A method for identifying arterial coordination mismatch sources based on traffic control error propagation constraints

Abstract

This invention relates to the field of intelligent traffic control technology and discloses a method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints. The method includes: acquiring vehicle passage data and signal timing data at arterial road intersections, and generating a spatiotemporal vehicle passage chain sequence; calculating time deviation based on timing data and outputting the intersection's operational status determination result; constructing a traffic control error propagation constraint model and performing logical judgment for intersections with abnormal status and adjacent upstream intersections to determine whether a propagation causal relationship exists between nodes; based on the judgment result, stripping affected cascading abnormal nodes to generate an independent abnormal intersection set; extracting the spatial sorting index of nodes within this set, performing a forward unidirectional search to obtain the initial mismatch source node, and blocking downstream probes. This invention directly pinpoints the initial physical source causing arterial road traffic coordination control mismatch by distinguishing between independently occurring abnormal nodes and affected nodes that cause abnormalities.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic control technology, specifically to a method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints. Background Technology

[0002] Arterial road traffic signal coordination control guides convoys to pass through multiple intersections continuously by setting the phase difference of signals at adjacent intersections. In actual operation, when the actual arrival time of vehicles at an intersection deviates from the preset green wave time window, an arterial road coordination mismatch occurs. Due to the continuity of traffic flow, the control error generated at a single intersection will spread to adjacent intersections as convoys become blocked or dispersed, causing multiple intersections along the arterial road to exhibit cascading abnormal states at the same time.

[0003] Existing traffic condition assessment methods rely on local cross-sectional detection data of individual intersections to independently assess the operational status of each intersection. When faced with cascading anomalies on arterial roads, this independent assessment mechanism can only count the number and distribution of abnormal nodes, but cannot sort out the causal relationships between abnormal nodes. Due to the lack of quantitative constraints on the spread and evolution boundaries of traffic control errors in physical space, existing technologies cannot distinguish whether the abnormality of a certain intersection is independent due to its own unreasonable control parameters, or a subordinate phenomenon caused by the propagation of errors from adjacent upstream nodes. This makes it difficult for the system to isolate the cascading invalid interference nodes from the complex sequence of abnormal nodes when diagnosing arterial road coordination problems, and it is impossible to directly pinpoint the initial physical location that caused the system failure, resulting in a lack of accurate spatial orientation in subsequent signal control optimization strategies. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints. This method solves the problem in existing arterial road traffic control assessments that it is difficult to distinguish between independent abnormal nodes and affected cascaded abnormal nodes, which leads to the inability to accurately pinpoint the initial source of signal coordination mismatch.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention provides a method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints, comprising the following steps:

[0007] Discrete vehicle passage data and traffic signal timing data at each intersection of the target arterial road are obtained and analyzed and preprocessed to form a standard input dataset;

[0008] Extract the unique vehicle identifier field from the standard input dataset, associate vehicle passage records, remove invalid chains, and generate a set of valid vehicle spatiotemporal passage chain sequences;

[0009] The ideal green wave time window is calculated based on traffic signal timing data. The time deviation is calculated by combining the effective vehicle spatiotemporal data with the chain sequence set, generating coordinated operation status characteristics and outputting the status judgment result.

[0010] For the target intersection whose state determination result is abnormal and the adjacent upstream intersection of the target intersection, a traffic control error propagation constraint model is constructed;

[0011] Based on the traffic control error propagation constraint model, the logic is used to determine whether the target intersection and the adjacent upstream intersection have a propagation causal relationship or are independent of each other.

[0012] Based on the logical judgment results, cascaded abnormal nodes are stripped, nodes that are not affected and are in abnormal state are extracted, and an independent set of abnormal intersections is generated.

[0013] Extract the spatial sort index of nodes from the set of independent abnormal intersections, perform a one-way search according to the forward driving topology to obtain the initial mismatch source node, terminate the downstream probing and output the diagnostic results.

[0014] This invention constructs a constraint model that includes the physical characteristics of spatial and traffic flow evolution, quantifies the causal relationship of abnormal states at upstream and downstream intersections, investigates the propagation path based on logical judgment and blocks cascading waves and misjudgments, and locks the initial physical source of traffic control mismatch according to the spatial topology.

[0015] Furthermore, the steps for obtaining discrete vehicle passage data and traffic signal timing data at each intersection of the target arterial road, and performing analytical preprocessing to form a standard input dataset include:

[0016] Discrete vehicle passage data is acquired and an intersection topology sequence is constructed. The discrete vehicle passage data is then parsed and extracted into a vehicle passage record vector containing a unique vehicle identifier, an intersection physical topology sort number, an absolute timestamp record, and a driving direction code.

[0017] The total number of signal cycles is calculated based on the length of the common signal cycle, and a time axis sequence of signal cycles arranged in absolute time order is constructed based on the start and end times of the green light.

[0018] Distorted discrete data with missing unique identifier fields, recognition confidence below the lower threshold, excessive absolute timestamps, or reversed driving direction are discarded and cleaned. The absolute timestamps of the remaining valid vehicle passage records are time-scale aligned and stored in the database to form the standard input dataset.

[0019] Furthermore, the steps of extracting the vehicle unique identifier field from the standard input dataset, associating vehicle passage records to remove invalid chains, and generating a set of valid vehicle spatiotemporal passage chain sequences include:

[0020] For the standard input dataset, multiple discrete vehicle passage records with the same unique vehicle identifier are aggregated, and then reassembled in ascending order according to the absolute timestamp, and mapped into a single vehicle spatiotemporal passage chain sequence.

[0021] Calculate the total number of physical nodes contained in the spatiotemporal passage chain sequence of a single vehicle. If the total number of physical nodes is less than the set lower limit threshold for trajectory validity, mark the spatiotemporal passage chain sequence of a single vehicle as a spatial broken chain sequence.

[0022] Calculate the actual absolute timestamp difference between adjacent nodes. If the actual absolute timestamp difference exceeds the theoretical maximum passage time threshold calculated by dividing the physical space distance by the minimum speed limit, mark the single vehicle spatiotemporal sequence as a time-series broken chain sequence.

[0023] Discard vehicle spatiotemporal passage chain sequences with spatial or temporal chain break markers directly, and archive and merge vehicle spatiotemporal passage chain sequences that have not triggered chain break markers to generate a set of valid vehicle spatiotemporal passage chain sequences.

[0024] In a preferred embodiment of the present invention, the step of calculating the ideal green wave time window based on traffic signal timing data and calculating the time deviation by combining the effective vehicle spatiotemporal passage chain sequence set includes:

[0025] The theoretical time consumption is calculated by dividing the green light start time of the first intersection of the arterial road, the physical distance between intersections, and the coordinated design speed of the arterial road, and by performing an algebraic superposition operation on the absolute phase difference between the target intersection and the first intersection to generate the ideal green wave time window within the corresponding signal cycle.

[0026] When the absolute timestamp of the actual arrival of a single vehicle falls within the ideal green wave time window, the vehicle's trajectory is determined to conform to the traffic signal coordination and control strategy, and the time deviation is assigned a value of 0.

[0027] When the actual arrival absolute timestamp of a bicycle is earlier than the start time of the ideal green wave time window or later than the end time of the ideal green wave time window, the algebraic difference between the actual arrival absolute timestamp and the corresponding start and end times of the ideal green wave time window is calculated, and a positive or negative sign is assigned according to the physical state of the bicycle arriving early or late. The algebraic difference with positive or negative signs is used as the bicycle's time deviation.

