid peak points in each time slice (N segments) is counted to obtain the peak frequency distribution of the time slice. A frequency threshold BV (which is BW% of the total number of road segments) is set. When the peak frequency of a certain time slice is greater than BV, it is marked as a high-incidence candidate time slice. Consecutive high-incidence candidate time slices are merged to form time intervals. The duration of each interval is the number of time slices in the interval × the time slice interval. The above time intervals are sorted according to their start time to generate a sequence of concentrated and superimposed high-incidence periods. The sequence data format is a list containing multiple time intervals. Each interval contains three attributes: start time slice number, end time slice number, and duration (minutes).

[0050] Step S119: Combine the coverage of the potential road segment associated with concentrated emissions and the time span of the high-incidence period sequence of concentrated emissions to determine the scope of the impact of concentrated emissions.

[0051] The coverage area of ​​potential road segments associated with concentrated emissions is determined through geospatial analysis: the geographic coordinates (latitude and longitude of the starting and ending points) of all road segments in the link are imported into the GIS system to generate a minimum bounding polygon, the boundary of which represents the spatial coverage area. The time span of the high-incidence period sequence of concentrated emissions is from the start time slice of the first time interval to the end time slice of the last time interval, and the total duration is calculated (the sum of the durations of all intervals). The impact range consists of two parts: spatial range and temporal range. The spatial range is the area formed by extending the minimum bounding polygon outward by BX meters (considering the traffic flow diffusion effect), represented by the latitude and longitude coordinates of the polygon's vertices; the temporal range is the time span of the high-incidence period sequence extended forward by BY minutes (warning preparation time) and backward by BZ minutes (lag impact time), represented by the start and end time slices. The final generated data on the impact range of concentrated emissions includes two fields: a list of spatial boundary coordinates and the start and end time slices.

[0052] Step S1110: Integrate the associated links of potential road sections with concentrated emissions, the high-incidence time sequence of concentrated emissions, and the impact range of concentrated emissions to generate a complete spatiotemporal identifier for the risk scenario.

[0053] The data from the above three parts are encapsulated into a risk scenario object, containing four core fields: Scenario ID (an automatically generated unique identifier), Generation Time (the current system time), Potential Road Segment Links (a JSON object generated in step S11710), High-Incidence Period Sequence (a list of time intervals generated in step S118), and Impact Range (spatial and temporal range data generated in step S119). For easier subsequent querying and management, a scenario level field can also be added (divided into Level 1, Level 2, and Level 3 based on peak frequency and impact range), with Level 1 being the highest risk level. For example, the structure of a spatiotemporal identifier for a certain risk scenario is: {"scene_id": "RSCCCDDEEFF", "generate_time": "CCCD-DD-EEFF: GG: HH", "potential_road_link": {...} (JSON data from step S11710), "high_frequency_time_series": [{"start_slice": II, "end_slice": JJ, "duration": KK}, ...], "impact_range": {"spatial_boundary": [[LL,MM], [NN,OO], ...], "time_start_slice": PP, "time_end_slice": QQ}, "scene_level": "Level 1"}.

[0054] Step S120: Generate a multi-segment intersection control action linkage scheme based on the spatiotemporal identifier of the risk scenario. The control action linkage scheme integrates the operation sequence and correlation of intersection signal control parameter adjustment operation, road segment traffic guidance path planning scheme and traffic information push execution operation.

[0055] After generating spatiotemporal identifiers for risk scenarios, corresponding coordinated control action plans need to be formulated. First, the potential road segment linkages resulting from concentrated emissions within the spatiotemporal identifiers are analyzed to distinguish between core and auxiliary control road segments, and control priorities are determined based on their role in traffic flow transmission within these links. Next, high-incidence time period sequences are extracted and divided into different time period sub-intervals, each corresponding to a set of traffic flow change characteristic data. For core control road segments, intersection signal control parameters are adjusted based on traffic flow convergence data and emission generation characteristic data; for auxiliary control road segments, traffic flow guidance paths are planned based on traffic pressure distribution data; and the content and timing of traffic information pushes are determined by combining the time period sub-intervals. Then, the timing connection standards and information push update frequency for each control action are determined, generating a coordinated trigger sequence. Finally, all control action content and relationships are integrated to form a preliminary plan, and control actions for edge road segments are added to generate a complete coordinated control action plan.

[0056] Step S121: Analyze the emission concentration superimposed potential road segment association links in the spatiotemporal identifier of the risk scenario, divide the core control road segments and auxiliary control road segments, and generate control priorities according to the traffic flow transmission role of the road segments in the emission concentration superimposed potential road segment association links. In the ranking results, the core control road segments are placed before the auxiliary control road segments.

[0057] Extract emission concentration overlay and potential road segment association data from the spatiotemporal identifiers of risk scenarios, focusing on reading the core transfer road segment list and the auxiliary transfer road segment list (from the "core_transfer_roads" and "auxiliary_transfer_roads" fields in step S11710). Core transfer road segments are directly classified as core control road segments, and auxiliary transfer road segments are classified as auxiliary control road segments. For core control road segments, they are sorted in descending order based on their betweenness centrality value in the connectivity graph; for auxiliary control road segments, they are sorted in descending order based on the traffic flow transfer scale (values ​​in the dictionary of indirectly associated road segments). The rule for generating the control priority sequence is: first arrange the core control road segments (in descending order of betweenness centrality), then arrange the auxiliary control road segments (in descending order of traffic flow transfer scale). For example, the core control segments AW (mediation centrality BN) and AY (mediation centrality RR) are ordered as AW and AY; the auxiliary control segments BR (transmission scale BS), BG (BK), and BH (BQ) are ordered as BR, BG, and BH; the final control priority sequence is [AW, AY, BR, BG, BH].

[0058] Step S122: Extract the emission concentration overlay high-incidence period sequence from the spatiotemporal identifier of the risk scenario, split it to generate different time period sub-intervals, and each time period sub-interval corresponds to a set of traffic flow change characteristic data.

[0059] The high-incidence time period sequence (time interval list generated in step S118) is obtained from the spatiotemporal identifier of the risk scenario, and each time interval is further subdivided. A dynamic time warping algorithm is used to divide the time period into sub-intervals based on the traffic flow change rate (the absolute value of the traffic flow difference between adjacent time slots divided by the traffic flow of the previous time slot): when the traffic flow change rate of RT consecutive time slots is less than RU, they are merged into one sub-interval; when the traffic flow change rate undergoes a sudden change (greater than RV), it is divided into new sub-intervals at the point of change. Each time period sub-interval contains three basic attributes: start time slot, end time slot, and duration, as well as corresponding traffic flow change characteristic data (five features: average traffic flow, traffic flow standard deviation, maximum traffic flow, minimum traffic flow, and mean traffic flow change rate within the sub-interval). For example, a time interval in a high-incidence time period sequence is the II-JJ time slot, which is split into two sub-intervals: II-RW and RX-JJ. The five corresponding traffic flow change characteristic values ​​are calculated for each sub-interval.

[0060] Step S123: Based on the traffic flow convergence data and emission generation characteristic data of the core control section, adjust the operation content of the intersection signal control parameters and allocate the running time of each phase according to the traffic flow change characteristic data.

[0061] Collect current signal control parameters (cycle duration, green light ratio for each phase, yellow light duration, etc.) of the intersections associated with the core control section, and adjust the parameters by combining the traffic flow convergence data (traffic flow convergence index generated in step S115) and emission generation characteristic data (comprehensive emission intensity in step S114) of the intersection. First, calculate the phase adjustment coefficient: Phase adjustment coefficient = (proportion of turning traffic flow corresponding to this phase × traffic flow convergence index × comprehensive emission intensity) / sum of (proportion of turning traffic flow × traffic flow convergence index × comprehensive emission intensity) of all phases. Then, determine the signal cycle duration based on the average flow and flow standard deviation in the traffic flow change characteristic data of the time period sub-interval: Cycle duration = base cycle duration + (average flow / number of lanes in the road segment) × flow standard deviation × RY, and take the result as an integer multiple of RZ. The running duration of each phase = cycle duration × phase adjustment coefficient - yellow light duration, ensuring that the sum of the running durations of each phase + the sum of the yellow light durations of each phase = cycle duration. For example, if the basic cycle duration of an intersection is SA seconds, the average flow rate of a sub-interval during a certain time period is SB vehicles / hour, the number of lanes is SC, and the standard deviation of the flow rate is SD, then the cycle duration is calculated as SA + (SB / SC) × SD × RY = SE seconds (take SE seconds). If the phase adjustment coefficient for the straight-ahead phase at the east entrance is SF, then the running time is SE × SF - SG = SH seconds. The adjusted cycle duration, running time of each phase, phase sequence, and other parameters are compiled into the intersection signal control parameter adjustment operation content, in the format of a parameter list associated with the intersection ID.

[0062] Step S124: Based on the traffic pressure distribution data of the auxiliary control road sections, generate a road section traffic flow guidance path planning scheme, which includes a path that avoids the concentrated superposition of emission impact range of the core control road sections.

[0063] Obtain traffic pressure distribution data for auxiliary control road sections (pressure index matrix generated in step S113), and filter out time periods and road sections with pressure index > AH (AI). Starting from these high-pressure road sections, use road network topology data (including weighted attributes such as road section travel time, road grade, and number of lanes) to plan alternative guidance paths using an improved A* algorithm. The objective function of path planning is to minimize the total travel time of the path while ensuring that the path does not pass through the concentrated emission superposition impact range of the core control road section (spatial range in step S119). The number of alternative paths is set to ST (main path and SU backup paths), and each path includes information such as road section sequence, estimated travel time, and traffic allocation ratio of each road section. For example, if the pressure index of auxiliary control road segment BR is SV (>AI) at a certain time period, the planned main path is BR→SW→SX→destination, with an estimated travel time of SY minutes and a traffic allocation ratio of SZ%; alternative path 1 is BR→TA→TB→destination, with an estimated travel time of TC minutes and a traffic allocation ratio of TD%; alternative path 2 is BR→TE→TF→destination, with an estimated travel time of TG minutes and a traffic allocation ratio of TH%. The above path information is integrated into a road segment traffic guidance path planning scheme.

[0064] Step S125: Combine the time period sub-intervals of the concentrated emission overlay high-incidence period sequence to generate traffic information push execution operation content. The traffic information push execution operation content includes the push time node and the coverage road segment range. The coverage road segment range includes the connection node between the core control road segment and the auxiliary control road segment.

