Traffic area road network simulation analysis method and device

Abstract

This application discloses a method and apparatus for simulating and analyzing traffic network in a traffic area. The method includes: constructing basic data for the simulated road network; calculating the flow difference between directed road segments at adjacent intersections; dynamically inserting virtual flow distribution points into the simulated paths corresponding to the directed road segments in response to a non-zero flow difference, and configuring the flow parameters of the virtual flow distribution points according to the flow difference; generating road network simulation data based on the basic data of the simulated road network, the flow data, and the flow parameters of the virtual flow distribution points, performing traffic simulation, and obtaining traffic operation indicators for the simulated area; dynamically adjusting the insertion position parameters of the virtual flow distribution points, recursively updating the road network simulation data, performing traffic simulation, and obtaining updated traffic operation indicators for the simulated area; comprehensively evaluating the traffic operation indicators of each road network simulation data, and selecting the optimal road network simulation data. Using this application, the problem of difficulty in collecting all existing trajectory data is solved, while achieving automatic flow allocation and road network optimization.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a method and apparatus for simulating and analyzing traffic area road networks. Background Technology

[0002] With the acceleration of urbanization, the functional division of urban areas is becoming increasingly refined, and the operational efficiency of regional traffic has become a key indicator for evaluating the rationality of regional planning and the quality of development. Against this backdrop, traffic organization optimization and traffic signal optimization are common technical means to alleviate regional congestion. Traffic regional road network simulation algorithms, due to their ability to economically, safely, and efficiently pre-simulate and verify the effects of these optimization schemes in a virtual environment, have become an indispensable technical support in the field of urban traffic management. A high-fidelity traffic simulation relies on two core data foundations: dynamic and realistic traffic flow data, and basic road network data that accurately reflects the physical road connectivity and attributes. Therefore, how to efficiently and accurately allocate lane-level traffic flow data and dynamically generate and optimize road network models that conform to physical reality is crucial for constructing regional road network simulation traffic allocation algorithms, and is of great significance for solving urban road congestion problems.

[0003] In related technologies, the system selects two algorithmic paths. One is to collect full vehicle trajectory data, which involves installing positioning devices in vehicles to collect vehicle positioning data and then plotting it into vehicle trajectory data to provide data support for simulation. The other is to collect license plate data through cameras, and then generate regional OD data through statistical analysis of the vehicles, providing data support for simulation.

[0004] However, neither of these two algorithmic approaches can efficiently simulate regional road networks. The reasons are as follows: 1. Due to restrictions on personal privacy and vehicle usage, it is impossible to install positioning devices on a large number of vehicles, thus preventing the acquisition of complete trajectory data and consequently hindering accurate traffic simulation. 2. Since existing algorithms and technologies cannot acquire complete trajectory data, supplementing trajectory data through sampling or coefficient-based rules is limited by algorithm accuracy, thus preventing accurate traffic simulation. 3. Configuring OD data for regional traffic simulation is problematic because the time complexity of OD data increases exponentially with the size of the regional road network, becoming a bottleneck for regional traffic simulation. Therefore, OD data-based simulation cannot optimize the road network. Summary of the Invention

[0005] This application provides a method and apparatus for simulating and analyzing traffic area road networks. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0006] In a first aspect, embodiments of this application provide a method for simulating and analyzing road networks in a traffic area, the method comprising: Based on the physical road network data of the target city area, construct the basic data of the simulated road network, which includes the simulated road network and the timing data of each intersection; Calculate the flow difference between directional road segments between adjacent intersections. The flow difference is the difference between the outflow from the starting intersection of the road segment and the inflow from the ending intersection of the road segment. In response to the traffic difference not being equal to zero, a virtual traffic hub is dynamically inserted into the simulation path corresponding to the directed road segment, and the traffic parameters of the virtual traffic hub are configured according to the traffic difference. Based on the basic data of the simulated road network, traffic data, and traffic parameters of the virtual traffic distribution center, road network simulation data is generated, traffic simulation is performed, and traffic operation indicators of the simulated area are obtained. The insertion location parameters of the virtual traffic distribution center are dynamically adjusted, the road network simulation data is recursively updated, traffic simulation is performed, and the updated traffic operation indicators of the simulation area are obtained. A multi-index decision evaluation algorithm is used to comprehensively evaluate the traffic operation indicators of each road network simulation data, and the optimal road network simulation data is selected.

[0007] Secondly, embodiments of this application provide a traffic area road network simulation analysis device, the device comprising: The simulated road network basic data construction module is used to construct simulated road network basic data, including the simulated road network and the timing data of each intersection, based on the physical road network data of the target urban area. The flow difference calculation module is used to calculate the flow difference between oriented road segments between adjacent intersections. The flow difference is the difference between the outflow flow at the starting intersection of the road segment and the inflow flow at the ending intersection of the road segment. The traffic parameter configuration module is used to dynamically insert virtual traffic distribution points into the simulation path corresponding to the directed road segment in response to a traffic difference that is not equal to zero, and to configure the traffic parameters of the virtual traffic distribution points according to the traffic difference. The simulation module is used to generate road network simulation data based on the basic data of the simulated road network, standardized hourly traffic data, and traffic parameters of the virtual traffic distribution center, perform traffic simulation, and obtain traffic operation indicators of the simulated area. The update module is used to dynamically adjust the insertion location parameters of the virtual traffic distribution center, recursively update the road network simulation data, perform traffic simulation, and obtain the updated traffic operation indicators of the simulation area. The comprehensive evaluation module is used to comprehensively evaluate the traffic operation indicators of each road network simulation data using a multi-index decision evaluation algorithm, and select the optimal road network simulation data.

[0008] The technical solutions provided in some embodiments of this application may include the following beneficial effects: In this embodiment, on the one hand, by acquiring the original vehicle passage records of intersection lanes and generating lane-level traffic datasets based on these records, the reliance on single-vehicle trajectory data is successfully bypassed. This provides a core input for simulation that is both legal and compliant, and reflects the true traffic distribution, effectively solving the simulation distortion problem caused by unavailable data. On the other hand, the traffic difference between directed road segments at adjacent intersections is calculated, and virtual traffic distribution points are dynamically inserted for compensation. The traffic difference directly originates from the actual observed cross-sectional traffic, accurately reflecting the traffic volume that disappears or is generated out of thin air in the road network. The virtual distribution point acts as a corrector, automatically filling the gap between this observation and the model, enabling the simulation system to self-adjust to match the actual observed traffic, avoiding systematic bias caused by inaccurate algorithm estimation, and significantly improving the fidelity of the simulation. Furthermore, the time complexity of comparing traffic differences and parameterizing the distribution point configuration is linearly related to the number of road segments, greatly reducing the complexity and computational cost of model construction, making it possible to perform efficient and automated simulation modeling and iterative optimization of large-scale, complex regional road networks.