[0028] Furthermore, the steps for generating coordinated operational state characteristics and outputting state determination results include:

[0029] The arithmetic mean of the time deviation of all vehicles within the current signal cycle is calculated to generate a coordinated operation status feature that represents the overall macroscopic operation status of the traffic flow at the target intersection.

[0030] When the total number of valid vehicles in the current signal cycle is detected to be 0, the bypass protection verification logic is triggered, and the coordinated operation status feature is forcibly assigned to 0 or the historical value of the coordinated operation status feature of the previous cycle is used.

[0031] The preset state determination threshold is invoked, and Boolean algebra mapping is performed on the absolute value of the coordinated operation state characteristics. The discrete mapping outputs a binarized state determination result.

[0032] Furthermore, for the target intersection with an abnormal state determination result and its adjacent upstream intersection, the steps for constructing a traffic control error propagation constraint model include:

[0033] Extract the topological parameters of the target intersection and the candidate upstream intersection to perform initial spatial state verification, and map the output spatial continuous constraint sub-items;

[0034] The binarized state determination results of the target intersection and the candidate upstream intersection within the same signal period are directly merged, skipping the intermediate state independent comparison step, and mapping the output state continuous constraint sub-items.

[0035] Extract the coordinated operation status features of the target intersection and the candidate upstream intersection, and determine the direction consistency constraint sub-item based on the algebraic sign homogeneity of the coordinated operation status features of the target intersection and the candidate upstream intersection.

[0036] The empirical constant parameters characterizing the discrete characteristics of the vehicle fleet and the free-flow vehicle speed of the road segment are substituted into the fluid dynamics attenuation law to generate the theoretical attenuation boundary. The absolute value of the theoretical attenuation boundary and the coordinated operation state characteristics of the target intersection are numerically compared to output the propagation evolution constraint sub-item.

[0037] In a preferred embodiment of the present invention, the step of substituting the empirical constant parameter characterizing the discrete characteristics of the vehicle fleet and the free-flow vehicle speed of the road segment into the hydrodynamic attenuation law to generate the theoretical attenuation boundary includes:

[0038] The absolute value of the coordinated operation status characteristics of the candidate upstream intersection is used as the calculation benchmark. The theoretical attenuation boundary is generated by combining the natural logarithm base and the empirical constant parameter characterizing the discrete characteristics of the vehicle fleet. The intermediate transmission dissipation calculation nodes are discarded and the propagation evolution constraint judgment result is directly output.

[0039] Furthermore, based on the traffic control error propagation constraint model, the steps to determine whether the target intersection and its adjacent upstream intersection have a propagation causal relationship or are independent include:

[0040] A Boolean product operation is performed on the comprehensive evaluation matrix, which includes spatial continuity constraint items, state continuity constraint items, orientation consistency constraint items, and propagation evolution constraint items, to generate a comprehensive propagation judgment index.

[0041] When the value of the comprehensive propagation judgment index is 1, it is determined that there is a physical propagation behavior of traffic control error. When the value of the comprehensive propagation judgment index is 0, it is determined to be an isolated event and the propagation association is blocked.

[0042] The target intersection with a comprehensive propagation judgment index of 1 and the candidate upstream intersection are mapped into connected vertices in a directed graph. The abnormal state node sequence is extracted along the topological connected edges of the directed graph to generate the arterial road traffic control error propagation chain.

[0043] Furthermore, the steps of separating cascading abnormal nodes based on the logical judgment results, extracting unaffected nodes with abnormal states, and generating an independent set of abnormal intersections include:

[0044] In memory, the node status attribute table is initialized with cascade waves, flag bits, and independent anomaly flag bits for the intersection nodes in the main road forward topology sequence.

[0045] When the target intersection node is the first node in the intersection topology sequence, the independent anomaly flag of the target intersection node is set to an active position.

[0046] When the target intersection node is not the first node and the value of the comprehensive propagation judgment index is equal to the logical truth value 1, the cascade wave and flag of the target intersection node are set, and the target intersection node is logically removed from the potential source candidate queue.

[0047] When the value of the comprehensive propagation judgment index is equal to the logical false value of 0, the independent anomaly flag of the target intersection node will be set to the bit position.

[0048] Filter all cascaded waves and subordinate propagation nodes where the flag is set, package and encapsulate nodes where the independent anomaly flag is set, and dynamically construct and generate a set of independent anomaly intersections.

[0049] Furthermore, the steps of extracting the spatial sorting index of nodes in the set of independent abnormal intersections, performing a one-way search according to the forward driving topology to obtain the initial mismatch source node, terminating downstream probing, and outputting the diagnostic results include:

[0050] The physical space sorting index of each independent abnormal intersection node in the set of independent abnormal intersections is extracted and an ascending order reorganization mapping is performed to construct a one-dimensional topological retrieval sequence that follows the physical evolution direction of traffic flow;

[0051] A one-way pointer traversal operation is initiated on the one-dimensional topological search sequence to capture the first physical node with the smallest physical space sorting index value and officially confirm it as the initial mismatch source node.

[0052] Trigger the retrieval circuit breaker mechanism to block the source detection test operation of the remaining downstream candidate nodes in the one-dimensional topology retrieval sequence by sending a forced termination command;

[0053] The physical correlation and topology path starting from the initial mismatch source node are extracted and reconstructed into a diagnostic data structure file.

[0054] This invention provides a method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints. It has the following advantages:

[0055] 1. This invention extracts unique vehicle identifiers by associating discrete vehicle passage data, eliminates broken chain records to generate a valid spatiotemporal vehicle passage chain sequence, and calculates time deviation by combining signal timing data to generate coordinated operation status characteristics; it uses the matching relationship between continuous driving trajectory and signal time window to quantify the traffic process, replacing traffic flow statistics of a single cross section, and provides an objective data analysis benchmark for determining whether the coordination status of an intersection is abnormal.

[0056] 2. This invention constructs an error propagation constraint model for abnormal intersections and their adjacent upstream intersections, integrating constraint sub-items such as spatial topology, operating status, error direction, and propagation evolution based on the hydrodynamic attenuation law; by performing Boolean operations on the comprehensive evaluation matrix to generate a comprehensive propagation judgment index, this scheme directly calculates whether there is a causal relationship of control error propagation between upstream and downstream intersections, thereby logically distinguishing between independently occurring abnormal nodes and cascaded abnormal nodes caused by upstream influence.

[0057] 3. This invention separates cascaded abnormal nodes based on propagation judgment indicators, encapsulates them to generate an independent abnormal intersection set, and extracts the physical spatial sorting index of the nodes in the set to construct a one-way topology retrieval sequence; in conjunction with one-way pointer traversal and retrieval circuit breaker instructions, after confirming the first physical node with the smallest spatial sorting value, it immediately blocks the probing calculation of the remaining downstream nodes, directly locks the initial physical source that causes trunk mismatch, and avoids redundant detection calculations of subordinate propagation nodes. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the system module architecture and data interaction of the present invention;

[0059] Figure 2 This is a schematic diagram of the overall process of the arterial coordination mismatch source identification method of the present invention;

[0060] Figure 3 This is a schematic diagram of the logic determination of the traffic control error propagation constraint model of the present invention;

[0061] Figure 4 This is a schematic diagram illustrating the comprehensive propagation index determination and error physical source tracing of the present invention;

[0062] Figure 5 This is a schematic diagram illustrating the spatiotemporal evolution of complex road network error cascading stripping and mismatch source localization according to the present invention.