[0065] Based on the start time slice of the time period sub-interval, the push time nodes are determined: the main push node is TI minutes before the start of the time period sub-interval, and the supplementary push nodes are once every TJ minutes within the sub-interval. The coverage area is determined by the connection nodes between the core control road segment and the auxiliary control road segment. The connection nodes are the shared intersections of directly adjacent road segments in the associated link, as well as the road segments within TK meters upstream and downstream of these intersections. The push content includes the current traffic conditions (congestion level, estimated travel time), recommended routes (the main routes in the road segment traffic guidance path planning scheme), and control measure prompts (such as signal timing adjustment information). Push channels include traffic radio, navigation apps, and roadside information screens. Among them, navigation apps use precise push (only push to vehicle users within the coverage area), and roadside information screens display information according to the location of the road segment. For example, if the starting time slice of a certain sub-interval is II (corresponding to the actual time TL: TM), then the main push node is the time slice TN (TL: TM-TI minutes), and the supplementary push nodes are the time slices TO (TL: TM+TJ minutes) and TP (TL: TM+2×TJ minutes); the covered road segment range is the connecting intersection TQ of the core control road segment AW and the auxiliary control road segment BG, and the AW and BG road segments upstream and downstream TK meters.

[0066] Step S126: Adjust the execution order of the operation content according to the intersection signal control parameters, generate the timing connection standard, and determine the start time parameter of the traffic flow guidance path planning based on the timing connection standard to achieve the timing connection between traffic flow guidance action and signal control adjustment action.

[0067] Extract the phase adjustment sequence from the intersection signal control parameter adjustment operation content, determine the execution steps of each intersection signal control adjustment (e.g., adjust the cycle duration first, then adjust the green ratio of each phase) and the estimated completion time (each step is calculated in TR minutes). The traffic flow change response cycle is determined according to the road segment type: TS minutes for main roads, TT minutes for secondary roads, and TU minutes for local roads. That is, after the signal parameters are adjusted, the traffic flow status will stabilize after the corresponding time. Traffic flow stabilization time point = estimated completion time of signal control adjustment + traffic flow change response cycle. The operation steps of the road segment traffic flow guidance path planning scheme include path calculation (TV minutes), path release (TW minutes), and guidance implementation (TX minutes), with a total execution time of TY minutes. The timing connection standard is set as follows: the start time of the traffic flow guidance operation steps should be TY minutes before the traffic flow stabilization time point (i.e., start synchronously with the signal control adjustment) to ensure that the guidance measures are effective when the traffic flow stabilizes. The road segment traffic flow guidance path planning start time parameter = signal control adjustment start time, with a time interval of TZ minutes (synchronous start). The execution sequence of signal control adjustment steps and traffic guidance operation steps is integrated along a timeline, marking the start time, execution duration, and completion time of each action. For example, signal control adjustment UA:UB starts (estimated completion UA:UB+TR×number of steps), traffic flow change response cycle TS minutes (stable time point UA:UB+TR×number of steps+TS); traffic guidance UA:UB starts synchronously (path calculation UA:UB-UA:UB+TV, path release UA:UB+TV-UA:UB+TV+TW, guidance implementation UA:UB+TV+TW-UA:UB+TV+TW+TX), ensuring that all guidance measures for UA:UB+TV+TW+TX are in place and match the stable time point.

[0068] Step S1261: Extract the intersection signal control parameter adjustment operation content from the control action linkage scheme, and determine the execution steps of the signal control adjustment and the estimated completion time of each step.

[0069] The specific execution steps for adjusting intersection signal control parameters are analyzed from the operation content. These typically include three standard steps: parameter calculation and verification (Step 1), signal controller parameter configuration (Step 2), and on-site effect testing (Step 3). Step 1 (Parameter Calculation and Verification): Calculate the adjusted signal parameters based on traffic flow data and verify the parameter validity using traffic simulation software. The estimated completion time is UU minutes. Step 2 (Signal Controller Parameter Configuration): Update the parameters via a remote control platform or on-site connection to the signal controller. The estimated completion time is UV minutes. Step 3 (On-site Effect Testing): Observe for 1-2 signal cycles after the parameter update to confirm that the parameters are effective and without abnormalities. The estimated completion time is UW minutes. The estimated completion time for each step is accumulated from the start time of the step. For example, Step 1 starts at UA:UB and is expected to complete at UA:UB+UU; Step 2 starts at UA:UB+UU and is expected to complete at UA:UB+UU+UV; Step 3 starts at UA:UB+UU+UV and is expected to complete at UA:UB+UU+UV+UW.

[0070] Step S1262: Determine the traffic flow change response period corresponding to each signal control adjustment step, wherein the traffic flow change response period is the time span from the completion of signal parameter adjustment to the stabilization of traffic flow.

[0071] The traffic flow response cycle is determined based on the road grade and traffic flow characteristics of each road segment: For core controlled road segments (main roads), the traffic flow response cycle = segment length (km) × UX + UY minutes, where UX minutes / km is the time it takes for vehicles to leave their original state, and UY minutes is the stabilization time of the new state. For auxiliary controlled road segments (secondary roads / local roads), the traffic flow response cycle = segment length (km) × UZ + VA minutes, where UZ minutes / km is the time it takes for vehicles to leave their original state, and VA minutes is the stabilization time of the new state. For example, if the length of the core controlled road segment AW is VB km, then the traffic flow response cycle = VB × UX + UY = VC minutes; if the length of the auxiliary controlled road segment BG is VD km, then the response cycle = VD × UZ + VA = VE minutes. After each signal control adjustment step is completed, the corresponding response cycle must be waited for before the traffic flow is considered stable.

[0072] Step S1263: Calculate the traffic flow stabilization time point corresponding to each signal control adjustment step based on the estimated completion time of the signal control adjustment and the traffic flow change response cycle.

[0073] Traffic flow stabilization time = estimated completion time of signal control adjustment steps + traffic flow change response cycle corresponding to that step. For example, if step 3 (field effect test) is expected to complete UA: UB+UU+UV+UW, corresponding to the response cycle VC minutes of the core control section AW, then the traffic flow stabilization time = UA: UB+UU+UV+UW+VC. If there are multiple consecutive steps, each step calculates an independent traffic flow stabilization time, but in actual application, the traffic flow stabilization time of the last step is used as the overall stabilization time of the signal control adjustment.

[0074] Step S1264: Obtain the operation steps of the traffic flow guidance path planning scheme for the road segment, and record the execution time required for each operation step.

[0075] The operational steps of the traffic flow guidance route planning scheme include route database update (Step A), guidance strategy generation (Step B), and guidance instruction issuance (Step C). Step A (Route Database Update): Synchronize the latest traffic data to the route planning database, estimated time VF minutes. Step B (Guidance Strategy Generation): Calculate the optimal guidance route and traffic allocation ratio based on the updated database, estimated time VG minutes. Step C (Guidance Instruction Issuance): Send the guidance strategy to roadside guidance equipment and the navigation platform, estimated time VH minutes. The execution time required for each step is determined in advance based on historical execution data statistics, with an allowable error range of ±VI minutes.

[0076] Step S1265: Based on the stable traffic flow time point, generate a timing connection standard, which includes the time matching rules for signal control adjustment steps and traffic flow guidance operation steps.

[0077] The core rule of the timing alignment standard is that the completion time of traffic flow guidance operation steps should not be later than the traffic flow stabilization time point of signal control adjustment. The specific matching rule is: the completion time of the last step of traffic flow guidance (step C) ≤ the traffic flow stabilization time point of signal control adjustment - VJ minutes (with VJ minutes reserved for system delay). For example, if the traffic flow stabilization time point of signal control adjustment is VK:VL, then the completion time of step C should ≤ VK:VL-VJ minutes. Based on the estimated duration of step C, VH minutes, the start time of step C can be deduced to ≤ VK:VL-VJ-VH minutes; step B must start before step C, and the start time of step B ≤ VK:VL-VJ-VH-VG minutes; the start time of step A ≤ VK:VL-VJ-VH-VG-VF minutes.

[0078] Step S1266: Based on the time sequence connection standard, determine the start time of each operation step of the road segment traffic guidance path planning scheme.

[0079] Based on the reverse calculation result of step S1265 and combined with the start time of the signal control adjustment step, the start time of each step in the traffic guidance is determined. Assuming that signal control adjustment step 1 starts from UA:UB, the start time of traffic guidance step A is set to VM:VN (satisfying ≤VK:VL-VJ-VH-VG-VF minutes), the start time of step B is VM:VN+VF (starting immediately after step A), and the start time of step C is VM:VN+VF+VG (starting immediately after step B). The start time of each step is accurate to the minute and time-bound through the system task scheduling module.

[0080] Step S1267: Set the time interval between the start time parameter of the traffic flow guidance path planning and the completion time of the signal control adjustment step.

[0081] The overall start time for traffic flow guidance path planning is the start time of step A (VM:VN), and the completion time of the signal control adjustment step is the estimated completion time of step 3 (UA:UB+UU+UV+UW). The time interval between the two is (UA:UB+UU+UV+UW) - VM:VN. This time interval is stored as a standard parameter in the control action linkage scheme for time calibration of subsequent time-segmented execution processes.

[0082] Step S1268: Record the sequence of each operation step in the road segment traffic guidance path planning scheme, and arrange the operation execution sequence in order of start time.

[0083] The operation execution sequence is arranged in ascending order of startup time: Step A (VM: VN starts, VM: VN+VF completes) → Step B (VM: VN+VF starts, VM: VN+VF+VG completes) → Step C (VM: VN+VF+VG starts, VM: VN+VF+VG+VH completes). The dependencies between each step are also marked in the sequence (e.g., step B depends on step A to complete, step C depends on step B to complete) to ensure the correct execution order.

[0084] Step S1269: Integrate the execution sequence of the signal control adjustment steps with the operation execution sequence of the road segment traffic guidance path planning scheme, and mark the start time and execution duration of each action.

[0085] The integrated action sequence is as follows: Signal control step 1 (UA:UB - UA:UB+UU) → Signal control step 2 (UA:UB+UU - UA:UB+UU+UV) → Signal control step 3 (UA:UB+UU+UV - UA:UB+UU+UV+UW) → Flow guidance step A (VM:VN - VM:VN+VF) → Flow guidance step B (VM:VN+VF - VM:VN+VF+VG) → Flow guidance step C (VM:VN+VF+VG - VM:VN+VF+VG+VH) → Vehicle flow stabilization time point (VK:VL). The overlapping relationships and sequence of each action are clearly shown through a time - axis chart. Among them, Flow guidance step A starts at VM:VN during the execution of Signal control step 3 to achieve parallel operation and shorten the overall preparation time.

[0086] Step S12610: Load the integrated action sequence and the timing connection standard into the control action linkage plan as the time basis for the execution of action linkage.

[0087] Convert the integrated action sequence into an executable time - instruction format, including fields such as action ID, action name, start time, estimated completion time, and dependent action ID; the timing connection standard is stored in the form of a rule document, including time - matching rules, time - interval parameters, error - tolerance ranges, etc. The above data serves as the time - control module of the control action linkage plan.

[0088] Step S127: Generate a parameter for updating the frequency of pushing traffic information based on the path update situation of the road - section flow - guidance path - planning plan, and update the pushed information based on this frequency parameter.