[0009] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0011] Figure 1 This is a flowchart illustrating a traffic area road network simulation analysis method provided in an embodiment of this application; Figure 2 This is a visualization of the upstream and downstream road sections of an intersection provided in an embodiment of this application; Figure 3 This is a visualization of intersection lane connectivity data provided in an embodiment of this application; Figure 4 This is a visualization of simulated road network basic data provided in an embodiment of this application; Figure 5This is a schematic diagram of the structure of a traffic area road network simulation analysis device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The following description and accompanying drawings fully illustrate specific embodiments of this application to enable those skilled in the art to practice them.

[0013] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0014] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0015] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0016] The following will be combined with the appendix Figure 1 - Appendix Figure 4 This application provides a detailed description of the traffic area road network simulation analysis method provided in its embodiments. This method can be implemented using a computer program and can run on a traffic area road network simulation analysis device based on the von Neumann architecture. This computer program can be integrated into applications or run as a standalone utility application.

[0017] Please see Figure 1 This document provides a flowchart illustrating a traffic area road network simulation analysis method as described in an embodiment of this application. Figure 1 As shown, the method in this application embodiment may include the following steps: S101, Based on the physical road network data of the target urban area, construct the basic data of the simulated road network, which includes the simulated road network and the timing data of each intersection; The physical road network data includes the various entrance directions and exit directions of each intersection, the lanes they contain, and the turning attributes of each lane.

[0018] In some embodiments of this application, the specific process of constructing basic simulated road network data, including simulated road network and timing data of each intersection, based on physical road network data of the target urban area includes: encapsulating each intersection's entrance direction, exit direction, and its included lanes, as well as the turning attributes of each lane, to obtain intersection channelization data for each intersection; generating intersection upstream and downstream relationship data based on the intersection channelization data for each intersection; creating simulated road segments between each pair of intersections with direct connections using the intersection upstream and downstream relationship data, to obtain intersection upstream and downstream road segments including upstream and downstream intersections and multiple simulated road segments; merging the intersection upstream and downstream road segments for each intersection to obtain a simulated road network file; configuring the intersection lane connection direction and signal timing based on the intersection channelization data for each intersection to obtain timing data for each intersection; and combining the simulated road network file and the timing data for each intersection into basic simulated road network data.

[0019] Intersection channelization data refers to the digital definition of the geometry and traffic organization rules of physical intersections, including at least the identification of each entrance and exit direction, as well as the lanes and permitted vehicle turning attributes for each direction. Intersection upstream and downstream relationship data characterizes the logical relationships of connections and traffic directions between different intersections in the road network, defining the drivable paths for vehicles within the network. Simulated road segments are logical traffic units created in the simulation environment based on the upstream and downstream relationships of intersections, forming the basic link segments for vehicle travel. Intersection upstream and downstream road segments are data structures describing the set of simulated road segments centered on a single intersection and connecting it to all directly connected upstream and downstream intersections. Timing data is the traffic signal control scheme configured for the intersection based on the lane connection logic defined by the intersection channelization data. This includes dividing the signal cycle into multiple phases and allocating specific lane connection combinations and corresponding traffic light states for each phase.

[0020] For example, through import direction Export direction Entrance lane Exit lane Generate intersection channelization data for each intersection. .

[0021] in, It is a unique identifier used to distinguish different lanes, directions, or intersections. It refers to geographical location. It is a lane name or descriptive label. This is a list representing the import lanes. This is a list of exit lanes. It is the steering attribute of each lane. It represents the number of elements. It is a symbol used to describe the number of mapping relationships.

[0022] For example, .

[0023] in, It is a left-turn lane. It is the right-turn lane. It is a straight-ahead lane. It is located in the east. It is specifically located to the west. It is specifically located in the south. It is specifically located in the north.

[0024] For example, data on upstream and downstream relationships at intersections. Simulated intersections are directly connected via simulated road edge segments.

[0025] The first one Indicates the starting intersection (upstream intersection) of the connection. The second one... Indicates the destination intersection (downstream intersection) of the connection. express It is an ID referenced from a specific intersection in the intersection set.

[0026] For example, simulated road segment edge=[ .

[0027] ,

[0028] in, It is the starting node of the road segment. It is the end point of the road segment. It refers to the number of entrance lanes at the connector at the end of the road segment. This refers to the number of exit lanes at the connection point at the beginning of the road segment. The max function indicates that the larger of the two values ​​is taken as the number of lanes for that road segment.

[0029] For example Figure 2 As shown, the upstream and downstream sections of the intersection This includes upstream and downstream intersections and multiple simulated road sections, among which... .

[0030] Finally, a simulated road network file is generated. .

[0031] Specifically, the process of configuring lane connection directions and signal timings at each intersection based on the intersection channelization data to obtain the timing data for each intersection includes: determining the allowed turning rules for each approach lane based on the intersection channelization data; pairing each approach lane with all legal exit lanes using the turning rules; establishing connection pairs for each approach lane and all corresponding legal exit lanes as intersection lane connectivity data; dividing a single signal cycle into multiple phases using the intersection lane connectivity data; simultaneously granting right-of-way to a set of non-conflicting lane connections within each phase; associating each phase with specific traffic light states; and binding and encapsulating each phase and the associated traffic light states to obtain the timing data for each intersection.

[0032] For example, based on the intersection channelization data of each intersection It can draw intersection lane connectivity data. ,For example Figure 3 As shown; then generate timing. .

[0033] Timing data Including phase The state data is defined as state=[{r,R,g,G,y,Y}]. The state data uses r,R,g,G,y,Y to identify signal passability, where timing data is also included. .

[0034] in, It is a list of lane connection pairs that are allowed to pass under the phase. It is the duration of the phase. It is with phase The specific traffic light status that is linked and displayed to the driver. r is red, R is full-screen red light, g is green, G is full-screen green light, y is yellow light, and Y is full-screen yellow light.

[0035] Finally, the simulation road network basic data Simulation_net is generated. This generated basic data includes the simulation road network file road_net and the timing data traffic_light. Based on the simulation road network file road_net and timing data traffic_light generated in the above steps, the simulation road network basic data Simulation_net={road_net,traffic_light} is generated, for example... Figure 4 As shown.

[0036] In one possible implementation, intersection entrance data is generated. Generate intersection exit data .