[0063] Figure 6 This is a schematic diagram of the one-dimensional topological retrieval and logical retrieval circuit breaker isolation space mapping of the present invention;

[0064] Figure 7 This is a schematic diagram comparing the evolution of the single-vehicle spatiotemporal chain and the ideal green wave time window in this invention;

[0065] Figure 8 This is a schematic diagram comparing the evaluation indicators of different identification methods of the present invention. Detailed Implementation

[0066] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] See attached document Figure 1 This invention provides a system for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints. The system includes: a data acquisition module, a data processing module, a propagation analysis module, and a result output module.

[0068] The data acquisition module is connected to urban traffic field detection equipment and traffic signal control platform. It is configured to collect vehicle passage records at each intersection of the target arterial road and the corresponding signal cycle timing parameters of each intersection, providing basic data input for the arterial road coordination mismatch source identification method based on traffic control error propagation constraints.

[0069] The data processing module is connected to the data acquisition module and is configured to receive vehicle passage records and signal period timing parameters, perform format standardization and alignment operations, associate discrete vehicle passing nodes based on the vehicle's unique identifier, and construct a single vehicle spatiotemporal passage chain sequence.

[0070] The propagation analysis module is connected to the data processing module and configured to receive the spatiotemporal passage chain sequence of a single vehicle, quantify and analyze the traffic operation parameters at the intersection level and generate characteristic quantification results for the corresponding period, establish logical constraint equations based on spatial connectivity, state inheritance, directional convergence and numerical evolution laws, and determine the causal relationship of error propagation between adjacent intersections.

[0071] The results output module is connected to the propagation analysis module and is configured to receive causal determination results, eliminate subordinate propagation nodes, execute a retrieval circuit breaker mechanism based on the forward topology graph to locate the initial mismatch source node, and generate a diagnostic data structure file.

[0072] See attached document Figure 2 This invention provides a method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints, comprising the following steps:

[0073] S1, the data acquisition module acquires discrete vehicle passage data and traffic signal timing data corresponding to the detection sections of each intersection of the target arterial road. The data processing module performs field parsing and noise reduction preprocessing on the discrete vehicle passage data and traffic signal timing data to form a standard input dataset.

[0074] S2, within the set statistical time window, the data processing module extracts the vehicle unique identifier field from the standard input dataset, associates the vehicle passage records of the same vehicle at multiple sections and arranges them in ascending order by timestamp, applies data cleaning rules to remove invalid chains, and generates a set of valid vehicle spatiotemporal passage chain sequences.

[0075] S3, the propagation analysis module calculates the ideal green wave time window of the signal cycle of the target intersection based on traffic signal timing data, compares the absolute timestamps of vehicles crossing the road section where the stop line is located in the effective vehicle spatiotemporal passage chain sequence set, calculates the time deviation of each vehicle, summarizes and generates the coordinated operation status characteristics at the intersection level, and outputs the binarized status judgment result in combination with the preset threshold.

[0076] S4, the propagation analysis module constructs a traffic control error propagation constraint model for the target intersection whose state determination result is abnormal and its directly adjacent upstream intersection. The traffic control error propagation constraint model includes spatial continuity constraint items, state continuity constraint items, direction consistency constraint items, and propagation evolution constraint items.

[0077] S5, the propagation analysis module, based on the traffic control error propagation constraint model, executes Boolean logic decision equations for the target intersection and its directly adjacent upstream intersection to determine whether there is a propagation causal relationship or mutual independence between the target intersection and its directly adjacent upstream intersection;

[0078] S6, The result output module, based on the Boolean logic judgment result, separates the cascaded abnormal nodes that are determined to be affected by the upstream error propagation, extracts the nodes that are not affected by the upstream error propagation and whose own state is determined to be abnormal, and generates an independent abnormal intersection set.

[0079] S7, the result output module extracts the spatial sorting index of nodes in the set of independent abnormal intersections, performs a one-way search according to the vehicle forward driving topology sequence, obtains the first spatial sorting node as the initial mismatch source of arterial coordinated control, and simultaneously terminates the source detection test of downstream subsequent nodes, and outputs the diagnostic results including the mismatch source intersection and the associated affected path.

[0080] In this embodiment, step S1 provided by the present invention may include the following steps in specific implementation:

[0081] S11, the data acquisition module accesses the urban traffic field detection equipment through the communication network and calls the existing electronic police equipment or security checkpoint system to collect discrete vehicle data of each intersection on the forward topology sequence of the target arterial road. The data acquisition module performs logical abstraction of the multi-node physical topology structure and constructs an intersection topology sequence containing multiple physical intersection entities sorted from beginning to end in a forward topology order. The total number of nodes in the intersection topology sequence depends on the number of intersections in the actual arterial road that are controlled by the same signal control strategy.

[0082] The data acquisition module further parses and refines the discrete vehicle passage data into a structured vehicle passage record vector. The vehicle passage record vector includes the vehicle's unique identifier, the intersection's physical topology sort number, the absolute timestamp record of the road section where the vehicle crosses the stop line, the physical lane number where the vehicle is located when the vehicle passage event occurs, and the vehicle's driving direction code generated by the data acquisition module based on the vehicle's driving vector coordinates. The vehicle's unique identifier is represented by a Chinese string of the license plate number or a feature hash value extracted based on the vehicle's physical outline. The vehicle capture mechanism and data transmission protocol of the urban traffic field detection equipment are known technologies, and this embodiment directly calls the output interface of the urban traffic field detection equipment to extract discrete vehicle passage data.

[0083] S12, the data acquisition module synchronously connects to the traffic signal control platform and acquires the traffic signal timing data of each intersection on the main road within the same statistical time window. The parameter set extracted by the data acquisition module includes the length of the common signal cycle, the green light ratio of the target coordinated phase, the absolute phase difference between adjacent intersections, and the start and end times of the green light in each operating cycle of each intersection.

[0084] The data acquisition module calculates the total number of signal cycles by dividing the total duration of the statistical time window by the length of the common signal cycle on the main road. Based on the total number of signal cycles, the data acquisition module constructs a time axis sequence of signal cycles arranged in absolute time order. The data acquisition module reconstructs the ideal operating benchmark for the coordination of green waves on the main road based on parameters such as the extracted common signal cycle length and the start and end times of the green light. The ideal operating benchmark forms a static data scale for judging the deviation of the actual travel trajectory of vehicles.

[0085] S13, the data processing module receives vehicle passage record vectors and traffic signal timing data and performs underlying field parsing and noise reduction preprocessing operations. The data processing module traverses the vehicle passage record vectors line by line and performs multi-dimensional rule comparison based on preset boundary parameters. When the logic determines that the vehicle unique identification field is missing or the character recognition confidence is lower than the lower limit threshold of 90% or the absolute timestamp value exceeds the range of the legal statistical time window or the physical driving direction indicated by the driving direction code is opposite to the coordinated control direction;

[0086] The data processing module determines that the corresponding vehicle passage record vector is distorted discrete data. The data processing module marks the distorted discrete data as invalid noise data and directly discards and cleans it from the buffer of the arterial coordination mismatch source identification system based on traffic control error propagation constraints. After removing invalid noise data, the data processing module extracts the absolute timestamp of the remaining valid vehicle passage records and the start and end times of the green light in the signal timing data.