[0089] The path update situation of the road - section flow - guidance path - planning plan is determined by calculating the path similarity: When the road - section overlap rate between the newly planned path and the currently pushed path <VO%, it is determined that the path has been significantly updated. The parameter for updating the frequency of pushing traffic information is dynamically adjusted according to the path update frequency and the vehicle - flow change characteristic data: When the path is significantly updated, the update - frequency parameter is set to high (once every VP minutes); when there is no significant path update for consecutive VQ minutes and the vehicle - flow change rate <VR, the update - frequency parameter is set to low (once every VS minutes); in other cases, it is set to medium (once every VT minutes). For example, if the path is updated frequently within a sub - interval of a certain time period (VV significant updates occur within VU hours), the push - update frequency parameter for this time period is set to high, that is, the pushed information is updated once every VP minutes.

[0090] Step S128: Generate a linkage trigger sequence for the adjustment operation of intersection signal - control parameters, the road - section flow - guidance path - planning plan, and the execution operation of pushing traffic information according to the vehicle - flow change characteristic data of time - period sub - intervals.

[0091] Based on the start time of each time period sub-interval, trigger thresholds are set according to the traffic flow change rate in the traffic flow change characteristic data: when the traffic flow change rate > VW, fine-tuning of intersection signal control parameters is triggered; when the traffic flow change rate > VX, road segment traffic guidance path update is triggered simultaneously; when the traffic flow change rate > VY, emergency passage information push is triggered. The linkage trigger sequence is represented by an event-action model. Each trigger event includes a trigger condition (traffic flow change rate threshold), a trigger time (time slice), and associated actions (signal adjustment / path update / information push). For example, if the traffic flow change characteristic data of a certain time period sub-interval shows that the traffic flow change rate reaches WC (>VX) in time slice VZ (WA:WB), then the trigger event is "time slice VZ, traffic flow change rate WC>VX", and the associated actions include "fine-tuning of intersection signal control parameters" and "road segment traffic guidance path update". These two actions are added to the trigger sequence in the order of signal adjustment priority (executed first) and path update lag by WD minutes.

[0092] Step S129: Integrate the operation content of intersection signal control parameter adjustment, road segment traffic guidance path planning scheme, traffic information push execution operation content, timing connection standard, traffic information push update frequency parameter and linkage trigger sequence to generate a preliminary control action linkage scheme.

[0093] The data from all the above components are integrated into a structured plan document, comprising eight main modules: Plan ID, associated risk scenario ID, applicable time period sub-intervals, list of regulated road sections, various operational contents (signal control, traffic guidance, information push), timing connection standards, update frequency parameters, and linkage trigger sequences. Each module uses a standardized data format; for example, the list of regulated road sections includes road section ID, control type (core / auxiliary), and priority sorting; the linkage trigger sequence is an event list, with each event including trigger conditions and a list of associated actions. After the initial plan is generated, its feasibility is ensured through an internal review process (checking for consistency of operational contents, time conflicts, etc.).

[0094] Step S1210: Based on the concentrated impact range of emissions in the spatiotemporal identifier of the risk scenario, add the control action content of the edge road section and generate a complete control action linkage plan.

[0095] The spatial boundary of the concentrated emission impact area is extended outward by WE kilometers. Edge road segments within this extended area (road segments not included in the core or auxiliary control segments but potentially affected) are identified. Traffic pressure distribution data and traffic flow status data are collected for these edge road segments. If the traffic pressure index of a certain edge road segment is greater than WF (second highest pressure), basic control actions are added: intersection signal timing optimization (fine-tuning the green light ratio, not exceeding WG%) and low-frequency information push (once every WH minutes). The control actions for these edge road segments are added to the control segment list and operation content of the preliminary control action linkage scheme, clarifying their priority as lower than auxiliary control segments, ultimately forming a complete control action linkage scheme.

[0096] Step S130: Generate a time-segmented execution flow according to the control action linkage scheme. The time-segmented execution flow includes a list of road segments and intersections participating in control in each time period, specific control action content, and action linkage trigger sequence.

[0097] After obtaining the coordinated control action plan, it needs to be transformed into a time-segmented execution flow. First, based on the time-segment sub-interval divisions in the coordinated control action plan, the start and end times of each sub-interval are determined to ensure the duration matches the smoothness of traffic flow changes. Then, a list of road segments and intersections participating in control within each time-segment is generated according to control priorities, clearly identifying core and auxiliary control road segments and related intersections. Next, based on the various operational contents in the plan, specific execution details for intersection signal control, road segment traffic guidance, and traffic information push are generated, including phase adjustment sequence, traffic guidance methods, and information push specifications. Simultaneously, the activation sequence and time intervals of each action are determined according to the timing connection standards, information update nodes are set according to update frequency parameters, and trigger conditions are defined according to the coordinated trigger sequence. Finally, the control information from all time-segment sub-intervals is integrated to form a complete time-segmented execution flow.

[0098] Step S131: Extract the time period sub-interval division results from the control action linkage scheme, determine the start and end times of each time period sub-interval, and ensure that the duration of each time period sub-interval is adapted to the stability of traffic flow changes.

[0099] Read the list of time period sub - intervals from the control action linkage plan (generated in step S122). Each sub - interval contains a start time slice and an end time slice. Convert the time slice to the actual time: time slice number × time slice interval = number of minutes from 0:00 on the current day. For example, time slice II corresponds to II×time slice interval = WI minutes = WA:WB. The start time = the actual time converted from the start time slice × time slice interval, the end time = the actual time converted from the end time slice × time slice interval, and the duration = end time - start time. The smoothness of traffic flow changes is measured by the standard deviation of traffic flow in this sub - interval. When the standard deviation of traffic flow < WJ, it is "smooth", and the duration can be set to WK - WL minutes; when WJ ≤ standard deviation of traffic flow < WM, it is "moderate fluctuation", and the duration is set to WN - WO minutes; when the standard deviation of traffic flow ≥ WM, it is "severe fluctuation", and the duration is set to WP - WQ minutes. For example, if the standard deviation of traffic flow in a certain sub - interval is WR (moderate fluctuation), then the duration is set to WS minutes, the start time is WA:WB, and the end time is WA:WB + WS minutes.

[0100] Step S132: According to the control priorities in the control action linkage plan, generate a list of road sections and intersections involved in control within each time period sub - interval. The list of road sections and intersections includes core control road sections, auxiliary control road sections, and associated intersections.

[0101] Obtain the control priority sequence from the control action linkage plan (generated in step S121), and screen out the road sections that need to perform control actions within the current time period sub - interval (determined based on road section traffic pressure distribution data and traffic flow change characteristic data). Associated intersections are the intersections at both ends of the core / auxiliary control road sections and their adjacent intersections (extending at most one intersection level outward). The format of the list of road sections and intersections is: [{"road section ID": "AW", "type": "core control road section", "associated intersections": ["TQ", "WT"]}, {"road section ID": "AY", "type": "core control road section", "associated intersections": ["WU", "WV"]},...], where each road section entry contains the road section ID, type (core / auxiliary), and a list of associated intersections (intersection IDs).

[0102] Step S133: Adjust the operation content according to the intersection signal control parameters in the control action linkage plan, and generate the phase adjustment sequence of intersection signals, the operation duration of each phase, and the signal cycle adjustment amplitude within each time period sub - interval.

[0103] The signal parameters corresponding to the current time period sub-interval are extracted from the intersection signal control parameter adjustment operation content. The phase adjustment order is sorted according to the importance of the intersection approach lanes (main road approach lanes take priority over secondary road approach lanes). For example, the phase adjustment order of a certain intersection is: East approach straight → North approach straight → East approach left turn → North approach left turn. The running duration of each phase directly adopts the adjusted parameter value (calculated in step S123). The signal cycle adjustment amplitude = (current time period cycle duration - basic cycle duration) / basic cycle duration × 100%, and the result is rounded to one decimal place. For example, if the basic cycle duration is SA seconds and the current time period cycle duration is SE seconds, then the adjustment amplitude = (SE - SA) / SA × 100% ≈ WX%. The above data is grouped by intersection ID to form the signal control adjustment details for each time period sub-interval.

[0104] Step S134: Based on the road segment traffic flow guidance path planning scheme in the control action linkage scheme, generate the traffic flow guidance method, diversion path direction and traffic flow allocation ratio of the road segment entrance in each time period sub-interval.

[0105] The alternative route information for the current time period sub-interval is extracted from the traffic flow guidance route planning scheme. The traffic flow guidance method is determined according to the road segment entrance type: the main road entrance adopts the method of "dynamic adjustment of lane signs + variable message sign display"; the auxiliary road entrance adopts the method of "roadside guides + temporary traffic cones". The diversion route direction is represented by text description combined with road segment sequence, such as "travel from BR entrance along SW→SX direction, avoiding the AW core control road segment". The traffic flow allocation ratio is allocated according to the default ratio of main path SZ%, backup path 1TD%, and backup path 2TH%. If the estimated travel time of a backup path is more than WY% less than that of the main path, the allocation ratio of the backup path is increased by WZ% (deducted from the main path). For example, if the main path is estimated to be SY minutes and backup path 1 is estimated to be TC minutes (less than SY×(1-WY%)), then the allocation is adjusted to backup path 1 TD%+WZ%, and the main path is allocated SZ%-WZ%.

[0106] Step S1341: Extract the traffic flow guidance path planning scheme from the control action linkage scheme, and extract and record the positional relationship between the target diversion road segment and the core control road segment in each time period sub-interval.

[0107] The target diversion road segment list (road segments requiring traffic guidance in auxiliary control road segments) for each time period sub-interval is retrieved from the road segment traffic guidance path planning scheme. The spatial relationship between each target diversion road segment and the core control road segment is calculated using the GIS system, including distance (the straight-line distance from the starting point of the target diversion road segment to the nearest point on the core control road segment) and orientation (e.g., east, south, west, north). The location relationship data is recorded as a quadruple of "Target Diversion Road Segment ID - Core Control Road Segment ID - Distance - Orientation," for example, "BR - AW - WA meters - East" indicates that the target diversion road segment BR is located WA meters east of the core control road segment AW.

[0108] Step S1342: Collect basic road data of the target diversion section and analyze the number of lanes and traffic capacity data of the target diversion section.

[0109] Obtain the lane number data of the target diversion section from the road infrastructure database (step S112), and calculate the capacity: Capacity = Number of lanes × Baseline lane capacity × Lane type coefficient × Time period coefficient, where the time period coefficient is determined based on the traffic characteristics of the time period sub-interval (XA for morning peak, XB for off-peak, and XC for evening peak). For example, if the target diversion section BR is a secondary arterial road with XC lanes, a base lane capacity of XD vehicles / hour, a lane type coefficient of XE, and the time period sub-interval is morning peak (time period coefficient XA), then the capacity = XF × XD × XE × XA.