[0037] Generate junctionA at intersection A = [ {1, ,

[0038] }, … … {4, ,

[0039] } ] Generate junctionB at intersection B = [ {1, ,

[0040] }, … … {4, ,

[0041] } ] in, , edge=[{001,001,002,3},{002,001,003,3}…], junction_edge=[junctionA, [{001,001,002,3},{002,001,003,3}…],junctionB],

[0042] .

[0043] In one possible implementation, when generating timing data for each intersection, lane connectivity data for the intersection is also included. phase Timing data for each intersection .

[0044] In one possible implementation, when combining the simulated road network file and the timing data for each intersection into the basic simulated road network data, Simulation_net={ , }

[0045] S102, calculate the flow difference between directional road segments between adjacent intersections. The flow difference is the difference between the outflow from the starting intersection of the road segment and the inflow from the ending intersection of the road segment. In some embodiments of this application, the specific process of calculating the flow difference between directed road segments of adjacent intersections includes: acquiring the original vehicle passage records of each lane in each intersection; dividing the original vehicle passage records into non-overlapping time slices using a preset time interval; summing and statistically analyzing each time slice to determine the number of vehicles passing through each time slice, which serves as a lane-level cross-sectional flow dataset for a preset time interval; converting the lane-level cross-sectional flow dataset for a preset time interval into a target dataset of preset units, which serves as a lane-level standardized hourly flow dataset; the standardized hourly flow dataset records the standardized hourly flow of each lane and each turn within a specific time period; and calculating the flow difference between directed road segments of adjacent intersections based on the standardized hourly flow dataset.

[0046] For example, raw vehicle traffic records for each lane at intersections are obtained through technical equipment deployed at intersections or manual data collection. The raw vehicle traffic record `ods_lane_flow=[{time,dir,lane,hphm,tunc,type}]`, where `time` is the observation time, `dir` is the vehicle's direction, `lane` is the specific lane number, `hphm` is the vehicle identifier, `tunc` is the direction of travel, and `type` is the recording point type (whether it's an entrance or exit record). , , , Then, the raw data is summed and statistically analyzed in 15-minute intervals to generate lane-level 15-minute traffic flow data (15min_lane_flow).

[0047] 15min_lane_flow=sum(ods_lane_flow,15); where, Then, the 15-minute traffic data is converted to pcu / h, where pcuh_lane_flow = 15min_Lane_flow * 4. .

[0048] Here, sum(ods_lane_flow,15) is an aggregation function that sums the original data ods_lane_flow within a fixed time window of 15 minutes. It is a list, where each element is a statistical record, indicating how many vehicles passed through a specific lane in a specific direction within a certain 15-minute period. It indicates a 15-minute time period. It represents the direction (e, w, s, n). It is the lane number. It is the steering (l,r,z). It is the record point type (in / out).

[0049] In one possible implementation, a lane-level standardized hourly traffic dataset is used:

[0050]

[0051] .

[0052] In some embodiments of this application, the specific process of calculating the flow difference between directed road segments between adjacent intersections based on a standardized hourly flow dataset includes: selecting any two road segments between intersections with direct connections in the road network as directed road segments between adjacent intersections, thus obtaining a set of directed road segments; calculating the inflow flow of each directed road segment based on the standardized hourly flow dataset; calculating the outflow flow of each directed road segment based on the standardized hourly flow dataset; and calculating the flow difference between the inflow flow and the outflow flow as the flow difference between directed road segments between adjacent intersections.

[0053] For example, to calculate the flow difference Cal_FlwDiff between directional road segments at adjacent intersections, the road segment between the two intersections is denoted as junction_road, and the flow difference between the road segments is the inflow flow at the starting intersection of the road segment minus the outflow flow at the ending intersection of the road segment.

[0054] In some embodiments, firstly, a directed road segment between any two intersections that have a direct connection is selected. ;in, , Then, based on the standardized hourly flow dataset pcuh_Lane_flow, the inflow flow to each directed road segment is calculated and labeled. .

[0055] Specific callable functions Calculate the inflow flow of each directed road segment, where Similarly, calculate the outflow of each directed road segment and mark it as follows. .in, Finally, the flow difference between directional road segments at adjacent intersections is calculated. in, It filters out all records where the type field equals the input parameter type and the dir field equals the input parameter dir.

[0056] In one possible implementation, the flow difference between directed road segments at adjacent intersections is Cal_FlwDiff, and these directed road segments are labeled as junction_road. The flow difference between directed road segments at adjacent intersections is the inflow flow at the starting intersection of the segment minus the outflow flow at the ending intersection. First, directed road segments between adjacent intersections are generated. Then, based on the lane-level standardized hourly flow dataset pcuh_Lane_flow, the segment inflow flow for each directed road segment is calculated and labeled. =500, calculate the outflow of each directed road segment and mark it as . =700, and finally calculate the flow difference between the inflow and outflow of the road segment.

[0057] S103, in response to the traffic difference not being equal to zero, a virtual traffic distribution point is dynamically inserted into the simulation path corresponding to the directed road segment, and the traffic parameters of the virtual traffic distribution point are configured according to the traffic difference. The basic data of the simulated road network includes upstream and downstream road segments at intersections and the simulated road network file; there are any two directed road segments in the road network that have a direct connection relationship.

[0058] In some embodiments of this application, in response to a non-zero flow difference, the specific process of dynamically inserting a virtual flow distribution center into the simulated path corresponding to a directed road segment includes: identifying target road segments where the inflow and outflow flows are not conserved based on the flow difference; the target road segments include a starting intersection and an ending intersection; obtaining all continuous simulated road segments connecting the starting and ending intersections based on the upstream and downstream road segments and directed road segments; halving and rounding up the total number of road segments included in all continuous simulated road segments, and fine-tuning it in conjunction with a preset index offset OFFSET to obtain the flow distribution center between intersections; calculating the number of lanes in the flow distribution center between intersections, and generating the inbound and outbound edges of each simulated road segment based on the number of lanes; combining the inbound and outbound edges of each simulated road segment into a distribution center intersection, and updating the distribution center intersection to the simulated road network file; the distribution center intersection is the physical carrier and specific implementation of the virtual flow distribution center in the simulated road network.

[0059] For example, find all continuous simulated road edges and mark them as junc_edgeslist, based on the upstream and downstream road segments at the intersection. With directed road sections The f_junction_edges function can be called.

[0060] The specific method for executing this function can be further specified as: f_junction_edges(start_junction, end_junction){ ['0']==start_junction and return [1]};wherein,

[0061] Then, the flow distribution point Cal_Flw_Edge between the generated intersections is calculated, and its index position is calculated. Define the index offset OFFSET=0. Obtain the flow distribution points between intersections. .

[0062] Then calculate the number of lanes (Cal_Flw_Lane) at the flow distribution point between intersections, where... The function f_create_edge(number) is called to generate the inbound edge cal_in_edage and the outbound edge cal_out_edage for each simulated road segment.