[0087] The data processing module maps various heterogeneous time points into millisecond-level standard timestamp format based on a unified network time synchronization protocol and stores them in the database of the arterial coordination mismatch source identification system based on traffic control error propagation constraints to form a standard input dataset. The time scale alignment operation eliminates time reference misalignment caused by clock drift inside the sensing device or network delay, and the data processing module ensures that the trajectory association operation logic is executed under a globally unified time axis reference based on the time scale alignment operation.

[0088] In this embodiment, step S2 provided by the present invention may include the following steps in specific implementation:

[0089] S21, the data processing module reads the standard input dataset and traverses all structured vehicle passage record vectors. The data processing module extracts the vehicle unique identifier field from the records and performs a global hash matching grouping operation. The data processing module aggregates multiple discrete vehicle passage records with the same vehicle unique identifier to form a single-vehicle exclusive passage record set. The data processing module further extracts the absolute timestamp field of each passage record in the single-vehicle exclusive passage record set and reorganizes them in ascending order according to the built-in sorting algorithm. The data processing module maps the reorganized passage records into a single-vehicle spatiotemporal passage chain sequence composed of the intersection node sequence and the corresponding vehicle passage timestamp according to the physical spatial topological order.

[0090] The data processing module generates a single-vehicle spatiotemporal passage chain sequence for any valid passing vehicle that passes through the target arterial road within a set statistical time window, based on an association sorting operation. The continuous driving process of the vehicles spatially exhibits the characteristic of sequential crossing of discrete intersection nodes and temporally exhibits a monotonically increasing characteristic of absolute timestamps. Based on the physical laws governing the continuous driving process of the vehicles, the data processing module establishes a mathematical expression model for the single-vehicle spatiotemporal passage chain sequence, and the mathematical expression model satisfies the following formula:

[0091] ;

[0092] In the formula, For the first The effective vehicle time-space transit chain sequence within each signal period; This is the index number of the signal period; This is the physical number of the intersection, where Sort the index for nodes; For the vehicle in the Crossing the intersection within one signal cycle The absolute timestamp of the section where the stop line is located; This is the node index for the intersection; This represents the total number of nodes at the intersection.

[0093] S22, after generating the single-vehicle spatiotemporal transit chain sequence of all vehicles, the data processing module extracts the absolute timestamp difference between adjacent nodes and the node spatial topology number, and calls the built-in data cleaning rules to perform screening. The data processing module calls the topology integrity verification operator to calculate the total number of physical nodes contained in the single-vehicle spatiotemporal transit chain sequence and compares it with the set trajectory validity lower limit threshold.

[0094] In this embodiment, the lower limit threshold for trajectory validity is set to 3 in combination with the minimum number of associated nodes at the arterial road coordination intersection. When the total number of physical nodes in the spatiotemporal passage chain sequence of a single vehicle is less than the lower limit threshold for trajectory validity, the data processing module determines that the vehicle has engaged in spatial truncation behavior of entering or leaving the arterial road midway during actual driving and marks the spatiotemporal passage chain sequence of the single vehicle corresponding to the spatial truncation behavior as a spatial broken chain sequence.

[0095] The data processing module synchronously calls the passage time logic verification operator to extract the physical spatial distance between adjacent nodes and the minimum speed limit legally set by the main road management department, and divides the two to calculate the theoretical maximum passage time threshold. The data processing module then performs a numerical comparison between the extracted actual absolute timestamp difference between adjacent nodes and the theoretical maximum passage time threshold.

[0096] When the actual absolute timestamp difference exceeds the theoretical maximum passage time threshold, the data processing module determines that the vehicle has engaged in abnormal physical behavior such as temporary roadside parking or staying in off-street service areas. The module then marks the spatiotemporal sequence of the vehicle corresponding to the abnormal physical behavior as a time-series broken sequence. The data processing module completes the logical definition of the vehicle's trajectory in terms of spatial connectivity and temporal continuity through a dual verification operator.

[0097] S23, the data processing module performs a filtering and blocking action on all single-vehicle spatiotemporal passage chain sequences in the data processing module buffer area according to the output marking results of the data cleaning rules. The data processing module directly discards single-vehicle spatiotemporal passage chain sequences with spatial chain break marks or temporal chain break marks and does not send single-vehicle spatiotemporal passage chain sequences with spatial chain break marks or temporal chain break marks to subsequent analysis nodes.

[0098] The data processing module archives and merges single-vehicle spatiotemporal passage chain sequences that have not triggered any chain break markers and maintain spatiotemporal logical consistency, and generates a set of valid vehicle spatiotemporal passage chain sequences. The data processing module pushes the set of valid vehicle spatiotemporal passage chain sequences to the propagation analysis module through the internal data bus as the basic input source for quantifying the coordinated operation status characteristics of the arterial road. After the data truncation and discarding operation, the dataset entering the core algorithm layer retains only the physical characteristics of the continuous driving trajectory constrained by traffic signal timing.

[0099] In this embodiment, step S3 provided by the present invention may include the following steps in specific implementation:

[0100] S31, the propagation analysis module receives the set of effective vehicle spatiotemporal passage chain sequences and calculates the ideal green wave time window of the signal cycle of the target intersection based on traffic signal timing data. The core mechanism of arterial coordinated control is to enable traffic flow to cross multiple intersections without stopping at a specific speed by setting the phase difference. The propagation analysis module extracts the green light start time of the first intersection of the arterial road as the absolute time reference and calculates the theoretical time for a vehicle to travel from the upstream node to the target intersection by combining the physical distance between the intersections and the arterial coordinated design vehicle speed.

[0101] The coordinated design speed for arterial roads is set by the traffic management department based on the road physical grade and the average historical traffic flow statistics. The range of the coordinated design speed for arterial roads is between 40 and 60 kilometers per hour. The propagation analysis module performs algebraic superposition calculations on the absolute time reference, theoretical time, and absolute phase difference between the target intersection and the first intersection in the corresponding signal cycle. The algebraic superposition calculation process simultaneously considers the extension scale of the physical space of the intersection and the time offset law of the signal phase and generates the ideal green wave time window within the current signal cycle.

[0102] The calculation logic for the start and end times of the ideal green wave time window satisfies the following formula:

[0103] ;

[0104] In the formula, For the first The intersection at the ... The ideal green wave time window start time within each signal cycle; For the first The intersection at the ... The ideal green wave time window end time within each signal cycle; The first intersection is at the 1st The start time of the green light within each signal cycle; From the first intersection to the... The physical spatial distance between the intersections; Design vehicle speeds to be coordinated for main roads; For the first intersection and the first Absolute phase difference between intersections; For the first The intersection at the ... The effective green light duration within each signal cycle; The index number of the target intersection; This is the index number of the signal period;

[0105] The propagation analysis module converts the extracted signal timing parameters into specific time intervals on the absolute time coordinate axis based on the kinematic mapping principle. These specific time intervals on the absolute time coordinate axis constitute a numerical reference benchmark for quantifying the coordination compliance degree of the vehicle's actual driving trajectory.

[0106] S32, the propagation analysis module extracts the actual absolute timestamps of each vehicle arriving at the target intersection in the effective vehicle spatiotemporal passage chain sequence and performs a numerical comparison with the calculated ideal green wave time window. The propagation analysis module internally calls a piecewise linear function based on the absolute time axis to quantify the specific degree to which the actual arrival time of a vehicle deviates from the ideal set interval.

[0107] When the actual arrival timestamp of a bicycle falls within the ideal green wave time window, the propagation analysis module determines that the bicycle's trajectory conforms to the traffic signal coordination and control strategy and directly assigns the corresponding time deviation of the bicycle to 0.