[0110] Step S1343: Combine the traffic flow prediction data within the time period sub-interval to calculate the expected flow volume of the core control section and the capacity flow volume of the target diversion section.

[0111] The projected traffic volume of the core control section is the average traffic volume of that section in each time slot within the time period sub-interval; the capacity of the target diversion section = capacity × (1 - current traffic pressure index), where the current traffic pressure index is the average pressure index of that section in the time period sub-interval (step S113). For example, if the projected traffic volume of the core control section AW is XG vehicles / hour, the capacity of the target diversion section BR is XH vehicles / hour, and the current traffic pressure index is XI, then the capacity of the target diversion section BR = XH × (1 - XI).

[0112] Step S1344: Based on the correspondence between the expected traffic volume and the capacity to handle traffic volume, determine the traffic guidance method at the entrance of the road segment. The traffic guidance method includes lane indication adjustment and entrance guidance sign setting.

[0113] When the capacity to handle traffic is greater than or equal to the estimated traffic volume × XJ (capable of handling at least XJ of diverted traffic), a combined guidance method of "lane indication adjustment + entrance guidance signage setting" is used; when XK ≤ capacity to handle traffic volume < estimated traffic volume × XJ, only the "entrance guidance signage setting" method is used; when the capacity to handle traffic volume < estimated traffic volume × XK, an "information prompt + passive guidance" method is used (only prompts are sent via the navigation app, without physical guidance signs). For example, if the capacity to handle traffic volume XL of BR is greater than or equal to the estimated traffic volume XG × XJ of AW, then a combined guidance method is used.

[0114] Step S1345: Determine the diversion path direction based on the positional relationship between the core control section and the target diversion section. The diversion path direction avoids the core area within the concentrated emission superposition influence range.

[0115] Using the path analysis function of the GIS system, starting from the entrance of the target diversion section, and constrained by the area of ​​concentrated emission superposition influence of the core control section (the spatial core area of ​​step S119, i.e., the smallest circumscribed polygon), XM alternative diversion paths are planned. The path description adopts the format of "direction + major intersection + road name," for example, "traveling from east to west, turning right at intersection TQ onto SW road, continuing straight to intersection XN and turning left onto SX road." Each path must be confirmed through spatial overlay analysis to avoid passing through the core area.

[0116] Step S1346: Based on the capacity of the target diversion road segment and the expected capacity of the core control road segment, determine the traffic flow allocation ratio from the core control road segment to each target diversion road segment.

[0117] First, calculate the total carrying capacity = the sum of the carrying capacity of all target diversion sections. Then, the allocation ratio of a target diversion section = the carrying capacity of that section / the total carrying capacity × the diversion ratio coefficient, where the diversion ratio coefficient is the total proportion that needs to be diverted from the core control section (determined based on the traffic pressure index; XP is used when the pressure index > XO, and XR is used when XQ < pressure index ≤ XO). For example, if the diversion ratio coefficient of the core control section AW is XS, the total carrying capacity = XL (BR) + XT (BG) = XU, the allocation ratio of BR = XL / XU × XS, and the allocation ratio of BG = XT / XU × XS.

[0118] Step S1347: Designate the person responsible for traffic guidance operations at the entrance of each road segment.

[0119] Based on the jurisdiction and management authority of the road section, operational personnel are assigned from the traffic management department's personnel dispatch system. XV primary responsible persons and XW auxiliary responsible persons are designated for each road section entrance. The responsible person's information includes their name, contact number, and affiliated unit. For example, the primary responsible person for the entrance of the BR road section is XX (contact number XYXXXXXXX, XZ Traffic Police Brigade), and the auxiliary responsible person is YA (contact number YBXXXXXXX, YC Traffic Assistant Team).

[0120] Step S1348: Set the execution time of the traffic guidance method, wherein the execution time is consistent with the start time of the time period sub-interval.

[0121] The execution time for traffic redirection is calculated as the start time of the sub-interval minus the preparation time. The preparation time is determined based on the complexity of the redirection method: YD minutes for combined redirection, YE minutes for single redirection markers, and YF minutes for information prompts. For example, if the start time of the sub-interval is WA:WB, and a combined redirection method is used, then the execution time is WA:WB - YD minutes, ensuring that all redirection measures are deployed before the start of the WA:WB time period.

[0122] Step S1349: Determine the location of the sign for the diversion path, wherein the sign location is located in the road section area before the diversion node.

[0123] Diversion nodes are intersections where a turn is required along the route. Signage should be placed on the roadside or median strip, YG-YH meters before the diversion node, ensuring visibility (drivers can see it from YI meters away). For example, if the diversion route involves a right turn at intersection TQ, the sign should be placed on the north side of the BR section, YJ meters east of intersection TQ. Record the latitude and longitude coordinates and installation method (e.g., temporary bracket, attached to an existing pole) for each sign location.

[0124] Step S13410: Integrate the traffic guidance methods, diversion routes, traffic allocation ratios, responsible personnel, execution times, and signage locations within each time period sub-interval to generate specific details of the road segment traffic guidance operation.

[0125] The above data are integrated into a structured document, with each time period sub-interval corresponding to a guidance operation detail, including fields such as target diversion section ID, traffic guidance method description, XM diversion path direction text, traffic flow allocation ratio of each path, list of operation responsible persons, execution time of guidance measures, and list of coordinates of sign setting locations. For example, the guidance operation details for the BR segment in the sub-interval of WA:WB-WA:WB+WS are as follows: {"target_road_id":"BR","guidance_method":"lane indication adjustment + entrance guidance sign setting","diversion_paths":["traveling from east to west, turn right at TQ intersection to enter SW road...",...],"traffic_distribution":{"path1":BR allocation ratio,"path2":BM allocation ratio,"path3":BN allocation ratio},"responsible_persons":[{"name":"XX","phone":"XYXXXXXXX",...},...],"execution_time":"WA:WB-YD minutes","sign_positions":[{"longitude":YK,"latitude":YL,"installation_method":"temporary support"},...]}.

[0126] Step S135: Combine the traffic information push execution operation content in the control action linkage scheme to generate the content composition of traffic information in each time period sub-interval, push channel selection and push frequency setting.

[0127] The traffic information consists of three parts: current traffic conditions (congestion level, estimated travel time), recommended routes (main route and estimated time), and control measures (signal timing adjustments, traffic guidance, etc.). The selection of push channels is determined based on the coverage area and target user group: roadside information screens and navigation apps are prioritized for core control sections and related intersections; navigation apps are the primary means of control for auxiliary control sections, supplemented by traffic radio. The push frequency directly adopts the traffic information push update frequency parameter (generated in step S127), with high frequency corresponding to once every VP minutes, medium frequency once every VT minutes, and low frequency once every VS minutes. For example, if the update frequency parameter for a certain time period sub-interval is medium, the push frequency is set to once every VT minutes, pushed at the start time, after VT minutes, and after 2×VT minutes.

[0128] Step S136: According to the timing connection standard in the control action linkage scheme, generate the start sequence and time interval of the intersection signal control parameter adjustment operation, the road segment traffic flow guidance path planning scheme and the traffic information push execution operation in each time period sub-interval.

[0129] The start time relationships of each operation are extracted from the timing sequence standard (the action sequence integrated in step S1269) and converted into offsets relative to the start time of the sub-interval. For example, if the start time of the sub-interval is WA:WB, the start time of signal control adjustment is WA:WB-YM minutes (offset -YM minutes), the start time of traffic guidance path planning is WA:WB-YM minutes (offset -YM minutes), the start time of main traffic information push is WA:WB-YM minutes (offset -YM minutes), and the supplementary push time is WA:WB+YN minutes (offset +YN minutes), WA:WB+2×YN minutes (offset +2×YN minutes), etc. The time interval is calculated by the difference in the start time offsets of adjacent actions. For example, the time interval between signal control adjustment and traffic guidance path planning is YO minutes (synchronous start), and the time interval between main push and supplementary push is YN minutes.

[0130] Step S137: Push update frequency parameters based on traffic information in the control action linkage scheme to generate update time nodes for traffic information in each time period sub-interval.

[0131] The update time node for traffic information = start time of the sub-interval + (YP-1) × update interval, where YP is the number of updates, and the update interval is determined according to the update frequency parameter (high frequency VP minutes, medium frequency VT minutes, low frequency VS minutes). The number of updates = sub-interval duration / update interval, rounded up. For example, if the sub-interval duration is WS minutes and the update frequency parameter is medium (VT minutes interval), then the number of updates = WS / VT ≈ YQ times, and the update time nodes are WA:WB (start time), WA:WB+VT minutes, and WA:WB+2×VT minutes.

[0132] Step S138: Generate the triggering conditions for each control action in each time period sub-interval according to the linkage triggering sequence in the control action linkage scheme.

[0133] The triggering conditions in the linkage trigger sequence are converted into specific monitoring indicator thresholds. For example, if "flow change rate > VX" triggers a path update, the monitoring object (the eastbound flow of the core control section AW), monitoring frequency (once every YR minutes), threshold (VX), and duration (for YS consecutive monitoring cycles) must be clearly defined. The description format of the triggering condition is "When the [monitoring object] meets the [threshold condition] for [consecutive durations] at the [monitoring frequency], the [related action] is triggered." For example, "When the flow change rate of the eastbound flow of the core control section AW meets >VX for YS consecutive cycles at a monitoring frequency of YR minutes, the flow guidance path update action for the section is triggered."

[0134] Step S139: Record the list of road segments and intersections participating in regulation within each time period sub-interval, the specific content of intersection signal control adjustment, the specific details of road segment traffic guidance operation, the specific specifications for traffic information push, the action start sequence, information update nodes and triggering conditions.

[0135] An execution record table is created for each time period sub-interval, recording information in columns according to the above items. For example, the road segment and intersection list section records the specific IDs and types of core control road segments, auxiliary control road segments, and related intersections; the signal control adjustment section details the cycle duration, phase sequence, and phase duration for each intersection; the traffic guidance operation section includes information such as guidance method, diversion path, allocation ratio, and responsible person; the information push specification clearly defines the content composition, push channels, and frequency; the action activation sequence indicates the activation time and time interval of each operation; the information update node lists the specific update time points; and the trigger conditions detail the trigger indicators and thresholds for each control action.

[0136] Step S1310: Integrate the control-related information of all time period sub-intervals to generate a complete time-segmented execution process.

[0137] The execution records for all time-segment sub-intervals are arranged chronologically to form a time-segmented execution process document. The document includes a process overview (a list of time-segment sub-intervals and main control objectives) and detailed execution content for each sub-interval. The overview concisely lists the start and end times, core control sections, and main control measures for each time-segment sub-interval; the detailed execution content unfolds sequentially according to the list of road segments and intersections, signal control adjustments, traffic guidance operations, information push specifications, action initiation sequence, information update nodes, and triggering conditions, ensuring that each time-segment control action has a clear execution basis and operational guidelines. Through this method, the abstract linkage plan is transformed into a concrete and executable time-segmented operation process.