[0063] Among them, cal_in_edage=f_create_edge(Cal_Flw_Lane,1), cal_out_edage=f_create_edge(Cal_Flw_Lane,-1), f_create_edge(number, dir){return {uuid, (Cal_Flw_Edge-dir).id,( Cal_Flw_Edge+dir).idCal_Flw_Edge[number].deepcopy()}.

[0064] Then, the entrance and exit sides of each simulated road segment are combined to form a distribution center intersection. Finally, it will be called. The function updates the Cal_FlwP intersection at the distribution point to the simulated road network file road_net, where { road_net[1].append( )}.

[0065] In one possible implementation, the function junc_edgeslis=[{001,001,002,3},…,{002,001,003,3}…]. Then calculate the flow distribution point Cal_Flw_Edge between intersections and calculate the index position. , Define the index offset OFFSET = 0. Obtain the flow distribution points between intersections. ={002,001,003,3}. Then calculate the number of lanes at the flow distribution points between intersections, Cal_Flw_Lane, where... =1, store the edge in the inbound direction as cal_in_edage=f_create_edge(1,1), store the edge in the outbound direction as cal_out_edage=f_create_edge(1,-1), and then generate the distribution center intersection using f_create_edge(number, dir). Then generate the distribution center intersection. Call The function updates the Cal_FlwP intersection at the distribution point to the simulated road network file road_net, where { road_net[1].append( )}.

[0066] In some embodiments of this application, the specific process of configuring the traffic parameters of the virtual traffic distribution center based on the traffic difference includes: marking the road segment as an inflow mode or an outflow mode according to the magnitude of the traffic difference; using the inflow mode or outflow mode as the functional type of the virtual traffic distribution center; calculating the ratio of the absolute value of the traffic difference to the inflow traffic of the road segment at the starting intersection to obtain the turning ratio; and encapsulating the road segment traffic distribution type and turning ratio and updating them to the intersection turning data to obtain the configuration parameters of the virtual traffic distribution center.

[0067] Specifically, the process of marking a road segment as either an inflow or outflow mode based on the magnitude of the flow difference includes: when the flow difference is less than 0, determining that the observed outflow flow is greater than the inflow flow in road segments with a flow difference of less than 0, and marking the function type of the road segment with a flow difference of less than 0 as an inflow mode; or, when the flow difference is greater than or equal to 0, determining that the observed outflow flow is greater than or equal to the inflow flow in road segments with a flow difference of greater than or equal to 0, and marking the function type of the road segment with a flow difference of greater than or equal to 0 as an outflow mode.

[0068] For example, determining the flow difference Cal_FlwDiff and the segment inflow flow at the starting intersection of the segment. The flow distribution type of the road segment is calculated using the flow difference Cal_FlwDiff and labeled as Cal_FlwType, where Cal_FlwType = Cal_FlwDiff < 0 ? IN : OUT. Then the steering ratio is calculated. Steering ratio . It takes the absolute value of the difference in flow rates.

[0069] Finally, the segment flow distribution type Cal_FlwType and the turning ratio Cal_pro are encapsulated to obtain the inter-intersection segment flow distribution traffic turning ratio Cal_FlwPro, where Cal_FlwPro={Cal_FlwType, Cal_pro}.

[0070] Finally, the encapsulated flow turning ratio Cal_FlwPro between intersection segments can update intersection turning data. .

[0071] In one possible implementation, the function type of the road segment is tagged as Cal_FlwType, where Cal_FlwType = (700 - 500) < 0 ? IN : OUT = IN. Then the steering ratio is calculated. ,in Finally, the flow turning ratio Cal_FlwPro between the intersection and the traffic flow distribution point is generated, where Cal_FlwPro={IN, 1}, and the intersection turning data is updated. .

[0072] In some embodiments of this application, the specific process of generating intersection turning data includes: performing grouping and summation operations on the standardized hourly traffic data set according to a preset aggregation dimension to obtain the intersection inlet turning traffic data set under each inlet-turn combination; the preset aggregation dimension includes inlet direction and lane turning; summing all turning traffic flows of each inlet direction at each intersection according to the intersection inlet turning traffic data set under each inlet-turn combination to obtain the total inflow of each inlet direction within the statistical period; calculating the ratio between the intersection inlet turning traffic data set under each inlet-turn combination and its corresponding total inflow to obtain the percentage of each turning traffic flow in the total inlet traffic flow, which is used as the intersection turning data.

[0073] For example, based on a standardized hourly traffic data set, according to lane turning... Import direction Perform grouped calculations to generate an intersection inlet turning flow dataset SilD_turning_flow=sum(pcuh_lane_flow, dir,turn) for each inlet-turn combination. .

[0074] Then, calculate the total inflow of each import direction during the statistical period: dir_flow = sum(SilD_turning_flow, dir).

[0075] Total inflow Then, based on the intersection turning flow dataset for each approach-turn combination, the turning flow ratio of each approach is calculated as percentage_flow = SilD_turning_flow[i] / dir_flow, which is used as the intersection turning data. .

[0076] In one possible implementation, according to lane turning Import direction Grouping calculations are performed to generate intersection approach turning flow datasets for each approach-turn combination:

[0077] .

[0078] Total inflow from each import direction during the statistical period .

[0079] The percentage of each turning flow relative to the total inlet flow is used to obtain intersection turning data:

[0080] .

[0081] S104 generates road network simulation data based on the simulated road network basic data, traffic data, and traffic parameters of the virtual traffic distribution center, performs traffic simulation, and obtains traffic operation indicators for the simulated area.

[0082] The traffic data is standardized hourly traffic.

[0083] In some embodiments of this application, the specific process of generating road network simulation data based on simulated road network basic data, traffic flow data, and traffic parameters of virtual traffic hubs, and executing traffic simulation to obtain traffic operation indicators for the simulation area includes: compiling the simulated road network basic data, standardized hourly traffic flow data, and traffic parameters of virtual traffic hubs into a complete SUMO simulation project configuration file to characterize the road network simulation data; running the simulation on the SUMO simulation project configuration file using SUMO simulation commands to simulate the vehicle's operating behavior in the road network, and recording relevant process and result data to obtain a result file; reading and parsing the result file output by the simulation engine through a preset post-processing program; based on the result file, statistically summarizing the travel data of all vehicles in the simulation area, calculating the custom aggregated evaluation indicators for the simulation area, which at least include travel delay rate, average delay time, and average parking loss indicators; assigning unique sequence indices to the calculated custom aggregated evaluation indicators, and encapsulating them into a structured indicator array according to a preset data structure to generate traffic operation indicators for the simulation area.