[0108] When the actual arrival timestamp of a bicycle is earlier than the start time of the ideal green wave time window or later than the end time of the ideal green wave time window, the propagation analysis module calculates the algebraic difference between the actual arrival timestamp of the bicycle and the start time or end time of the ideal green wave time window, and assigns opposite algebraic positive or negative signs according to the objective physical state of the bicycle arriving earlier or later than the ideal green wave time window. The algebraic difference with positive or negative signs is used as the corresponding time deviation.

[0109] The mathematical model for calculating the time deviation of a single vehicle satisfies the following formula:

[0110] ;

[0111] In the formula, For the first The car in Within the signal cycle, the first... Time deviation at each intersection; For the first The intersection at the ... The ideal green wave time window start time within each signal cycle; For the first The intersection at the ... The ideal green wave time window end time within each signal cycle; For the first The car crossed the first The absolute timestamp of the section where the stop line of each intersection is located; For the vehicle's index number; The index number of the target intersection; This is the index number of the signal period;

[0112] The mathematical calculation model for single-vehicle time deviation converts the traffic physical phenomenon of vehicles arriving early or late into a continuous quantitative value. The magnitude of the time deviation value for a single vehicle directly reflects the objective physical magnitude by which the physical vehicle deviates from the theoretical green wave band due to external traffic flow interference or individual driver behavior differences during its journey on the road segment.

[0113] S33, after calculating the time deviation of all vehicles in the corresponding signal cycle, the propagation analysis module performs intersection-level state feature aggregation and binarization discretization processing. The propagation analysis module counts the total number of effective vehicles crossing the target intersection in the current signal cycle and performs an arithmetic mean calculation on the time deviation of all vehicles. The arithmetic mean calculation result generates a coordinated operation state feature representing the overall macro-operation state of traffic flow at the target intersection.

[0114] To address the issue of extremely low traffic during the statistical time window leading to a total of 0 valid vehicles and thus triggering a division-by-zero dead zone at the underlying level, the propagation analysis module is configured with bypass protection verification logic. When the total number of valid vehicles in the current signal cycle is detected to be 0, the propagation analysis module forcibly assigns the coordinated operation status feature of the current signal cycle to 0, or uses the historical value of the feature from the previous cycle. The propagation analysis module then calls the status judgment threshold to perform Boolean algebra mapping on the absolute values ​​of the continuous coordinated operation status features.

[0115] The specific determination logic for the coordinated operation status characteristics and binarized output of the intersection satisfies the following formula:

[0116] ;

[0117] ;

[0118] In the formula, For the first The intersection at the ... Characteristics of coordinated operation status within a signal cycle; For the first The intersection at the ... The total number of valid vehicles passing through within a signal cycle; For the first The car in Within the signal cycle, the first... Time deviation at each intersection; For the vehicle's index number; The index number of the target intersection; This is the index number of the signal period; For the first The intersection at the ... The state binarization determination result within each signal period; For the first The intersection at the ... Characteristics of coordinated operation status within a signal cycle; The preset state judgment threshold is set as a fixed constant within the range of 4 to 8 seconds, based on the statistical mean of the deviation distribution under the historical normal traffic conditions of the main road.

[0119] The propagation analysis module uses a threshold comparison mechanism to discretize and map the degree of deviation of continuous traffic flow into Boolean state variables that represent normal or abnormal conditions. The state binarization judgment result directly constitutes the basic input source for subsequent constraint logic verification steps.

[0120] See attached document Figure 3 In specific implementations, step S4 provided by the present invention may include the following steps:

[0121] The propagation analysis module directly constructs a comprehensive evaluation matrix for the traffic control error propagation constraint model, which includes spatial continuity constraint items, state continuity constraint items, direction consistency constraint items, and propagation evolution constraint items, for the target intersection whose state determination result is abnormal and its directly adjacent upstream intersection.

[0122] S41, the propagation analysis module reads the physical sort number of the target intersection whose state judgment result is abnormal and extracts the topology parameters of the target intersection and the candidate upstream intersection to perform initial spatial state and connectivity verification. The conventional spatial topology forced verification logic is directly called and merged as the basic prerequisite for establishing the causal relationship of error propagation in the initial spatial state and connectivity verification.

[0123] The mathematical verification logic of the spatial continuity constraint sub-item satisfies the formula:

[0124] ;

[0125] In the formula, For the first The intersection and the first Decision results on spatial continuity constraints at each intersection; The index number of the target intersection; This is the index number of the candidate upstream intersection.

[0126] S42, after establishing the spatial connectivity relationship, the propagation analysis module directly merges the binarized state determination results of the target intersection and the candidate upstream intersection within the same signal cycle. The propagation analysis module skips the intermediate state independent comparison process and directly maps and outputs the state continuous constraint determination results.

[0127] The calculation and decision logic for state continuity constraint sub-items satisfies the following formula:

[0128] ;

[0129] In the formula, For the first The intersection and the first The intersection at the ... The result of the state continuity constraint determination within each signal cycle; For the first The intersection at the ... The state binarization determination result within each signal period; For the first The intersection at the ... The state binarization determination result within each signal period; For Boolean logic AND operator; The index number of the target intersection; The index number of the candidate upstream intersection; This is the index number of the signal period.

[0130] S43, the propagation analysis module extracts the coordinated operation status features of the target intersection and the candidate upstream intersection, and directly maps the direction-consistent constraint sub-items output based on the algebraic sign homogeneity of the coordinated operation status features of the target intersection and the candidate upstream intersection. The initial logic verification and feature alignment operation are completed according to the standard processing flow without redundant decomposition.

[0131] The specific calculation and determination formula for the direction consistency constraint sub-item satisfies:

[0132] ;

[0133] In the formula, For the first The intersection and the first The intersection at the ... The result of the direction consistency constraint determination within each signal period; For the first The intersection at the ... Characteristics of coordinated operation status within a signal cycle; For the first The intersection at the ... Characteristics of coordinated operation status within a signal cycle; The index number of the target intersection; The index number of the candidate upstream intersection; This is the index number of the signal period.

[0134] S44, the propagation analysis module directly substitutes the empirical constant parameters characterizing the discrete characteristics of the vehicle fleet and the free-flow vehicle speed of the road segment into the fluid dynamics attenuation law to generate the theoretical attenuation boundary, and performs a numerical comparison between the theoretical attenuation boundary and the absolute value of the coordinated operation state characteristics of the target intersection. The theoretical attenuation boundary is calculated based on the absolute value of the coordinated operation state characteristics of the candidate upstream intersection. The propagation analysis module discards the intermediate conduction dissipation calculation nodes and directly outputs the final propagation evolution constraint judgment result.

[0135] The determination condition for the propagation and evolution constraint sub-item satisfies the formula:

[0136] ;

[0137] In the formula, For the first The intersection and the first The intersection at the ... The propagation evolution constraint determination results within each signal cycle; For the first The intersection at the ... Characteristics of coordinated operation status within a signal cycle; For the first The intersection at the ... Characteristics of coordinated operation status within a signal cycle; is the base of the natural logarithm; In this embodiment, the empirical constant parameter characterizing the discrete characteristics of the vehicle fleet is... The value range is set to 0.1 to 0.35; For the first The intersection to the first The distance between each intersection; This refers to the free-flow speed of the road segment; The index number of the target intersection; The index number of the candidate upstream intersection; This is the index number of the signal period.