[0138] Step S140: Start the multi-entity synchronous execution procedure according to the time-segmented execution process, and collect the actual execution data of each control action and the real-time operation feedback data of road segments and intersections.

[0139] After the time-segmented execution process is generated, a multi-entity synchronous execution procedure needs to be initiated to ensure that all execution entities coordinate and implement control actions consistently. First, based on the start time in the time-segmented execution process, control initiation instructions are sent to the signal control execution entity, traffic flow guidance execution entity, and information push execution entity, clarifying the control action requirements for each time period. Upon receiving the instructions, each execution entity executes the corresponding control actions according to the specific operational content in the process and records the actual execution status. Simultaneously, monitoring equipment deployed at road sections and intersections collects real-time traffic operation data, including information on flow rate, speed, and lane occupancy. The execution data and real-time operation data are then correlated and integrated over time to form a complete feedback dataset.

[0140] Step S141: According to the start time in the time-segmented execution process, send the control start command to the signal control execution entity, the flow guidance execution entity, and the information push execution entity. The control start command includes the control action requirements for the corresponding time period.

[0141] YT minutes before the start time of each sub-interval in the time-segmented execution process, the system sends a control initiation command to each executing entity via a dedicated communication protocol (such as the YU protocol). The command content adopts a structured data format, including fields such as time period identifier, list of controlled road segments / intersections, specific action parameters, execution time limit, and feedback requirements. For example, the command sent to the signal control executing entity may include: "Time period identifier: WA:WB-WA:WB+WS minutes; Controlled intersections: TQ, WT; Action parameters: TQ intersection cycle SE seconds, phase sequence East Straight-North Straight-East Left-North Left, duration of each phase SH, YV, YW, YX seconds; Execution time limit: Parameter update to be completed before WA:WB; Feedback requirements: Execution result (success / failure), completion time, and abnormal information." After receiving the command, each executing entity returns a confirmation receipt to ensure that the command is transmitted correctly.

[0142] Step S142: After receiving the control start command, the signal control execution entity performs the specific content of the intersection signal control adjustment in the time-segmented execution process, adjusts the signal control parameters of the corresponding intersection, and records the numerical changes of the parameters before and after the adjustment and the adjustment completion time.

[0143] The signal control execution entity (such as the traffic signal control center system) parses the control start command and extracts the target intersection ID and corresponding signal control parameters. Through a remote control interface or field controller, it updates the current signal parameters (cycle duration, phase green ratio, yellow light duration, etc.) to the target parameters in the command. During parameter adjustment, the system automatically records the old parameter values ​​before adjustment, the new parameter values ​​after adjustment, the start and end times of the adjustment operation, and the operator ID. If an anomaly occurs during adjustment (such as communication failure or signal controller unresponsiveness), the anomaly code and fault description are recorded. For example, when adjusting the signal parameters at intersection TQ, the following is recorded: old cycle duration SA seconds → new cycle duration SE seconds, old east-straight phase duration YY seconds → new east-straight phase duration SH seconds, adjustment start time WA: WB-YZ minutes, completion time WA: WB-ZA minutes, operator ID ZB.

[0144] Step S143: The traffic guidance execution entity implements traffic guidance measures at the entrance of the corresponding road segment according to the control start command and the specific details of the road segment traffic guidance operation in the time-segment execution process, and records the execution method, start time and coverage of the guidance measures.

[0145] Traffic flow guidance implementation entities (such as traffic enforcement personnel and roadside equipment management systems) deploy traffic flow guidance measures based on the road segment entry list and guidance operation details in the instructions. If the "dynamic adjustment of lane signs + variable message sign display" method is adopted, the implementation entity needs to install or adjust the content of the signs and variable message signs at the designated location (the sign setting location determined in step S1349), and record the device ID, display content, and start display time; if roadside guides are involved, the guide ID, arrival time, and guidance start time are recorded. The coverage area is recorded using GPS positioning to determine the latitude and longitude range of the actual guidance area. For example, when implementing guidance at the entrance of the BR road segment, the following is recorded: guidance method "lane signs + variable message signs", device ID ZC (sign), ZD (variable message sign), display content "Road segment AW ahead is under control, it is recommended to detour via SW→SX", start time WA: WB-YD minutes, coverage area ZE-ZF (latitude and longitude range).

[0146] Step S144: The information push execution entity sends traffic guidance information to vehicle users through preset push channels in accordance with the control start command and the specific specifications for traffic information push in the time-segmented execution process, and records the information sending time, content details and push channel usage.

[0147] The information push execution entity (such as a traffic information service platform) determines the push content, channels, and time nodes according to the push specifications. Content details include the current congestion level (e.g., "moderate congestion"), estimated travel time, recommended routes, and control measures reminders; push channels are selected according to priority (e.g., navigation apps take precedence over traffic radio); and the sending time strictly adheres to the information update nodes. The system records the unique identifier, sending time, target user group (e.g., vehicles within the covered road segment), content summary, number of successful / failed transmissions for each channel, and response time for each message. For example, a push message record might have the following information ID: ZG, sending time: WA: WB-YM minutes, content: "Signal control will be implemented on the AW road segment from 07:00 to 07:40. It is recommended to detour via BR→SW→SX, with an estimated time saving of ZH minutes," and push channels: navigation apps (ZI successful, ZJ failed), and roadside information screens (ZK screens successfully displayed).

[0148] Step S145: Integrate the parameter adjustment records of the signal control execution subject, the guidance measure execution records of the flow guidance execution subject, and the information sending records of the information push execution subject to generate the actual execution data of each control action.

[0149] The actual execution data is integrated using a unified timestamp to form an execution dataset based on time-segment sub-intervals. Each record in the dataset includes the action type (signal control / traffic guidance / information push), associated road segment / intersection ID, execution start time, execution completion time, key parameters (such as signal cycle before and after adjustment, guidance method, and push content), execution status (success / failure / partial success), and exception information (if any). For example, the actual execution data for a certain sub-interval may include: {"action_type": "signal control", "road_id": "TQ", "start_time": "WA: WB-YZ minutes", "end_time": "WA: WB-ZA minutes", "parameters": {"old_cycle": SA", "new_cycle": SE, ...}, "status": "success", "error": ""}, {"action_type": "traffic guidance", "road_id": "BR", "start_time": "WA: WB-YD minutes", "end_time": "WA: WB-ZL minutes", "parameters": {"method": "combined guidance", "sign_ids": ["ZC", "ZD"], ...}, "status": "success", "error": ""}, ...

[0150] Step S146: During the execution of the control action, real-time traffic flow data, vehicle speed data and lane occupancy data of each participating road segment are collected through the monitoring equipment set up on the road segment.

[0151] Road segment monitoring equipment (such as loop detectors and video analytics devices) collects data at a fixed sampling frequency (once every ZM seconds). Real-time traffic flow data is the number of vehicles passing through each lane per unit time (vehicles / minute); vehicle speed data is the average speed of vehicles passing through the detection area (km / h); lane occupancy data is the percentage of time a lane is occupied by vehicles (%). After data collection, it is preprocessed (noise reduction, outlier removal) using edge computing equipment and then accurately timestamped (accurate to the second). For example, the monitoring data record for road segment AW is as follows: timestamp WA:WB:ZN, lane 1 traffic flow ZO vehicles / minute, speed ZP km / h, occupancy rate ZQ%; lane 2 traffic flow ZR vehicles / minute, speed ZS km / h, occupancy rate ZT%...

[0152] Step S147: Collect real-time turning traffic flow data, signal phase operation data, and vehicle passage time data of each participating intersection through the monitoring equipment deployed at the intersection.

[0153] Intersection monitoring equipment (such as video cameras and microwave radar) collects real-time turning traffic flow data (number of vehicles per minute in each turning direction), signal phase operation data (current phase, remaining duration, phase switching time), and vehicle passage time data (average time for vehicles to pass through the intersection, seconds per vehicle). The data is also pre-processed and timestamped to ensure synchronization with the road segment data. For example, the monitoring data record for intersection TQ is as follows: timestamp WA:WB:ZN, left-turning traffic flow at the east entrance ZU vehicles / minute, current phase "East Straight", remaining duration ZV seconds, average passage time ZW seconds / vehicle...

[0154] Step S148: Classify and integrate real-time traffic flow data, vehicle speed data, lane occupancy data, real-time turning traffic flow data, signal phase operation data, and vehicle passage time data to generate real-time operation feedback data for road segments and intersections.

[0155] The collected data are aligned by road segment / intersection ID and timestamp to form a unified spatiotemporal dataset. First, sub-databases are established for each road segment and intersection, each containing time-series data for the corresponding monitoring indicators. Then, derived indicators are calculated, such as the road segment congestion index (a comprehensive index based on traffic flow, speed, and occupancy rate) and the intersection turning conflict rate (the frequency of conflict between turning traffic and intersecting traffic). After data integration, the data is stored in a compressed format, retaining the original sampled data and minute-level statistical data (average, maximum, and minimum values). For example, the real-time operational feedback data for road segment AW includes minute-level statistical values ​​of traffic flow, speed, and occupancy rate from WA:WB:00 to WA:WB+WS, as well as the calculated congestion index time series; intersection TQ includes minute-level data on each turning traffic flow, phase operation, and passage time, as well as the conflict rate indicator.

[0156] For example, step S1481: Classify and organize the real-time traffic data, divide the data into groups according to the road segment affiliation, and record the traffic value and change data of each road segment at different collection time points.

[0157] Real-time traffic data is grouped by segment ID. Each segment data group contains raw sampled data and statistical data for all lanes. For each collection time point (e.g., every minute), the total traffic flow (sum of traffic flow in all lanes), traffic flow change (difference between the current minute's traffic flow and the previous minute's traffic flow), and traffic flow change rate (change rate divided by the previous minute's traffic flow) are calculated for that segment. Data is indexed by timestamp and stored as a 5-tuple of segment ID-timestamp-total traffic flow-change rate-change rate. For example, the data group for segment AW contains: timestamp WA: WB, total traffic flow ZX vehicles / minute, change rate ZY vehicles / minute, change rate ZZ%; timestamp WA: WB+1 minute, total traffic flow AAB vehicles / minute, change rate AAC vehicles / minute, change rate AAD%...

[0158] Step S1482: Classify the vehicle flow speed data, split the data according to the lane type, and record the driving speed values and change data of different lanes in each section at each collection time point.

[0159] Split the vehicle flow speed data according to the section ID and lane type (such as main lane, auxiliary lane). Each lane data includes the original speed sampling value and statistical values (average speed, speed standard deviation). For each collection time point, calculate the average speed, speed change amount (the difference between the current average speed and the average speed of the previous time point), and speed change rate of the lane. The data storage format is a six-tuple of section ID - lane type - timestamp - average speed - speed change amount - speed change rate. For example, for the data of the main lane 1 of section AW: timestamp WA: WB, average speed AAE km / h, change amount AAF km / h, change rate AAG%...