[0084] For example, first, based on the steps described above, generate the basic simulation road network data Simulation_net, standardized hourly traffic flow data pcuh_lane_flow, and configuration parameters for the virtual traffic distribution center. These configuration parameters are obtained by updating the intersection turning data percentage_flow. Then, generate the complete SUMO simulation project configuration file project.sumocfg. Next, run the simulation using the SUMO simulation command sumo -c project.sumocfg --tripinfo-output tripinfo.txt --summary-output summary.txt. Finally, use a pre-defined post-processing program (Python program) to obtain the trip-level performance index tripinfo for each simulated vehicle and generate an index array. .

[0085] In one possible implementation, the simulation result indicator "tripinfo" is obtained through a Python program, and an array of indicators is generated. . It is the trip delay rate, It is the average delay time. It is an average parking loss indicator.

[0086] S105: Dynamically adjust the insertion location parameters of the virtual traffic distribution center, recursively update the road network simulation data, execute traffic simulation, and obtain the updated traffic operation indicators of the simulation area. S106 utilizes a multi-index decision evaluation algorithm to comprehensively evaluate the traffic operation indicators of each road network simulation data and select the optimal road network simulation data.

[0087] In one possible implementation, the OFFSET value is modified, where This involves adjusting the insertion location parameters of virtual traffic hubs. Each adjustment updates the location of a traffic hub, correspondingly updating the road network simulation base data and the traffic configuration parameters. This process is repeated recursively to update the road network simulation data. Based on the resulting road network simulation data, traffic simulations are performed to obtain updated traffic operation indicators for the simulation area. In other words, by repeatedly adjusting the insertion location parameters of virtual traffic hubs, multiple sets of road network simulation data are obtained, and multiple simulations are performed to obtain multiple sets of traffic operation indicators for the simulation area.

[0088] In one possible implementation, the TOPSIS algorithm is used to evaluate the tripinfo traffic operation index for each road network simulation data, selecting the optimal road network simulation data. First, the tripinfo is processed to achieve a similar trend. Then, the data is normalized by sum of squares to adjust its dimensions. Next, the distance between the positive ideal solution (D+) and the negative ideal solution (D-) of each simulation object is used to calculate the proximity program C value, generating the C index. And sort them to obtain the optimal road network simulation data. .

[0089] In one possible implementation, the TOPSIS algorithm is used to evaluate the simulation index tripinfo and select the optimal simulation scheme. Understandably, the optimal simulation scheme is the road network simulation data corresponding to the optimal traffic operation index.

[0090] For example, the indicator data can be obtained by successively adjusting the offset to 1, 2, 3, and 4. Generate C pointer To obtain the optimal simulation scheme =1.

[0091] In this embodiment, road surface cameras are used to collect original vehicle traffic records, eliminating the need to install positioning devices in vehicles. This overcomes the limitations of personal privacy and vehicle usage, while avoiding the loss of algorithm accuracy caused by sampling data or coefficient supplementation rules, thus providing a real and reliable data source for traffic simulation.

[0092] In this embodiment, the traffic flow configuration of the road network can be generated by statistical analysis of lane-level traffic flow, reducing the time complexity to a constant order of O(1), which significantly improves the computational efficiency of regional traffic simulation and breaks through the performance bottleneck of large-scale road network simulation.

[0093] In this embodiment, key inflow and outflow points of traffic flow are generated by calculating traffic flow aggregation and dispersion points, and the traffic flow of the road network is scientifically allocated, providing accurate guidance data for vehicle simulation and improving the realism and reliability of the simulation results.

[0094] In this embodiment, an index evaluation algorithm is used to comprehensively evaluate the simulation results, and the simulation road network is automatically optimized based on the evaluation results to generate the simulation scheme that best matches the physical world, thus forming a closed-loop simulation system of "collection-analysis-allocation-evaluation-optimization".

[0095] In this embodiment, on the one hand, by acquiring the original vehicle passage records of intersection lanes and generating a standardized hourly traffic flow dataset at the lane level, the reliance on single-vehicle trajectory data is successfully bypassed. This provides a core input for simulation that is both legal and compliant, and reflects the real traffic flow distribution, effectively solving the simulation distortion problem caused by unavailable data. On the other hand, the traffic flow difference between directed road segments at adjacent intersections is calculated, and virtual traffic distribution points are dynamically inserted for compensation. The traffic flow difference directly originates from the actual observed cross-sectional traffic flow, accurately reflecting the traffic volume that disappears or is generated out of thin air in the road network. The virtual distribution point acts as a corrector, automatically filling the gap between this observed value and the model, enabling the simulation system to self-adjust to match the actual observed traffic flow, avoiding systematic bias caused by inaccurate algorithm estimation, and significantly improving the fidelity of the simulation. Furthermore, the time complexity of comparing traffic flow differences and parameterizing the distribution point is linearly related to the number of road segments, greatly reducing the complexity and computational cost of model construction, making it possible to perform efficient and automated simulation modeling and iterative optimization of large-scale, complex regional road networks.

[0096] The following are embodiments of the apparatus of this application, which can be used to execute the embodiments of the method of this application. For details not disclosed in the embodiments of the apparatus of this application, please refer to the embodiments of the method of this application.

[0097] Please see Figure 5 This illustration shows a schematic diagram of a traffic area road network simulation analysis device provided in an exemplary embodiment of this application. This traffic area road network simulation analysis device can be implemented as all or part of an electronic device through software, hardware, or a combination of both. The device 1 includes a simulated road network basic data construction module 10, a standardized hourly traffic flow dataset acquisition module 20, a traffic flow difference calculation module 30, a traffic flow parameter configuration module 40, a simulation module 50, an update module 60, and a comprehensive evaluation module 70.

[0098] The simulation road network basic data construction module 10 is used to construct simulation road network basic data, including the simulation road network and the timing data of each intersection, based on the physical road network data of the target urban area. The standardized hourly traffic data acquisition module 20 is used to acquire the original vehicle passage records of each lane at each intersection, and generate a lane-level standardized hourly traffic data set based on the original vehicle passage records. The flow difference calculation module 30 is used to calculate the flow difference between directed road segments between adjacent intersections based on a standardized hourly flow dataset. The flow difference is the difference between the outflow flow at the starting intersection of the road segment and the inflow flow at the ending intersection of the road segment. The flow parameter configuration module 40 is used to dynamically insert a virtual flow distribution point in the simulation path corresponding to the directed road segment in response to a flow difference that is not equal to zero, and configure the flow parameters of the virtual flow distribution point according to the flow difference. The simulation module 50 is used to generate road network simulation data based on the basic data of the simulated road network, standardized hourly traffic data and traffic parameters of the virtual traffic distribution center, perform traffic simulation, and obtain traffic operation indicators of the simulated area. The update module 60 is used to dynamically adjust the insertion location parameters of the virtual traffic distribution center, recursively update the road network simulation data, perform traffic simulation, and obtain the updated traffic operation indicators of the simulation area. The comprehensive evaluation module 70 is used to comprehensively evaluate the traffic operation indicators of each road network simulation data using a multi-index decision evaluation algorithm, and select the optimal road network simulation data.

