Traffic jam judgment method and device based on map coordinates and vehicle big data

Abstract

According to the traffic jam judgment method and device based on the map coordinates and the vehicle big data provided by the embodiment of the invention, high-precision coordinate information and massive vehicle driving data provided by modern map service are fully utilized, and the vehicle driving direction, speed and other multi-dimensional data are comprehensively analyzed, so that the traffic jam judgment accuracy is improved. The traffic congestion state can be quickly and accurately judged, and timely and reliable decision support is provided for a traffic management department, so that traffic flow management is optimized, the road traffic efficiency is improved, and traffic delay and environmental pollution are reduced. According to the method, map geometric analysis and vehicle big data processing are innovatively combined, many limitations of a traditional method are overcome, and the method has remarkable application value and popularization prospects.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to a method and device for determining traffic congestion based on map coordinates and vehicle big data. Background Art

[0002] With the acceleration of urbanization and the rapid development of the automotive industry, urban traffic congestion is becoming increasingly serious, negatively impacting people's daily lives, economic development, and environmental quality. Against this backdrop, accurately and promptly identifying congested road sections has become a key issue in intelligent traffic management.

[0003] Traditional methods for identifying traffic congestion primarily rely on fixed traffic flow sensors, cameras, and other equipment. These devices have high deployment costs, limited coverage, and infrequent data updates, making them difficult to meet the needs of real-time traffic management. Furthermore, some methods based solely on vehicle counts, while able to reflect traffic flow to a certain extent, often fail to accurately determine congestion levels due to a lack of comprehensive consideration of key information such as road geometry and vehicle travel direction. For example, on two-way lanes, simply counting vehicles cannot distinguish between traffic flows in different directions, easily leading to misjudgments. Similarly, on complex overpasses or curves, the diverse vehicle travel directions make it difficult for traditional methods to effectively identify congested sections.

[0004] To address these issues, this paper proposes a traffic congestion identification method and device based on map coordinates and vehicle big data. By leveraging the high-precision coordinate information provided by modern map services and massive amounts of vehicle travel data, this method enables rapid and accurate identification of traffic congestion conditions, providing timely and reliable decision-making support for traffic management departments. This helps optimize traffic flow management, improve road efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and potential for widespread adoption. Summary of the Invention

[0005] In response to the problems in the existing technology, this application provides a traffic congestion identification method and device based on map coordinates and vehicle big data to solve the problems of high management costs and difficult dynamic expansion caused by the separate deployment of xxl-job, and to realize the automatic discovery and dynamic scheduling of scheduled tasks in a microservice environment.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions: In a first aspect, the present application provides a traffic congestion identification method based on map coordinates and vehicle big data, comprising: Obtaining a general route of the vehicle, where the general route is divided into multiple sections based on lines connecting selected points on a map; calculating the direction angle of the section and the direction of travel of the vehicle on the section based on the starting and ending coordinates of the section; Obtain all capture devices on the road section, and obtain the distance from each capture device to the road section and its capture direction; determine the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section as the road section belonging device; Calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle on the road section; The driving time and speed of all vehicles on the trajectory route are obtained based on the information collected by the capture device on the trajectory route, and when the vehicle's driving speed is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold, the road section is determined to be congested.

[0007] Furthermore, the step of calculating the direction angle of the road section according to the starting point coordinates and the end point coordinates of the road section includes: Convert the latitude and longitude of the starting and ending coordinates of the road segment into radians; Using a four-quadrant inverse tangent function, and based on the radian value, calculating the radian value of the direction angle of the road section from the starting point coordinate to the end point coordinate; Convert the direction angle radian value into standard longitude and latitude angle values, and determine the direction angle of the road section according to the standard longitude and latitude angle values; Furthermore, the step of calculating the direction angle of the road section according to the starting point coordinates and the end point coordinates of the road section includes: If the calculated result is a negative value, add 360° to keep the direction angle range from 0° to 360°; Set the maximum allowable deviation distance of the road segment. When the road segment deviates from its straight line segment by more than the deviation distance, it will be split and the direction angle of each straight line segment will be calculated separately.

[0008] Furthermore, the step of obtaining the distance between each capture device and the road section includes: The distance D from the capture device P to the road section AB is calculated based on the relationship between the capture device P and the two endpoints A and B of the road section AB: like , then the angle between AP and BP is an obtuse angle, and the distance D is the vertical distance from the capture device P to the road section AB; like , then the angle between AP and AB is an obtuse angle, and the distance D is the distance between AP; like , then the angle between AB and BP is an obtuse angle, and the distance D is the distance of BP.