[0138] See attached document Figure 4 In specific implementations, step S5 provided by the present invention may include the following steps:

[0139] S51, the propagation analysis module receives a comprehensive evaluation matrix containing spatial continuous constraint sub-items, state continuous constraint sub-items, direction consistency constraint sub-items, and propagation evolution constraint sub-items, and calls the logic operation unit to perform Boolean multiplication operation to generate a comprehensive propagation judgment index. The comprehensive propagation judgment index performs logical multiplication on the constraint judgment results of each independent dimension and uses the logical multiplication result to quantify the physical causal relationship of error transmission between the target intersection and the candidate upstream intersection.

[0140] The underlying judgment logic is set so that when the Boolean operation result of the comprehensive propagation judgment index is 1, the propagation analysis module determines that an objective traffic control error physical propagation behavior has occurred from the candidate upstream intersection to the target intersection within the current signal cycle. When the comprehensive propagation judgment index is 0, the propagation analysis module determines that the abnormal state of the target intersection is caused by independent and isolated events such as a surge in local traffic flow or illegal parking on the roadside, and blocks the propagation association between the target intersection and the candidate upstream intersection.

[0141] The Boolean product operation logic of the comprehensive propagation judgment index satisfies the following formula:

[0142] ;

[0143] In the formula, For the first The intersection and the first The intersection at the ... Comprehensive propagation judgment index within a signal cycle; For the first The intersection and the first The spatial continuity constraint determination results for each intersection; For the first The intersection and the first The intersection at the ... The result of the state continuity constraint determination within each signal cycle; For the first The intersection and the first The intersection at the ... The result of the direction consistency constraint determination within each signal period; For the first The intersection and the first The intersection at the ... The propagation evolution constraint determination results within each signal cycle; The index number of the target intersection; The index number of the candidate upstream intersection; This is the index number of the signal period;

[0144] The propagation analysis module is configured to perform dimensionality reduction and cleaning on the road network operation status dataset through logical product operations and filter out adjacent intersection node pairs with continuous physical transmission characteristics.

[0145] S52, after completing the calculation of the comprehensive propagation judgment index for all intersection node pairs, the propagation analysis module starts the underlying graph structure traversal algorithm to trace the physical source of the error. The propagation analysis module uses the arterial forward topology sequence as the basic framework and maps the target intersection with a comprehensive propagation judgment index of 1 and the candidate upstream intersection into connected vertices in the directed graph.

[0146] The propagation analysis module extracts a continuous sequence of anomalous state nodes along the topological connected edges of the directed graph and generates a structured arterial traffic control error propagation chain based on the continuous sequence of anomalous state nodes. The arterial traffic control error propagation chain represents the physical source of traffic flow deviation from the coordination baseline state and the spatial span and node depth of the gradual spread to the forward road segments.

[0147] S53, the propagation analysis module packages the generated trunk road traffic control error propagation chain and comprehensive propagation judgment index matrix into standardized format data, and pushes it to the result output module through the internal data bus as the input basis for subsequent mismatch source localization.

[0148] See attached document Figure 5 In specific implementations, step S6 provided by the present invention may include the following steps:

[0149] S61, the result output module receives the main road traffic control error propagation chain and the comprehensive propagation judgment index matrix containing all intersection node pairs issued by the propagation analysis module through the data bus inside the main road coordination mismatch source identification system based on traffic control error propagation constraints.

[0150] The output module initializes the node state attribute table for all intersection nodes in the arterial road forward topology sequence in the memory of the arterial road coordination mismatch source identification system based on traffic control error propagation constraints. The node state attribute table is configured with independent cascade waves and flag bits and independent anomaly flag bits. The output module starts the topology traversal engine and sequentially reads each intersection node entity in the node state attribute table in order to perform subsequent causal attribute stripping operations.

[0151] S62, for target intersection nodes whose status determination result is abnormal, the result output module determines whether the target intersection node is the first node in the intersection topology sequence. When the target intersection node is the first node, the result output module directly determines that the abnormal operation of the target intersection node is not affected by the status of the physical upstream node and sets the independent abnormal flag of the target intersection node in the node status attribute table. When the target intersection node is not the first node, the result output module accurately extracts the comprehensive propagation determination index values ​​corresponding to the target intersection node and the directly adjacent upstream intersection node from the comprehensive propagation determination index matrix.

[0152] When the value of the comprehensive propagation judgment index is equal to the logical truth value 1, the result output module determines that the current traffic flow disorder state of the target intersection node is caused by the physical propagation of the control error of the upstream node. The result output module will set the cascade wave and flag position of the target intersection node in the node state attribute table and logically remove the target intersection node from the potential source candidate queue.

[0153] When the value of the comprehensive propagation judgment index is equal to the logical false value 0, the result output module determines that the operation abnormality of the target intersection node is not affected by the status of the physical upstream node, and sets the independent abnormality flag of the target intersection node in the node status attribute table.

[0154] S63, the result output module traverses the node status attribute table and filters all cascade waves and subordinate propagation nodes whose flag bits are set. The result output module specifically extracts nodes that are not affected by the status of physical upstream nodes and whose own coordinated operation status is determined to be abnormal, that is, extracts nodes whose independent abnormal flag bits are set.

[0155] The output module packages and encapsulates the physical topology number of the node that is not affected by the state of the upstream physical node and whose own coordinated operation status is determined to be abnormal, the time window index corresponding to the abnormality, and the specific coordinated operation status characteristics. It dynamically constructs and generates an independent set of abnormal intersections. The set of independent abnormal intersections only retains intersection entities with spontaneous error distortion physical characteristics. The output module relies on the set of independent abnormal intersections to achieve thorough cleaning and isolation of cascaded noise data in the complex road network propagation network.

[0156] See attached document Figure 6In specific implementations, step S7 provided by the present invention may include the following steps:

[0157] S71, the result output module reads the completed set of independent abnormal intersections and calls the underlying feature extraction operator to extract the physical space sorting index of each independent abnormal intersection node in the set of independent abnormal intersections. The result output module is based on the legal forward driving physical trajectory order of the vehicle on the target arterial road.

[0158] The built-in sorting algorithm is invoked to perform ascending order reordering mapping on the physical space sorting index. The result output module constructs a one-dimensional topological retrieval sequence that strictly follows the physical evolution direction of traffic flow through the ascending order reordering mapping operation. The one-dimensional topological retrieval sequence objectively reflects the topological sequence of spontaneous abnormal events in the spatial dimension of the arterial road.

[0159] S72, the result output module starts a one-way pointer traversal operation from upstream to downstream on the one-dimensional topological search sequence based on the objective physical law that traffic control error propagates unidirectionally along the forward road segment with the traffic flow.

[0160] The output module directly captures the first physical node with the smallest spatial sorting index value in the one-dimensional topology search sequence through a one-way pointer traversal operation. The output module formally confirms the physical intersection corresponding to the first physical node as the initial mismatch source node of the target arterial road's current coordination control strategy failure at the logical level of the arterial road coordination mismatch source identification system based on traffic control error propagation constraints. The confirmation of the initial mismatch source node eliminates the interference of all downstream secondary error sources.

[0161] S73, after successfully locating and locking the initial mismatch source node, the result output module immediately triggers the retrieval circuit breaker mechanism at the software logic level of the arterial coordination mismatch source identification system based on traffic control error propagation constraints.