[0099] It should be noted that the traffic area road network simulation analysis device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the traffic area road network simulation analysis method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the traffic area road network simulation analysis device and the traffic area road network simulation analysis method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0100] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0101] In this embodiment, on the one hand, by acquiring the original vehicle passage records of intersection lanes and generating a standardized hourly traffic flow dataset at the lane level, the reliance on single-vehicle trajectory data is successfully bypassed. This provides a core input for simulation that is both legal and compliant, and reflects the real traffic flow distribution, effectively solving the simulation distortion problem caused by unavailable data. On the other hand, the traffic flow difference between directed road segments at adjacent intersections is calculated, and virtual traffic distribution points are dynamically inserted for compensation. The traffic flow difference directly originates from the actual observed cross-sectional traffic flow, accurately reflecting the traffic volume that disappears or is generated out of thin air in the road network. The virtual distribution point acts as a corrector, automatically filling the gap between this observed value and the model, enabling the simulation system to self-adjust to match the actual observed traffic flow, avoiding systematic bias caused by inaccurate algorithm estimation, and significantly improving the fidelity of the simulation. Furthermore, the time complexity of comparing traffic flow differences and parameterizing the distribution point is linearly related to the number of road segments, greatly reducing the complexity and computational cost of model construction, making it possible to perform efficient and automated simulation modeling and iterative optimization of large-scale, complex regional road networks.

[0102] This application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the traffic area road network simulation analysis method provided in the above-described method embodiments.

[0103] This application also provides a computer program product containing instructions that, when run on a computer, causes the computer to execute the traffic area road network simulation analysis method of the above-described method embodiments.

[0104] Please see Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0105] The communication bus 1002 is used to realize the connection and communication between these components.

[0106] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0107] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0108] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the electronic device 1000 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles operating devices, user interfaces, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, not integrated into the processor 1001.

[0109] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing the operating device, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 6 As shown, the memory 1005, which serves as a computer storage medium, may include an operating device, a network communication module, a user interface module, and a traffic area road network simulation analysis application.

[0110] exist Figure 6In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 1001 can be used to call the traffic area road network simulation analysis application stored in the memory 1005 and specifically perform the following operations: Based on the physical road network data of the target city area, construct the basic data of the simulated road network, which includes the simulated road network and the timing data of each intersection; Calculate the flow difference between directional road segments between adjacent intersections. The flow difference is the difference between the outflow from the starting intersection of the road segment and the inflow from the ending intersection of the road segment. In response to the traffic difference not being equal to zero, a virtual traffic hub is dynamically inserted into the simulation path corresponding to the directed road segment, and the traffic parameters of the virtual traffic hub are configured according to the traffic difference. Based on the simulated road network basic data, traffic data, and traffic parameters of the virtual traffic distribution center, traffic simulation is performed to obtain traffic operation indicators for the simulated area. The insertion location parameters of the virtual traffic distribution center are dynamically adjusted, the road network simulation data is recursively updated, traffic simulation is performed, and the updated traffic operation indicators of the simulation area are obtained. A multi-index decision evaluation algorithm is used to comprehensively evaluate the traffic operation indicators of each road network simulation data, and the optimal road network simulation data is selected.

[0111] In one embodiment, when the processor 1001 executes the basic data of the simulated road network, which includes the simulated road network and the timing data of each intersection, based on the physical road network data of the target urban area, it specifically performs the following operations: Encapsulate the various entrance directions, exit directions, and their included lanes at each intersection, as well as the turning attributes of each lane, to obtain the intersection channelization data for each intersection; Based on the intersection channelization data for each intersection, generate upstream and downstream relationship data for the intersection; By using the upstream and downstream relationship data of intersections, simulated road segments are created between each pair of intersections with direct connections, resulting in upstream and downstream road segments that include upstream and downstream intersections and multiple simulated road segments. Merge the upstream and downstream road segments of each intersection to obtain a simulated road network file; Based on the intersection channelization data for each intersection, the lane connection directions and signal timings are configured to obtain the timing data for each intersection. The simulated road network file and the timing data of each intersection are combined to form the basic data of the simulated road network.

[0112] In one embodiment, when the processor 1001 calculates the traffic flow difference between directed road segments between adjacent intersections, it specifically performs the following operations: Obtain the original vehicle passage records for each lane at each intersection; The original vehicle passage records are divided into non-overlapping time slices using a preset time interval. The data from each time slice is summed to calculate the number of vehicles passing through each time slice, which is then used as the cross-sectional flow data set for lane-level preset time intervals. The cross-sectional flow data set at preset time intervals at the lane level is converted into a target data set at preset units, which serves as a standardized hourly flow data set at the lane level. The standardized hourly flow data set records the standardized hourly flow for each lane and each turn within a specific time period. Based on the standardized hourly traffic data set, the traffic flow difference between directed road segments at adjacent intersections is calculated.

[0113] In one embodiment, when processor 1001 calculates the traffic difference between directed road segments between adjacent intersections based on a standardized hourly traffic dataset, it specifically performs the following operations: In the road network, any two road segments between intersections that have a direct connection are selected as directed road segments between adjacent intersections, thus obtaining a set of directed road segments; Based on the standardized hourly traffic data set, calculate the inflow traffic to each directed road segment; Based on the standardized hourly traffic data set, calculate the outflow traffic of each directed road segment; The difference between the inflow and outflow of a road segment is calculated and used as the flow difference between directional road segments between adjacent intersections.

[0114] In one embodiment, when processor 1001 dynamically inserts a virtual traffic distribution center into the simulation path corresponding to a directed road segment in response to a non-zero traffic difference, it specifically performs the following operations: By analyzing the flow difference, target road segments where the inflow and outflow flows are not conserved can be identified; the target road segments include the starting intersection and the ending intersection. Based on the upstream and downstream road segments and directed road segments at the intersection, obtain all continuous simulated road segments connecting the starting intersection and the ending intersection; The total number of road segments included in all continuous simulation road segments is halved and rounded up, and then fine-tuned in combination with the preset index offset OFFSET to obtain the flow distribution points of road segments between intersections. Calculate the number of lanes in the flow distribution area between intersections, and based on the number of lanes, generate the inbound and outbound edges for each simulated road segment; The inbound and outbound edges of each simulated road segment are combined into a distribution point intersection, and the distribution point intersection is updated to the simulated road network file; the distribution point intersection is the physical carrier and specific implementation of the virtual traffic distribution point in the simulated road network.