[0009] Furthermore, the step of obtaining the distance between each capture device and the road section includes: Use GeoHash encoding to convert the coordinates of the captured device into a string hash value, and group the captured devices according to the first few digits of the GeoHash code; Filter out the capture devices grouped near the road section and calculate the distances of the capture devices in the group; The method for determining whether the capturing direction is consistent with the driving direction of the vehicle on the road section is as follows: Define the angle threshold between the capture direction and the road section direction angle, and set the angle = abs(capture direction - road section direction angle). If the angle is greater than 180°, then the angle = 360° - angle. If the angle is within the range of ±30°, it is determined that the capture direction matches the direction angle of the road section, that is, it is consistent with the driving direction of the vehicle on the road section.

[0010] Furthermore, the step of calculating the distance between the device to which the road section belongs and the starting point of the road section includes: Calculate the perpendicular point from the equipment belonging to the road section to the road section, use the cumulative distance from the perpendicular point to the starting point of the road section as the position parameter of the equipment belonging to the road section on the road section, and sort the equipment belonging to the road section according to this position parameter.

[0011] Furthermore, the step of obtaining the driving time and driving speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route includes: Obtain all vehicle data collected by the capture devices along the trajectory route; Query the passing data of the first and last capture devices on each vehicle's passing trajectory; Calculate the travel speed of all vehicles on the trajectory route based on the travel time and road segment distance.

[0012] In a second aspect, the present application provides a traffic congestion determination device based on map coordinates and vehicle big data, comprising: A road segment acquisition module is used to obtain the vehicle's overall route, which is divided into multiple road segments based on the selected points on the map; and calculate the direction angle of the road segment and the vehicle's driving direction on the road segment based on the starting and ending coordinates of the road segment; The device acquisition module is used to acquire all the capture devices on the road section, and obtain the distance of each capture device to the road section and its capture direction; the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section is determined as the road section belonging device; The trajectory acquisition module is used to calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle traveling on the road section; The congestion judgment module is used to obtain the driving time and speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and determine that the road section is congested when the vehicle's driving speed is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold.

[0013] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for determining traffic congestion based on map coordinates and vehicle big data are implemented.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the traffic congestion determination method based on map coordinates and vehicle big data.

[0015] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the traffic congestion determination method based on map coordinates and vehicle big data.

[0016] As can be seen from the above technical solution, this application provides a traffic congestion identification method and device based on map coordinates and vehicle big data. This method fully utilizes the high-precision coordinate information and massive vehicle driving data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it can quickly and accurately identify traffic congestion status, providing timely and reliable decision-making support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1Schematic diagram of a flow chart of a traffic congestion determination method based on map coordinates and vehicle big data in an embodiment of the present application; Figure 2 A schematic diagram of connecting points on a map road for a traffic congestion determination method based on map coordinates and vehicle big data in an embodiment of the present application; Figure 3 This is a structural diagram of a traffic congestion determination device based on map coordinates and vehicle big data in an embodiment of the present application; Figure 4 Schematic diagram of the structure of the electronic device in the embodiment of the present application.

[0019] Reference numerals: Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0022] Considering the existing problems in the prior art, this application provides a traffic congestion identification method and device based on map coordinates and vehicle big data. This method fully utilizes the high-precision coordinate information and massive amounts of vehicle travel data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle travel direction and speed, it enables rapid and accurate identification of traffic congestion conditions, providing timely and reliable decision-making support for traffic management departments, thereby optimizing traffic flow management, improving road efficiency, and reducing traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many of the limitations of traditional methods and possessing significant application value and promotional prospects.

[0023] In order to solve the problem that existing methods cannot judge traffic congestion in a timely and accurate manner, this application provides an embodiment of a traffic congestion judgment method based on map coordinates and vehicle big data, see Figure 1-Figure 2 The traffic congestion identification method based on map coordinates and vehicle big data specifically includes the following contents: Step S101: Obtain the total route of the vehicle, which is divided into multiple sections according to the selected points on the map; calculate the direction angle of the section and the driving direction of the vehicle on the section according to the starting coordinates and the end coordinates of the section.