[0162] The retrieval circuit breaker mechanism sends a forced termination command to the topology traversal engine, simultaneously blocking the source detection and probing operations of the remaining downstream candidate nodes in the one-dimensional topology retrieval sequence by the one-way pointer traversal operation. The triggering of the retrieval circuit breaker mechanism avoids the redundant consumption of computing power in the arterial coordination mismatch source identification system based on traffic control error propagation constraints, and completely prevents the problem of multiple false alarms of mismatch sources caused by secondary oscillations in downstream traffic flow from the algorithm logic level, ensuring the uniqueness of the mismatch source location results and the accuracy of physical source tracing.

[0163] S74, the result output module, based on the determined initial mismatch source node and combined with the trunk road traffic control error propagation chain generated by the propagation analysis module, extracts the physical associated sweep topology path starting from the initial mismatch source node; the result output module reorganizes the physical number of the initial mismatch source node, the error occurrence time scale, and the physical associated sweep topology path into a standardized diagnostic data structure file.

[0164] The diagnostic data structure file is pushed to the traffic signal control platform through the output interface in a lightweight data exchange format. The diagnostic data structure file provides a complete, clear, and physically causal decision support basis for the subsequent adaptive signal control system to execute precise timing compensation strategies.

[0165] Application Examples:

[0166] To better understand the technical solution of the present invention, the following is an application example of urban arterial road traffic signal coordination control status monitoring and identification.

[0167] The system's data acquisition module collects vehicle passage data at each intersection of the target arterial road within a set statistical time window through the electronic police system. The data acquisition module also synchronously obtains the common signal cycle, green light ratio, and absolute phase difference timing parameters of each intersection within the corresponding time period from the traffic signal control platform.

[0168] The data processing module cleans the acquired data and removes missing or distorted data caused by equipment failure. The data processing module aligns the timestamps and associates the vehicle's passing records at various intersections along the target arterial road based on the vehicle's feature hash value. After removing invalid data such as vehicles leaving the arterial road midway or stopping for a long time midway, the data processing module generates a single-vehicle spatiotemporal passage chain sequence of valid vehicles.

[0169] The propagation analysis module calculates the ideal green wave time window for each node in each cycle based on the timing data. By comparing the actual vehicle passing time, the propagation analysis module determines that the vehicles at the upstream nodes fall within the ideal green wave time window and are in normal condition. At the same time, the propagation analysis module concludes that the arrival time of vehicles at multiple downstream nodes is later than the end time of the green wave and that there is a time deviation, and that the condition is judged as abnormal.

[0170] The propagation analysis module establishes constraint models for downstream nodes that exhibit anomalies, and then performs multi-dimensional logical verification of spatial continuity constraint items, state continuity constraint items, directional consistency constraint items, and propagation evolution constraint items to obtain a comprehensive propagation judgment index between nodes. Based on the comprehensive propagation judgment index, the propagation analysis module determines that the anomaly of the upstream abnormal node is isolated and that the anomalies of subsequent abnormal nodes are caused by the propagation effect of upstream errors.

[0171] The output module performs a cascade stripping operation and removes the subordinate nodes affected by the upstream from the source candidate queue. The output module places the upstream abnormal node that is not affected by the upstream and is itself abnormal into the independent abnormal intersection set. The output module searches this set in a forward direction and locks the node with the highest spatial sorting as the initial mismatch source for trunk coordination control. The output module triggers the retrieval circuit breaker mechanism to stop tracing downstream and generates a diagnostic data structure file containing the mismatch source and the affected path, which is then sent to the signal control system for timing adjustment compensation.

[0172] This invention compares the proposed identification system with traditional methods that rely solely on single intersection traffic threshold alarms without considering causal propagation relationships between adjacent intersections, as well as traditional methods that rely on human experience to replay video recordings for mismatch source investigation. Evaluation metrics include the identification accuracy rate for correctly identifying the probability of the initial source event causing the green wave failure on the arterial road, the average response time for outputting the mismatch source diagnosis report, the false alarm rate for incorrectly marking downstream secondary congestion as the initial source probability, and the completeness of the affected path, which covers the completeness of the actual affected intersection sequence.

[0173] The test results are shown in the table below.

[0174] Experimental verification and effect comparison table.

[0175] Traditional single-point threshold alarm method lower Longer higher lower Manual experience screening method medium longest medium medium Method of the present invention Highest shortest lowest Highest

[0176] Experimental comparisons show that the method of this invention has the best recognition accuracy and successfully filters out false anomalies caused by upstream and downstream linkages. The method of this invention solves the problem of high false alarm rate in traditional single-point threshold alarm methods, which misjudge downstream secondary congestion as the source.

[0177] The retrieval circuit breaker mechanism of this invention stops the consumption of computing power and locks the independent abnormal nodes in the topology sequence. The method of this invention shortens the diagnosis time from the long time of manual investigation to a short time and meets the requirements of system closed-loop control.

[0178] The error propagation chain constructed by this invention accurately depicts the scope of traffic flow distortion. The method of this invention achieves the highest rating in the scope path integrity index and provides a complete and reliable basis for adaptive timing compensation.

[0179] See appendix Figure 6 The figure shows the scatter distribution of the preset parallel green wave channel and the actual effective vehicle driving trajectory. The density of the scatter points falling inside and outside the green wave band shows the evolution of the vehicle time deviation.

[0180] See appendix Figure 7The design employs a dual-axis coordinate system with three different comparison methods on the horizontal axis. The vertical axis corresponds to a bar chart to show the comparison level between recognition accuracy and the integrity of the affected path, while the other vertical axis corresponds to a broken line to show the difference in average response time. The figure confirms the leading status of this invention in multiple performance indicators.

[0181] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Those skilled in the art can omit, substitute, and modify the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function and achieve substantially the same result using substantially the same method falls within the scope of the present invention.

Claims

1. A method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints, characterized in that, Includes the following steps: Discrete vehicle passage data and traffic signal timing data at each intersection of the target arterial road are obtained and analyzed and preprocessed to form a standard input dataset; Extract the unique vehicle identifier field from the standard input dataset, associate vehicle passage records, remove invalid chains, and generate a set of valid vehicle spatiotemporal passage chain sequences; The ideal green wave time window is calculated based on traffic signal timing data. The time deviation is calculated by combining the effective vehicle spatiotemporal data with the chain sequence set, generating coordinated operation status characteristics and outputting the status judgment result. For the target intersection whose state determination result is abnormal and the adjacent upstream intersection of the target intersection, a traffic control error propagation constraint model is constructed; Based on the traffic control error propagation constraint model, the logic is used to determine whether the target intersection and the adjacent upstream intersection have a propagation causal relationship or are independent of each other. Based on the logical judgment results, cascaded abnormal nodes are stripped, nodes that are not affected and are in abnormal state are extracted, and an independent set of abnormal intersections is generated. Extract the spatial sort index of nodes from the set of independent abnormal intersections, perform a one-way search according to the forward driving topology to obtain the initial mismatch source node, terminate the downstream probing and output the diagnostic results.

2. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The steps of acquiring discrete vehicle passage data and traffic signal timing data at each intersection of the target arterial road, and performing analytical preprocessing to form a standard input dataset include: Discrete vehicle passage data is acquired and an intersection topology sequence is constructed. The discrete vehicle passage data is then parsed and extracted into a vehicle passage record vector containing a unique vehicle identifier, an intersection physical topology sort number, an absolute timestamp record, and a driving direction code. The total number of signal cycles is calculated based on the length of the common signal cycle, and a time axis sequence of signal cycles arranged in absolute time order is constructed based on the start and end times of the green light. Distorted discrete data with missing unique identifier fields, recognition confidence below the lower threshold, excessive absolute timestamps, or reversed driving direction are discarded and cleaned. The absolute timestamps of the remaining valid vehicle passage records are time-scale aligned and stored in the database to form the standard input dataset.

3. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The steps of extracting the vehicle unique identifier field from the standard input dataset, associating vehicle passage records to remove invalid chains, and generating a set of valid vehicle spatiotemporal passage chain sequences include: For the standard input dataset, multiple discrete vehicle passage records with the same unique vehicle identifier are aggregated, and then reassembled in ascending order according to the absolute timestamp, and mapped into a single vehicle spatiotemporal passage chain sequence. Calculate the total number of physical nodes in the spatiotemporal passage chain sequence of a single vehicle. If the total number of physical nodes is less than the set lower limit threshold for trajectory validity, mark the spatiotemporal passage chain sequence of a single vehicle as a spatial broken chain sequence. Calculate the actual absolute timestamp difference between adjacent nodes. If the actual absolute timestamp difference exceeds the theoretical maximum passage time threshold calculated by dividing the physical spatial distance by the minimum speed limit, mark the spatiotemporal passage chain sequence of a single vehicle as a temporal broken chain sequence. Discard vehicle spatiotemporal passage chain sequences with spatial or temporal chain break markers directly, and archive and merge vehicle spatiotemporal passage chain sequences that have not triggered chain break markers to generate a set of valid vehicle spatiotemporal passage chain sequences.

4. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The steps of calculating the ideal green wave time window based on traffic signal timing data and calculating the time deviation by combining the effective vehicle spatiotemporal pass chain sequence set include: The theoretical time consumption is calculated by dividing the green light start time of the first intersection of the arterial road, the physical distance between intersections, and the coordinated design speed of the arterial road, and by performing an algebraic superposition operation on the absolute phase difference between the target intersection and the first intersection to generate the ideal green wave time window within the corresponding signal cycle. When the actual arrival absolute timestamp of a single vehicle falls within the ideal green wave time window, the vehicle's trajectory is determined to conform to the traffic signal coordination control strategy, and the time deviation is assigned a value of 0. When the actual arrival absolute timestamp of a single vehicle is earlier than the start time or later than the end time of the ideal green wave time window, the algebraic difference between the actual arrival absolute timestamp and the corresponding start and end times of the ideal green wave time window is calculated, and a positive or negative sign is assigned according to the objective physical state of early or late arrival. The algebraic difference with positive or negative signs is used as the time deviation of the single vehicle.

5. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The steps of generating coordinated operation state characteristics and outputting state determination results include: The time deviation of all vehicles in the current signal cycle is calculated by arithmetic mean to generate a coordinated operation status feature that represents the overall macroscopic operation status of traffic flow at the target intersection; and when the total number of valid vehicles in the current signal cycle is detected to be 0, the bypass protection verification logic is triggered to force the coordinated operation status feature to be assigned to 0 or to use the historical value of the coordinated operation status feature of the previous cycle. The preset state determination threshold is invoked, and Boolean algebra mapping is performed on the absolute value of the coordinated operation state characteristics. The discrete mapping outputs a binarized state determination result.

6. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The steps for constructing a traffic control error propagation constraint model for the target intersection whose state determination result is abnormal and its adjacent upstream intersection include: Extract the topological parameters of the target intersection and the candidate upstream intersection to perform initial spatial state verification, and map the output spatial continuous constraint sub-items; The binarized state determination results of the target intersection and the candidate upstream intersection within the same signal period are directly merged, skipping the intermediate state independent comparison step, and mapping the output state continuous constraint sub-items. Extract the coordinated operation status features of the target intersection and the candidate upstream intersection, and determine the direction consistency constraint sub-item based on the algebraic sign homogeneity of the coordinated operation status features of the target intersection and the candidate upstream intersection. The empirical constant parameters characterizing the discrete characteristics of the vehicle fleet and the free-flow vehicle speed of the road segment are substituted into the fluid dynamics attenuation law to generate the theoretical attenuation boundary. The absolute value of the theoretical attenuation boundary and the coordinated operation state characteristics of the target intersection are numerically compared to output the propagation evolution constraint sub-item.

7. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 6, characterized in that, The step of substituting the empirical constant parameters characterizing the discrete characteristics of the vehicle fleet and the free-flow vehicle speed on the road segment into the hydrodynamic attenuation law to generate the theoretical attenuation boundary includes: The absolute value of the coordinated operation status characteristics of the candidate upstream intersection is used as the calculation benchmark. The theoretical attenuation boundary is generated by combining the natural logarithm base and the empirical constant parameter characterizing the discrete characteristics of the vehicle fleet. The intermediate transmission dissipation calculation nodes are discarded and the propagation evolution constraint judgment result is directly output.

8. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The step of determining whether the target intersection and its adjacent upstream intersection have a propagation causal relationship or are independent based on the traffic control error propagation constraint model includes: A Boolean product operation is performed on the comprehensive evaluation matrix, which includes spatial continuity constraint items, state continuity constraint items, orientation consistency constraint items, and propagation evolution constraint items, to generate a comprehensive propagation judgment index. When the value of the comprehensive propagation judgment index is 1, it is determined that there is a physical propagation behavior of traffic control error. When the value of the comprehensive propagation judgment index is 0, it is determined to be an isolated event and the propagation association is blocked. The target intersection with a comprehensive propagation judgment index of 1 and the candidate upstream intersection are mapped into connected vertices in a directed graph. The abnormal state node sequence is extracted along the topological connected edges of the directed graph to generate the arterial road traffic control error propagation chain.

9. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The steps of stripping cascaded abnormal nodes based on logical judgment results, extracting unaffected nodes with abnormal states, and generating an independent set of abnormal intersections include: In memory, the node status attribute table is initialized with cascade waves, flag bits, and independent anomaly flag bits for the intersection nodes in the main road forward topology sequence. When the target intersection node is the first node in the intersection topology sequence, the independent anomaly flag of the target intersection node is set directly; when the target intersection node is not the first node and the value of the comprehensive propagation judgment index is equal to the logical truth value 1, the cascade wave and flag of the target intersection node are set, and the target intersection node is logically removed from the potential source candidate queue; when the value of the comprehensive propagation judgment index is equal to the logical false value 0, the independent anomaly flag of the target intersection node is set. Filter all cascaded waves and subordinate propagation nodes with set flags, package and encapsulate nodes with set independent anomaly flags, and dynamically construct and generate a set of independent anomaly intersections.

10. The method for identifying arterial road coordination mismatch sources based on traffic control error propagation constraints according to claim 1, characterized in that, The steps of extracting the spatial sorting index of nodes in the set of independent abnormal intersections, performing a one-way search according to the forward driving topology to obtain the initial mismatch source node, terminating downstream probing, and outputting the diagnostic results include: The physical space sorting index of each independent abnormal intersection node in the set of independent abnormal intersections is extracted and an ascending order reorganization mapping is performed to construct a one-dimensional topological retrieval sequence that follows the physical evolution direction of traffic flow; A one-way pointer traversal operation is initiated on the one-dimensional topological search sequence to capture the first physical node with the smallest physical space sorting index value and officially confirm it as the initial mismatch source node. Trigger the retrieval circuit breaker mechanism by sending a forced termination command to block the source detection and probing operations of the one-way pointer traversal operation on the remaining downstream candidate nodes in the one-dimensional topology retrieval sequence; reverse the extraction of the physical association and topology path starting from the initial mismatch source node and reconstruct it into a diagnostic data structure file.

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