[0115] In one embodiment, when processor 1001 executes the configuration of traffic parameters for a virtual traffic hub based on traffic differences, it specifically performs the following operations: Based on the magnitude of the flow difference, road segments are marked as either inflow or outflow patterns; Inflow or outflow pattern can be used as the functional type of virtual traffic distribution center; The turning ratio is obtained by calculating the ratio of the absolute value of the flow difference to the inflow flow at the starting intersection of the road segment. The road segment flow distribution type and turning ratio are encapsulated and updated into the intersection turning data to obtain the configuration parameters of the virtual flow distribution point.

[0116] In one embodiment, when the processor 1001 marks a road segment as either an inflow or outflow mode based on the magnitude of the flow difference, it specifically performs the following operations: When the flow difference is less than 0, if the observed outflow is greater than the inflow in a road segment with a flow difference less than 0, the function type of the road segment with a flow difference less than 0 is marked as inflow mode; or, When the flow difference is greater than or equal to 0, the outflow flow observed in the road segment with the flow difference greater than or equal to 0 is greater than or equal to the inflow flow, and the function type of the road segment with the flow difference greater than or equal to 0 is marked as outflow mode.

[0117] In one embodiment, when processor 1001 generates intersection turning data, it specifically performs the following operations: According to the preset aggregation dimensions, the standardized hourly traffic data set is subjected to grouping and summation to obtain the intersection approach turning traffic data set for each approach-turning combination; the preset aggregation dimensions include approach direction and lane turning. Based on the intersection inlet turning flow data set under each inlet-turn combination, sum up all turning flows in each inlet direction of each intersection to obtain the total inflow of each inlet direction during the statistical period. Calculate the ratio between the intersection inlet turning flow data set and its corresponding total inflow for each inlet-turn combination, and obtain the percentage of each turning flow in the total inlet flow as the intersection turning data.

[0118] In one embodiment, when the processor 1001 generates road network simulation data based on simulated road network basic data, traffic data, and traffic parameters from virtual traffic distribution centers, and performs traffic simulation to obtain traffic operation indicators for the simulated area, it specifically performs the following operations: The basic data of the simulated road network, the standardized hourly traffic flow data, and the traffic flow parameters of the virtual traffic distribution center are compiled into a complete SUMO simulation project configuration file to characterize the road network simulation data. Using the SUMO simulation command, the simulation is run on the SUMO simulation project configuration file to simulate the vehicle's behavior in the road network, and the relevant process and result data are recorded to obtain the result file; The system reads and parses the result files output by the simulation engine using a pre-defined post-processing program. Based on the results file, the travel data of all vehicles in the simulation area are statistically summarized, and the custom aggregated evaluation index of the simulation area is calculated. The custom aggregated evaluation index includes at least the travel delay rate, average delay time and average parking loss index. The calculated custom aggregated evaluation index is assigned a unique sequence index and encapsulated into a structured index array according to a preset data structure to generate traffic operation indicators for the simulation area.

[0119] In this embodiment, on the one hand, by acquiring the original vehicle passage records of intersection lanes and generating a standardized hourly traffic flow dataset at the lane level, the reliance on single-vehicle trajectory data is successfully bypassed. This provides a core input for simulation that is both legal and compliant, and reflects the real traffic flow distribution, effectively solving the simulation distortion problem caused by unavailable data. On the other hand, the traffic flow difference between directed road segments at adjacent intersections is calculated, and virtual traffic distribution points are dynamically inserted for compensation. The traffic flow difference directly originates from the actual observed cross-sectional traffic flow, accurately reflecting the traffic volume that disappears or is generated out of thin air in the road network. The virtual distribution point acts as a corrector, automatically filling the gap between this observed value and the model, enabling the simulation system to self-adjust to match the actual observed traffic flow, avoiding systematic bias caused by inaccurate algorithm estimation, and significantly improving the fidelity of the simulation. Furthermore, the time complexity of comparing traffic flow differences and parameterizing the distribution point is linearly related to the number of road segments, greatly reducing the complexity and computational cost of model construction, making it possible to perform efficient and automated simulation modeling and iterative optimization of large-scale, complex regional road networks.

[0120] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The traffic area road network simulation analysis program can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the traffic area road network simulation analysis program can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0121] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A traffic area road network simulation analysis method, characterized in that, The method includes: Based on the physical road network data of the target city area, construct the basic data of the simulated road network, which includes the simulated road network and the timing data of each intersection; Calculate the flow difference between directional road segments between adjacent intersections, where the flow difference is the difference between the outflow from the starting intersection of the road segment and the inflow from the ending intersection of the road segment; In response to the traffic difference not being equal to zero, a virtual traffic hub is dynamically inserted into the simulation path corresponding to the directed road segment, and the traffic parameters of the virtual traffic hub are configured according to the traffic difference. Based on the simulated road network basic data, traffic data, and traffic parameters of the virtual traffic distribution center, road network simulation data is generated, traffic simulation is performed, and traffic operation indicators of the simulated area are obtained. The insertion location parameters of the virtual traffic distribution center are dynamically adjusted, the road network simulation data is recursively updated, traffic simulation is performed, and the updated traffic operation indicators of the simulation area are obtained. A multi-index decision evaluation algorithm is used to comprehensively evaluate the traffic operation indicators of each road network simulation data, and the optimal road network simulation data is selected.

2. The method of claim 1, wherein, The physical road network data includes the various entrance directions and exit directions of each intersection, the lanes they contain, and the turning attributes of each lane; The process involves constructing basic simulated road network data, including the simulated road network and timing data for each intersection, based on the physical road network data of the target urban area. Encapsulate each entrance direction, exit direction, and lanes contained in each intersection, as well as the turning attributes of each lane, to obtain the intersection channelization data for each intersection; Based on the intersection channelization data for each intersection, generate upstream and downstream relationship data for the intersection; Using the upstream and downstream relationship data of the intersection, simulated road segments are created between each pair of intersections with direct connections, resulting in upstream and downstream road segments that include upstream and downstream intersections and multiple simulated road segments. Merge the upstream and downstream road segments of each intersection to obtain a simulated road network file; Based on the intersection channelization data of each intersection, the lane connection direction and signal timing of the intersection are configured to obtain the timing data of each intersection; The simulated road network file and the timing data of each intersection are combined to form the basic data of the simulated road network.