[0160] Step S1483: Organize the lane occupancy data, and mark the lane usage status and occupancy ratio data at each collection time point according to the corresponding relationship between the section and the lane.

[0161] The lane occupancy data is organized according to the corresponding relationship of section ID - lane number. Each lane records the occupancy rate (%) and usage status (normal / congested / idle, judged based on the occupancy rate threshold: occupancy rate > AAH% is congested, < AAI% is idle, otherwise it is normal) at each collection time point. At the same time, calculate the occupancy rate change trend (the change direction of the occupancy rate at three consecutive time points). The data format is a six-tuple of section ID - lane number - timestamp - occupancy rate - usage status - change trend. For example, for the data of lane 1 of section AW: timestamp WA: WB, occupancy rate AAJ%, usage status "congested", change trend "rising"...

[0162] Step S1484: Classify and count the real-time turning vehicle flow data, divide the data categories according to the intersection turning direction, and record the vehicle flow values and change data of different turns at each intersection at each collection time point.

[0163] The real-time turning vehicle flow data is classified according to intersection ID - turning direction (east left turn, east straight, south left turn, etc.). Count the vehicle flow number (vehicles / minute), vehicle flow change amount (the difference from the previous time point), and vehicle flow ratio (the percentage of the vehicle flow of this turn in the total vehicle flow of the intersection) of each turn at each collection time point. The data format is a six-tuple of intersection ID - turning direction - timestamp - vehicle flow number - change amount - ratio. For example, for the data of the east left turn of intersection TQ: timestamp WA: WB, vehicle flow number AAK vehicles / minute, change amount AAL vehicles / minute, ratio AAM%...

[0164] Step S1485: Record the operation duration, start time, and end time data of each phase of each intersection.

[0165] The actual operation records of each phase at each intersection are extracted from the signal phase operation data, including phase name (e.g., Dongzhi), start time, end time, actual running time (end time - start time), and deviation from the planned duration (actual duration - planned duration). The data is stored according to the dimension of intersection ID-phase name-date, forming a phase operation log. For example, the data for the Dongzhi phase at intersection TQ: start time WA: WB, end time WA: WB+SH seconds, actual running time SH seconds, deviation 0 seconds (consistent with the plan)...

[0166] Step S1486: Organize vehicle passage time data, and record the vehicle passage time value and change data at each collection time point according to the combination relationship between intersections and road segments.

[0167] Vehicle travel time data is recorded in "intersection-segment" combinations (e.g., TQ intersection-AW segment). The average travel time (seconds / vehicle), time change (difference from the previous time point), and travel time level (low / medium / high, based on thresholds) are calculated for each data collection point. The data format is a six-tuple: intersection ID-segment ID-timestamp-average time-change-travel time level. For example, the data for TQ intersection-AW segment: timestamp WA:WB, average time AAN seconds / vehicle, change AAO seconds / vehicle, travel time level "medium"...

[0168] Step S1487: Establish a unified time reference axis and map the real-time traffic flow data, vehicle speed data, lane occupancy data, real-time turning traffic flow data, signal phase operation data and vehicle passage time data at each collection time point to the same time mark.

[0169] Using a standard timestamp (accurate to the second) as a unified baseline, the collection time points of various data types are aligned. For data with different sampling frequencies (e.g., traffic data once per minute, speed data once every 30 seconds), interpolation or resampling methods are used to unify them to minute-level timestamps. For example, speed data collected every 30 seconds is resampled to minute-level data by averaging, ensuring that all data are comparable under the same timestamp. The unified data is organized by timestamp, forming a minute-level data snapshot containing all indicators.

[0170] Step S1488: Based on the spatial relationship between road segments and intersections, combine various types of data within the same area to generate a regional operation data group.

[0171] Based on the spatial topological relationship between road segments and intersections (e.g., a road segment connecting two intersections), related road segment and intersection data are combined into regional operational data sets. For example, the traffic flow, speed, and occupancy data of road segment AW (connecting intersections TQ and WT) are combined with the turning traffic flow, phase operation, and travel time data of intersections TQ and WT to form a regional data set centered on road segment AW. The regional data set includes a spatial description (latitude and longitude boundaries) and time-series data for various indicators, facilitating the analysis of traffic operation conditions at the regional level.

[0172] Step S1489: Standardize the format of various types of data in the regional operation data group, remove abnormal data from the regional operation data group, integrate all processed regional operation data groups, and generate real-time operation feedback data covering all road sections and intersections involved in the control.

[0173] Each indicator in the regional data set is standardized in format (e.g., standardized timestamp format, unit conversion), and outliers (data exceeding the range Q1-1.5×IQR to Q3+1.5×IQR) are removed using the IQR (interquartile range) method. Missing data is imputed using linear interpolation. Finally, all regional data sets are integrated into a single global real-time operational feedback dataset, containing standardized and cleaned data from all road segments and intersections involved in the control measures, supporting queries and analysis by time, space, and indicator type.

[0174] Step S149: Establish a time correspondence between actual execution data and real-time operation feedback data, so that the execution status of control actions at the same time node matches the traffic operation status data.

[0175] By linking actual execution data with real-time operational feedback data using timestamps, an "action-state" correspondence is established. For each control action (such as signal parameter adjustment), real-time operational data after its completion time is found, and the impact of the action on traffic conditions is analyzed. For example, if signal control adjustment is completed within WA:WB-ZA minutes, real-time flow and speed data from WA:WB onwards are correlated to evaluate the adjustment effect. The correlation results are stored as a mapping table of action ID-timestamp-traffic state index.

[0176] Step S1410: Continuously collect actual execution data and real-time operation feedback data until the termination time in the time-segmented execution process is reached, and complete the execution of the control action for that time period.

[0177] The system stops data collection at the termination time (WA: WB + WS minutes) in the time-segmented execution process, and summarizes, verifies, and stores the actual execution data and real-time operational feedback data within that time period. Data storage utilizes a distributed database to ensure data integrity and traceability. Simultaneously, an execution summary report for that time period is generated, including indicators such as action execution rate, data integrity rate, and a list of abnormal events, marking the completion of the control actions for that time period.

[0178] Step S150: Based on actual execution data and real-time operation feedback data, adjust the linkage scheme of control actions and the time-segmented execution process, generate updated control instructions and continuously execute them to form a road emission coordinated control cycle.

[0179] After execution within each time-segment sub-interval, the control effect needs to be evaluated based on actual execution data and real-time operational feedback data, and the control action linkage scheme and time-segment execution process need to be dynamically adjusted. First, the impact of each control action on traffic flow and emissions is analyzed, and problems in the scheme are identified (such as unreasonable signal timing, unbalanced traffic distribution along guidance paths, etc.). Then, based on the analysis results, intersection signal control parameters, traffic guidance paths, and information push strategies are adjusted, and the time nodes and triggering conditions in the time-segment execution process are updated. Finally, updated control instructions are generated and executed in the next time-segment sub-interval, forming a collaborative control loop of "execution-feedback-adjustment-re-execution" to continuously optimize road emission control effectiveness.

[0180] Step S151: Extract the signal control parameter adjustment record from the actual execution data, compare it with the signal phase operation data in the real-time operation feedback data, and obtain the traffic flow change response data after the signal control adjustment.

[0181] The signal control parameter adjustment records (such as the adjustment values ​​of cycle duration and phase duration) are extracted from the actual execution data. The adjusted signal phase operation data (actual phase duration, phase switching time) and traffic flow data (turning traffic flow, travel time) for the corresponding intersection are extracted from the real-time operation feedback data. Traffic flow change indicators before and after the adjustment are calculated: turning traffic flow change rate (number of vehicles after adjustment / number of vehicles before adjustment), travel time change rate (time after adjustment / time before adjustment), and phase saturation (actual traffic flow / phase capacity). These indicators together constitute traffic flow change response data, reflecting the actual impact of signal control adjustments on traffic flow. For example, after the eastbound straight phase duration at intersection TQ is adjusted from YY seconds to SH seconds, the eastbound straight turning traffic flow change rate is AAP, the travel time change rate is AAQ, and the phase saturation is AAR.

[0182] Step S152: Obtain the execution record of traffic guidance measures from the actual execution data, and combine it with the real-time traffic data and vehicle speed data in the real-time operation feedback data to obtain the traffic change data of the road section after traffic guidance.

[0183] Traffic flow guidance measures execution records include guidance methods, start time, and coverage area; real-time traffic and speed data provide changes in traffic flow and speed before and after guidance. Traffic flow change indicators are calculated as follows: guided traffic flow (the actual number of vehicles diverted after guidance), traffic allocation accuracy (the deviation between the actual allocation ratio and the planned allocation ratio), average speed improvement rate (average speed after guidance / average speed before guidance - 1), and travel time saving rate (1 - travel time after guidance / travel time before guidance). These indicators constitute the traffic flow change data for evaluating the effectiveness of traffic flow guidance. For example, after guidance on the BR segment, the actual number of diverted vehicles is AAS, the traffic allocation accuracy rate is AAT%, the average speed improvement rate is AAU%, and the travel time saving rate is AAV.

[0184] Step S153: Obtain the information push record in the actual execution data, compare it with the vehicle passage time data in the real-time operation feedback data, and extract the path selection change data after the passage information is pushed.

[0185] The push notification record includes the push time, content, and number of users covered; vehicle travel time data reflects changes in travel time for different routes. By analyzing changes in the proportion of user route selection before and after the push (e.g., the percentage of vehicles choosing the recommended route), and changes in the difference between the travel time of the recommended route and other routes, route selection change data is extracted. Metrics include: route switching rate (the percentage increase in vehicles choosing the recommended route after the push), recommended route utilization rate (the percentage of vehicles choosing the recommended route out of the total number of vehicles), and route time difference (the difference between the time of the recommended route and the time of the shortest alternative route). For example, after the push, the switching rate of the BR→SW→SX route is AAW%, the utilization rate is AAX%, and the route time difference is AAY seconds.

[0186] Step S154: Based on traffic flow change response data, road segment traffic change data, and route selection change data, analyze the execution effect of each action in the control action linkage scheme and the correspondence between traffic flow changes.

[0187] Correlation analysis was used to establish the correspondence between control action parameters and traffic flow change indicators. For example, the correlation coefficients were established between signal phase duration adjustment and turning traffic flow change rate, traffic flow guidance allocation ratio and segment speed increase rate, and information push frequency and route switching rate. Regression analysis was used to identify key influencing factors and to pinpoint action parameters that significantly affect traffic flow changes (e.g., phase duration adjustment has the highest weight in terms of impact on traffic flow). Simultaneously, the interaction effects between various actions were analyzed (e.g., the synergistic effect of signal adjustment and traffic flow guidance).