3. The method of claim 1, wherein, Calculate the traffic flow difference between directed road segments at adjacent intersections, including: Obtain the original vehicle passage records for each lane at each intersection; The original vehicle passage records are divided into non-overlapping time slices using a preset time interval. The data from each time slice is summed and statistically analyzed to determine the number of vehicles passing through each time slice, which is then used as a cross-sectional flow data set for a lane-level preset time interval. The cross-sectional flow data set of lane-level preset time intervals is converted into a target data set of preset units as a lane-level standardized hourly flow data set; the standardized hourly flow data set records the standardized hourly flow of each lane and each turn within a specific time period; Based on the standardized hourly traffic data set, the traffic flow difference between directed road segments at adjacent intersections is calculated.

4. The method of claim 3, wherein, The calculation of the traffic flow difference between directed road segments at adjacent intersections based on the standardized hourly traffic flow dataset includes: In the road network, any two road segments between intersections that have a direct connection are selected as directed road segments between adjacent intersections, thus obtaining a set of directed road segments; Based on the standardized hourly traffic data set, calculate the inflow traffic of each directed road segment; Based on the standardized hourly traffic data set, calculate the outflow traffic of each directed road segment; The flow difference between the inflow and outflow of the road segment is calculated and used as the flow difference between directional road segments between adjacent intersections.

5. The method of claim 1, wherein, The basic data of the simulated road network includes upstream and downstream road segments at intersections and the simulated road network file; there are any two directed road segments in the road network that have a direct connection relationship; In the simulation path corresponding to the directed road segment, a virtual traffic aggregation point is dynamically inserted, including: The target road segment is identified by the flow difference, which shows that the inflow and outflow flows are not conserved. The target road segment includes the starting intersection and the ending intersection. Based on the upstream and downstream road segments of the intersection and the directed road segments, obtain all continuous simulated road segments connecting the starting intersection and the ending intersection; The total number of road segments included in all the continuous simulated road segments is halved and rounded up, and then fine-tuned in combination with the preset index offset OFFSET to obtain the flow distribution points of road segments between intersections. Calculate the number of lanes in the flow distribution area between the intersections, and based on the number of lanes, generate the inlet and outlet edges for each simulated road segment; The inbound and outbound edges of each simulated road segment are combined into a distribution point intersection, and the distribution point intersection is updated to the simulated road network file; the distribution point intersection is the physical carrier and specific implementation of the virtual traffic distribution point in the simulated road network.

6. The method of claim 1, wherein, The step of configuring the traffic parameters of the virtual traffic distribution center based on the traffic difference includes: Based on the magnitude of the flow difference, the road segment is marked as either an inflow pattern or an outflow pattern; The inflow pattern or the outflow pattern shall be used as the functional type of the virtual traffic distribution center; The turning ratio is obtained by calculating the ratio of the absolute value of the flow difference to the inflow flow at the starting intersection of the road segment; The road segment flow distribution type and the turning ratio are encapsulated and updated into the intersection turning data to obtain the configuration parameters of the virtual flow distribution location.

7. The method of claim 6, wherein, The step of marking a road segment as either an inflow or outflow pattern based on the magnitude of the flow difference includes: When the flow difference is less than 0, it is determined that the observed outflow flow is greater than the inflow flow in the road segment where the flow difference is less than 0, and the function type of the road segment where the flow difference is less than 0 is marked as inflow mode; or, When the flow difference is greater than or equal to 0, it is determined that the observed outflow flow in the road segment with the flow difference greater than or equal to 0 is greater than or equal to the inflow flow, and the functional type of the road segment with the flow difference greater than or equal to 0 is marked as outflow mode.

8. The method of claim 6, wherein, To generate intersection turning data, follow these steps: According to a preset aggregation dimension, a grouping and summation operation is performed on the standardized hourly traffic data set to obtain the intersection approach turning traffic data set for each approach-turning combination; the preset aggregation dimension includes approach direction and lane turning. Based on the intersection inlet turning flow data set under each inlet-turn combination, sum all turning flows in each inlet direction of each intersection to obtain the total inflow of each inlet direction during the statistical period. Calculate the ratio between the intersection inlet turning flow data set and the corresponding total inflow for each inlet-turn combination to obtain the percentage of each turning flow in the total inlet flow, which is used as the intersection turning data.

9. The method according to claim 1, characterized in that, The traffic data is standardized hourly traffic. Based on the simulated road network basic data, traffic flow data, and traffic flow parameters of the virtual traffic distribution center, road network simulation data is generated, traffic simulation is performed, and traffic operation indicators of the simulation area are obtained, including: The simulated road network basic data, the standardized hourly traffic data, and the traffic parameters of the virtual traffic distribution center are compiled into a complete SUMO simulation project configuration file to characterize the road network simulation data. The SUMO simulation command is used to run the simulation on the SUMO simulation project configuration file to simulate the vehicle's operation behavior in the road network, and record the relevant process and result data to obtain the result file. The system reads and parses the result files output by the simulation engine using a pre-defined post-processing program. Based on the results file, the travel data of all vehicles in the simulation area are statistically summarized, and the custom aggregated evaluation index of the simulation area is calculated. The custom aggregated evaluation index includes at least the travel delay rate, average delay time and average parking loss index. The calculated custom aggregated evaluation index is assigned a unique sequence index and encapsulated into a structured index array according to a preset data structure to generate traffic operation indicators for the simulation area.

10. A traffic area road network simulation analysis device, characterized in that, The device includes: The simulated road network basic data construction module is used to construct simulated road network basic data, including the simulated road network and the timing data of each intersection, based on the physical road network data of the target urban area. The flow difference calculation module is used to calculate the flow difference between directional road segments between adjacent intersections. The flow difference is the difference between the outflow flow at the starting intersection of the road segment and the inflow flow at the ending intersection of the road segment. The traffic parameter configuration module is used to dynamically insert a virtual traffic distribution point into the simulation path corresponding to the directed road segment in response to the traffic difference not being equal to zero, and to configure the traffic parameters of the virtual traffic distribution point according to the traffic difference. The simulation module is used to generate road network simulation data, perform traffic simulation, and obtain traffic operation indicators of the simulation area based on the basic data of the simulated road network, traffic data, and traffic parameters of the virtual traffic distribution center. The update module is used to dynamically adjust the insertion location parameters of the virtual traffic distribution center, recursively update the road network simulation data, perform traffic simulation, and obtain the updated traffic operation indicators of the simulation area. The comprehensive evaluation module is used to comprehensively evaluate the traffic operation indicators of each road network simulation data using a multi-index decision evaluation algorithm, and select the optimal road network simulation data.

Images