[0188] Step S155: Based on the lane occupancy data and vehicle passage time data in the real-time operation feedback data, adjust the operation content of the intersection signal control parameters and the direction of the road segment flow guidance path planning scheme in the linkage scheme of the control action.

[0189] If lane occupancy data shows continuous congestion in a certain phase (occupancy rate > AAH%), the duration of that phase will be increased (the adjustment amount is based on the degree of congestion; for example, for every increase of AAZ% in occupancy rate, the phase duration increases by AB0%). If vehicle travel time data shows an increase in the travel time of a guidance route (exceeding the expected time by AB1%), the route will be replanned, and a shorter alternative route will be selected. For example, if the occupancy rate of the eastbound straight phase lane at the TQ intersection is AB2% (>AAH%), the eastbound straight phase duration will be increased from SH seconds to AB4 seconds by AB3%. If the travel time of the BR→SW→SX route increases by AB5%, the route will be switched to BR→TA→TB.

[0190] Step S156: Based on the peak changes in the traffic flow response data, update the linkage trigger sequence in the control action linkage scheme.

[0191] Changes in peak traffic flow in the traffic flow response data (such as earlier or later peak occurrence time, or increased or decreased peak traffic flow) are used to adjust the trigger threshold. If the peak traffic flow increases by AB6%, the trigger threshold is lowered (e.g., the traffic change rate threshold is lowered from VX to AB7% to AB8); if the peak time is advanced by AB9 minutes, the trigger time is advanced by AB9 minutes accordingly. For example, the original trigger condition was "traffic change rate > VX", and the updated condition is "traffic change rate > AB8"; the original trigger time was time slice VZ, and the updated time slice is VZ-AC0.

[0192] Step S157: Based on the time-period difference characteristics in the road segment traffic change data, adjust the time-period sub-interval division and the start and end times of each time period in the time-period execution process.

[0193] Analyze the differences in traffic flow data across different time periods (e.g., prolonged congestion during morning rush hour and increased fluctuations in off-peak traffic) and re-divide time period sub-intervals. If the standard deviation of traffic flow in a certain time period increases from AD to AC1 (>WM), the duration of the sub-interval for that time period is shortened from AC2 minutes to AC3 minutes (the duration corresponding to drastic fluctuations). If adjacent time periods have similar traffic characteristics (correlation coefficient > AC4), they are merged into one sub-interval. For example, if the original morning rush hour was divided into two sub-intervals, AC5-AC6 and AC6-AC7, analysis reveals similar traffic characteristics, and they are merged into one sub-interval, AC5-AC7, with a duration adjusted to AC8 minutes.

[0194] Step S158: According to the user response situation in the path selection change data, update the frequency and content composition of the specific specifications for passing information push in the execution process by time period.

[0195] If the path selection change data shows that the response rate of users to the push information is low (path switching rate < AC9%), increase the push frequency (such as increasing from once every VT minutes to once every AD0 minutes), and optimize the content composition (add real-time traffic condition videos, estimated delay times, etc.); if the response rate is high (> AD1%), maintain the current frequency or appropriately reduce it. For example, if the original push frequency is once every VT minutes and the response rate is AD2% (< AC9%), then update it to once every AD0 minutes, and the content is increased with "the current congestion picture of the AW section ahead" and "estimated delay of AD3 minutes".

[0196] Step S159: Integrate the adjusted intersection signal control parameter adjustment operation content, road section traffic flow guidance path planning scheme, linkage trigger sequence, time period sub-interval division, and specific specifications for passing information push to generate an updated regulation action linkage scheme and an execution process by time period.

[0197] Integrate the above adjustment contents into the original scheme and process to ensure data consistency and logical coherence. The updated regulation action linkage scheme includes new signal control parameters, path planning, and trigger sequences; the updated execution process by time period includes adjusted time period sub-intervals, push specifications, etc. Conduct conflict detection (such as time conflict, parameter conflict) during the integration process, and verify the adjustment effect through simulation to ensure that the updated scheme and process are better than the original scheme.

[0198] Step S1510: Based on the updated regulation action linkage scheme and the execution process by time period, generate new regulation instructions and continuously issue them to each execution entity for execution.

[0199] Convert the updated regulation action linkage scheme and the execution process by time period into new regulation instructions and issue them to each execution entity in the manner of Step S141. At the same time, set a scheme evaluation period (such as evaluating once every AD4 time period sub-intervals), continuously monitor the execution effect, and continuously iterate and optimize to form a closed-loop cycle of road emission collaborative control. Through the above dynamic adjustment mechanism, ensure that the regulation scheme can adapt to real-time traffic condition changes and continuously reduce the risk of concentrated superposition of road emissions. In an exemplary embodiment, a road emission collaborative control system based on traffic flow prediction is provided. The road emission collaborative control system based on traffic flow prediction can be a terminal, a server, etc., and its internal structure diagram can be as Figure 2As shown, the road emission collaborative control system based on traffic flow prediction includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements a road emission collaborative control method based on traffic flow prediction. The display unit is used to generate a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the housing of the road emission cooperative control system based on traffic flow prediction, or an external keyboard, touchpad, or mouse, etc.

[0200] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A road emission collaborative control method based on traffic flow prediction, characterized in that, The method includes: Based on traffic flow prediction data, road emission concentration superposition risk scenarios are identified, and spatiotemporal identifiers of risk scenarios are generated. The spatiotemporal identifiers of risk scenarios include the associated links of potential road segments with concentrated emission superposition, the high-incidence time sequence of concentrated emission superposition, and the impact range of concentrated emission superposition. Based on the spatiotemporal identifier of risk scenarios, a multi-segment intersection control action linkage scheme is generated. The control action linkage scheme integrates the operation sequence and correlation of intersection signal control parameter adjustment operation, road segment traffic guidance path planning scheme and traffic information push execution operation. The time-segmented execution process is generated according to the control action linkage scheme. The time-segmented execution process includes a list of road sections and intersections participating in the control in each time period, specific control action content, and action linkage trigger sequence. The multi-entity synchronous execution procedure is initiated according to the time-segmented execution process, and the actual execution data of each control action and the real-time operation feedback data of road sections and intersections are collected. Based on actual execution data and real-time operational feedback data, the control action linkage scheme and time-segmented execution process are adjusted, updated control instructions are generated and continuously executed, forming a road emission coordinated control cycle.

2. The road emission coordinated control method based on traffic flow prediction according to claim 1, characterized in that, The method of anchoring road emission concentration and superimposing risk scenarios based on traffic flow prediction data to generate spatiotemporal identifiers for risk scenarios includes: Acquire traffic flow prediction data, which includes traffic flow change data, vehicle driving status data, and intersection turning traffic data for each road segment at different times. Collect basic road data for each road segment, including data on the number of lanes in the road segment, data on the distribution of surrounding buildings, and data on the connection forms of intersections; The traffic flow forecast data is matched with the number of lanes in the road section in the basic road data, and the traffic pressure distribution data of the road section is generated by analyzing the correspondence between the traffic flow change magnitude and the lane carrying capacity. The vehicle driving status data in the traffic flow prediction data is correlated with the surrounding building distribution data in the road basic data, and emission generation feature data is extracted and generated according to the spatial relationship between driving status and building distribution. The intersection turning traffic flow data in the traffic flow prediction data is combined with the intersection connection form data in the road basic data, and traffic flow convergence data is generated according to the adaptation relationship between the turning traffic flow scale and the intersection connection structure. Based on road segment traffic pressure distribution data, emission generation characteristic data, and traffic flow convergence data, potential initial road segments with concentrated emission overlays are selected and generated. Starting from the initial road segment where emissions are concentrated and superimposed, the traffic flow path of adjacent road segments is obtained, and directly related road segments and indirectly related road segments are integrated to generate the associated links of the potential road segments where emissions are concentrated and superimposed. Peak traffic flow data for different time periods are extracted from traffic flow prediction data for each road segment, and a sequence of high-incidence emission periods is generated by superimposing peak occurrence frequency and duration. By combining the coverage of the potential road segments associated with concentrated emissions and the time span of the high-incidence period sequence of concentrated emissions, the range of impact of concentrated emissions is determined. By integrating the potential road segment association links of concentrated emissions, the high-incidence time sequence of concentrated emissions, and the impact range of concentrated emissions, a complete spatiotemporal identifier for risk scenarios is generated.

3. The road emission coordinated control method based on traffic flow prediction according to claim 1, characterized in that, The multi-segment intersection control action linkage scheme based on risk scenario spatiotemporal identifiers includes: The association links of potential road segments with concentrated emissions in the spatiotemporal identification of risk scenarios are analyzed, and core control road segments and auxiliary control road segments are divided. The control priorities are generated by sorting the road segments according to their traffic flow transmission role in the association links of potential road segments with concentrated emissions. In the sorting results, the core control road segments are placed before the auxiliary control road segments. Extract emission concentrations and overlay high-incidence time periods from the spatiotemporal identifiers of risk scenarios, and split them to generate different time period sub-intervals. Each time period sub-interval corresponds to a set of traffic flow change characteristic data. Based on the traffic flow convergence data and emission generation characteristic data of the core control section, adjust the operation content of the intersection signal control parameters and allocate the running time of each phase according to the traffic flow change characteristic data; Based on the traffic pressure distribution data of the auxiliary control road sections, a road section traffic flow guidance path planning scheme is generated. The road section traffic flow guidance path planning scheme includes a path that avoids the concentrated superposition of emission impact range of the core control road section. By combining the time period sub-intervals of the concentrated emission superimposed high-incidence period sequence, traffic information push execution operation content is generated. The traffic information push execution operation content includes the push time node and the coverage road segment range. The coverage road segment range includes the connection node between the core control road segment and the auxiliary control road segment. Adjust the execution order of the operation according to the intersection signal control parameters, generate a timing connection standard, and determine the start time parameters of the traffic flow guidance path planning based on the timing connection standard to achieve the timing connection between traffic flow guidance actions and signal control adjustment actions; Based on the path update status of the road segment traffic guidance path planning scheme, generate traffic information push update frequency parameters, and update the push information based on these frequency parameters; Based on the traffic flow change characteristic data of time sub-intervals, generate a linkage trigger sequence for adjusting intersection signal control parameters, planning road segment traffic flow guidance paths, and pushing traffic information execution operations. Integrate the operation content of intersection signal control parameter adjustment, road segment traffic guidance path planning scheme, traffic information push execution operation content, timing connection standard, traffic information push update frequency parameter and linkage trigger sequence to generate a preliminary control action linkage scheme; Based on the concentrated impact range of emissions in the spatiotemporal identifiers of risk scenarios, the content of control actions for edge road sections is added to generate a complete control action linkage plan.

4. The road emission coordinated control method based on traffic flow prediction according to claim 1, characterized in that, The time-segmented execution process generated according to the control action linkage scheme includes: Extract the time period sub-interval division results from the control action linkage scheme, determine the start and end times of each time period sub-interval, and ensure that the duration of each time period sub-interval is adapted to the stability of traffic flow changes. According to the control priority in the control action linkage plan, a list of road segments and intersections participating in control is generated in each time period sub-interval. The list of road segments and intersections includes core control road segments, auxiliary control road segments and related intersections. Based on the intersection signal control parameters in the control action linkage scheme, the operation content is adjusted to generate the phase adjustment sequence, the running time of each phase, and the signal cycle adjustment range of the intersection signal in each time period sub-interval. Based on the traffic flow guidance path planning scheme in the linkage control action plan, the traffic flow guidance method, diversion path direction and traffic flow allocation ratio of the road segment entrance in each time period sub-interval are generated; Based on the traffic information push execution operation content in the control action linkage plan, generate the content composition of traffic information in each time period sub-interval, the selection of push channels, and the setting of push frequency; Based on the timing connection standard in the control action linkage plan, generate the start sequence and time interval of the intersection signal control parameter adjustment operation, the road segment traffic guidance path planning scheme, and the traffic information push execution operation within each time period sub-interval; Based on the traffic information push update frequency parameters in the control action linkage plan, the update time node of traffic information in each time period sub-interval is generated; Based on the linkage trigger sequence in the linkage scheme, generate the trigger conditions for each linkage action in each time period sub-interval; Record the list of road segments and intersections involved in regulation within each time period sub-interval, the specific content of intersection signal control adjustments, the specific details of road segment traffic guidance operations, the specific specifications for traffic information push, the action initiation sequence, information update nodes and triggering conditions; Integrate all time-segment-specific control information to generate a complete time-segmented execution process.

5. The road emission coordinated control method based on traffic flow prediction according to claim 1, characterized in that, The multi-entity synchronous execution procedure, initiated according to the time-segmented execution process, collects actual execution data of each control action and real-time operational feedback data of road segments and intersections, including: According to the start time in the time-segmented execution process, a control start command is sent to the signal control execution entity, the flow guidance execution entity, and the information push execution entity. The control start command includes the control action requirements for the corresponding time period. After receiving the control start command, the signal control execution entity performs the specific adjustments to the intersection signal control in the time-segmented execution process, adjusts the signal control parameters of the corresponding intersection, and records the changes in the parameters before and after the adjustment and the adjustment completion time. The traffic guidance execution entity implements traffic guidance measures at the entrance of the corresponding road segment based on the control activation command and the specific details of the road segment traffic guidance operation in the time-segment execution process, and records the execution method, activation time and coverage of the guidance measures; The information push execution entity, in accordance with the control activation command and the specific specifications for traffic information push in the time-segmented execution process, sends traffic guidance information to vehicle users through preset push channels, and records the information sending time, content details and push channel usage; Integrate the parameter adjustment records of the signal control execution entity, the guidance measure execution records of the flow guidance execution entity, and the information sending records of the information push execution entity to generate actual execution data for each control action; During the implementation of the control measures, real-time traffic flow data, vehicle speed data, and lane occupancy data of each participating road segment are collected through monitoring equipment installed on the road segments. The monitoring equipment deployed at the intersections collects real-time turning traffic flow data, signal phase operation data, and vehicle travel time data for each intersection involved in the control. Real-time traffic flow data, vehicle speed data, lane occupancy data, real-time turning traffic flow data, signal phase operation data, and vehicle passage time data are classified and integrated to generate real-time operation feedback data for road segments and intersections. Establish a time-correspondence relationship between actual execution data and real-time operation feedback data, so that the execution status of control actions at the same time point is matched with traffic operation status data; Continuously collect actual execution data and real-time operational feedback data until the termination time of the time-segmented execution process is reached, and complete the execution of the control actions for that time segment.

6. The road emission coordinated control method based on traffic flow prediction according to claim 1, characterized in that, The linkage scheme for adjusting and controlling actions and the time-segmented execution process based on actual execution data and real-time operational feedback data include: Extract the signal control parameter adjustment records from the actual execution data, compare them with the signal phase operation data in the real-time operation feedback data, and obtain the traffic flow change response data after the signal control adjustment. The execution records of traffic guidance measures are obtained from the actual execution data. Combined with the real-time traffic data and vehicle speed data in the real-time operation feedback data, the traffic change data of the road section after traffic guidance is obtained. Obtain the information push records from the actual execution data, compare them with the vehicle passage time data in the real-time operation feedback data, and extract the path selection change data after the passage information is pushed. Based on traffic flow change response data, road segment traffic change data, and route selection change data, we analyze the relationship between the execution effect of each action in the control action linkage scheme and traffic flow changes. Based on the lane occupancy data and vehicle travel time data in the real-time operation feedback data, the operation content of the intersection signal control parameters and the direction of the road segment traffic flow guidance path planning scheme in the linkage control action plan are adjusted. Based on the peak changes in traffic flow response data, update the linkage trigger sequence in the control action linkage scheme; Based on the time-period difference characteristics in the road segment traffic change data, the time-period sub-interval division and the start and end times of each time period in the time-period execution process are adjusted. Based on user response data in the path selection change data, update the frequency and content composition of the specific specifications for pushing access information in the time-segmented execution process; The integrated and adjusted intersection signal control parameter adjustment operation content, road segment traffic guidance path planning scheme, linkage trigger sequence, time period sub-interval division and traffic information push specific specifications are used to generate an updated control action linkage scheme and time period execution process. Based on the updated control action linkage scheme and time-segmented execution process, new control instructions are generated and continuously issued to each implementing entity for execution.

7. The road emission coordinated control method based on traffic flow prediction according to claim 2, characterized in that, Starting from the initial potential road segment where emissions are concentrated, the process involves obtaining the traffic flow paths of adjacent road segments, integrating directly related and indirectly related road segments, and generating a link between potential road segments with concentrated emissions, including: Obtain basic road data of potential initial road segments with concentrated emissions, extract information on connecting road segments at both ends of the potential initial road segments with concentrated emissions, and determine directly adjacent road segments; Collect traffic flow prediction data of directly adjacent road segments and analyze the traffic flow exchange scale data of directly adjacent road segments and potential initial road segments with concentrated emissions; Based on traffic flow exchange scale data, directly adjacent road segments that have high-frequency traffic flow exchange with potential initial road segments with concentrated emissions are identified and loaded into the set of directly related road segments; Extract the basic road data of each road segment in the set of directly related road segments, determine the adjacent road segments of the directly related road segments, and obtain the indirectly adjacent road segments; Collect traffic flow prediction data for indirectly adjacent road segments and analyze the traffic flow transfer scale data between indirectly adjacent road segments and directly related road segments; Based on the traffic flow transmission scale data, identify the indirect adjacent road segments that have continuous traffic flow transmission with the directly related road segments, and load them into the indirect related road segment set; Obtain traffic flow direction data for each road segment in the directly associated road segment set and the indirectly associated road segment set to form traffic flow transmission paths between road segments; A connection map between road segments is generated according to the order of traffic flow paths. The connection map includes the traffic flow input and output directions of each road segment. Based on the connectivity map, the role of each road segment in traffic flow transfer is determined as either a core transfer road segment or an auxiliary transfer road segment; By integrating the sets of directly associated road segments, the sets of indirectly associated road segments, traffic flow transmission paths, connection relationship maps, and the annotation results of core transmission road segments and auxiliary transmission road segments, a complete emission-overlay potential road segment association link is generated.

8. The road emission coordinated control method based on traffic flow prediction according to claim 3, characterized in that, The execution sequence of the operation content adjusted according to the intersection signal control parameters is used to generate a timing connection standard. Based on the timing connection standard, the start time parameters for the traffic flow guidance path planning of the road segment are determined to achieve the timing connection between traffic flow guidance actions and signal control adjustment actions, including: Extract the intersection signal control parameter adjustment operation content from the control action linkage scheme, and determine the execution steps of the signal control adjustment and the estimated completion time of each step; Determine the traffic flow change response period corresponding to each signal control adjustment step, wherein the traffic flow change response period is the time span from the completion of signal parameter adjustment to the stabilization of traffic flow status. Based on the estimated completion time of signal control adjustments and the traffic flow change response cycle, calculate the traffic flow stabilization time point corresponding to each signal control adjustment step; Obtain the operation steps for planning the traffic flow guidance path for a road segment, and record the execution time required for each operation step; Based on the stable traffic flow time point, a timing connection standard is generated, which includes the time matching rules for signal control adjustment steps and traffic flow guidance operation steps. Based on the time sequence connection standard, the start time of each operation step of the road segment traffic guidance path planning scheme is determined; Set the time interval between the start time parameter of the traffic flow guidance path planning and the completion time of the signal control adjustment steps; Record the sequence of operations in the traffic flow guidance path planning scheme for each road segment, and arrange the operation execution sequence in order of start time; The execution sequence of signal control adjustment steps is integrated with the operation execution sequence of the road segment traffic guidance path planning scheme, and the start time and execution duration of each action are marked. The integrated action sequence and timing connection standard are loaded into the control action linkage scheme as the time basis for action linkage execution.

9. The road emission coordinated control method based on traffic flow prediction according to claim 4, characterized in that, The traffic flow guidance path planning scheme based on the control action linkage plan generates the traffic flow guidance method, diversion path direction, and traffic flow allocation ratio at the entrance of the road segment within each time period sub-interval, including: Extract the traffic flow guidance path planning scheme from the linkage scheme of the control action, and extract and record the positional relationship between the target diversion road segment and the core control road segment in each time period sub-interval; Collect basic road data for the target diversion routes and analyze the number of lanes and traffic capacity data for the target diversion routes; By combining traffic flow forecast data within the time period sub-interval, the expected traffic volume of the core control road section and the capacity to handle traffic volume of the target diversion road section are calculated. Based on the correspondence between the expected traffic volume and the capacity to handle traffic volume, the traffic guidance method at the entrance of the road segment is determined, and the traffic guidance method includes lane indication adjustment and entrance guidance sign setting; The diversion path is determined based on the positional relationship between the core control road section and the target diversion road section, and the diversion path avoids the core area within the range of concentrated and superimposed emission impact. Based on the capacity of the target diversion road segments and the projected capacity of the core control road segments, determine the traffic flow allocation ratio from the core control road segments to each target diversion road segment. Designate a person responsible for traffic guidance operations at the entrance of each road segment; Set the execution time for the traffic redirection method, and the execution time shall be consistent with the start time of the sub-interval of the time period; The location for setting up signs to determine the direction of the diversion path is located in the road segment area before the diversion node; By integrating the traffic guidance methods, diversion routes, traffic allocation ratios, responsible personnel, execution times, and signage locations within each time period sub-interval, specific details of the traffic guidance operation for each road segment are generated.

10. A road emission collaborative control system based on traffic flow prediction, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the road emission cooperative control method based on traffic flow prediction as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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