[0024] refer to Figure 2 As shown, in this embodiment, the map points are ABC, and two general routes can be formed between AC, namely, starting point A and end point C, and starting point C and end point A. This embodiment takes one of them as an example for explanation, and the general route is divided into two sections: point A to point B and point B to point C.

[0025] Optionally, in this embodiment, the direction angle of the road section is calculated based on the starting point coordinates and the end point coordinates of the road section, specifically: (1) Convert the latitude and longitude of the starting point coordinates S (LonS, LatS) and the ending point coordinates E (LonE, LatE) of the road section into radians:

[0026] (2) Calculate the direction angle from the starting point coordinate S to the end point coordinate E. The formula is as follows:

[0027] Among them, atan2(y, x) is the four-quadrant inverse tangent function, which determines the quadrant of the angle based on the positive and negative signs of x and y. The returned angle range is π (-180° to 180°), usually with the north direction as the reference (that is, the north direction is 0 degrees and increases clockwise).

[0028] (3) Convert the angular value of the direction angle to the standard latitude and longitude angle value: Angle value = θ ×

[0029] The method for determining the direction of a road section based on the standard latitude and longitude angle values is: 0° to 22.5° and 337.5° to 360°: The direction of the road segment is due north (N).

[0030] 22.5° to 67.5°: The road section direction is northeast (NE).

[0031] 67.5° to 112.5°: The road section direction is due east (E).

[0032] 112.5° to 157.5°: The road section direction is southeast (SE).

[0033] 157.5° to 202.5°: The road section direction is due south (S).

[0034] 202.5° to 247.5°: The road section direction is southwest (SW).

[0035] 247.5° to 292.5°: The road section direction is due west (W).

[0036] 292.5° to 337.5°: The road section direction is northwest (NW).

[0037] For example: Suppose there is a route with starting coordinates S (LonS=116.397528, LatS=39.908735) and ending coordinates E (LonE=116.407528, LatE=39.909735).

[0038] 1. Convert the latitude and longitude of the starting and ending points into radians: LatS radians = 39.908735 × ≈0.6968 LonS radians = 116.397528 × ≈2.0306 LatE radians = 39.909735 × ≈0.6969 LonE radians = 116.407528 × ≈2.0311 2. Calculate the direction angle: Lon radians = 2.0311 − 2.0306 = 0.0005 y=sin(0.0005)×cos(0.6969)≈0.00049999×0.7660≈0.000383 x=cos(0.6968)×sin(0.6969)−sin(0.6968)×cos(0.6969)×cos(0.0005) Calculate the approximate value of each part: cos(0.6968)≈0.7661 sin(0.6969)≈0.6445 sin(0.6968)≈0.6444 cos(0.6969)≈0.7660 cos(0.0005)≈0.99999994 Substitute the expression for x: x≈0.7661×0.6445−0.6444×0.7660×0.99999994≈0.5−0.5≈0.0 (the calculation is simplified here, and there will be tiny non-zero values in reality) Since the value of x is very small and close to zero, and y is a positive number, the direction angle θ=atan2(0.000383,0)≈ ≈1.5708 radians.

[0039] 3. Convert radians to standard degrees: Angle value = 1.5708× ≈90° This indicates that the heading angle from start point S to end point E is approximately 90°, or due east. While actual calculations may vary slightly due to minor coordinate discrepancies and computational accuracy, the general direction of this route segment is east. This calculation allows us to determine the heading angle of each route segment on the map, providing accurate directional information for subsequent operations such as traffic congestion detection.

[0040] Optionally, in this embodiment, an angle normalization step can be added when calculating the direction angle from the starting coordinate S to the end coordinate E. If the calculated result is a negative value, 360° is added to ensure that the angle range is [0, 360°]. For example, if the calculated direction angle is -45°, it is normalized to 315°.

[0041] Optionally, in this embodiment, to avoid polar errors, the improved Vincenty formula may be used to calculate the direction angle: θ=arctan2(sinΔLon·cosLatE, cosLatS·sinLatE- sinLatS·cosLatE·cos Lon) in, Lon is the longitude difference between the starting point S and the end point E. If the calculated direction angle θ is less than 0, then θ=θ+2π, and the output direction angle is ultimately within the range of 0° to 360°, with due north as 0° and increasing clockwise.

[0042] Optionally, in this embodiment, for complex roads (such as S-shaped curves), the route can be split into multiple straight line segments. Specifically, the split points can be determined by setting a maximum allowable deviation distance (e.g., 10 meters). When the route deviates from a straight line segment by more than this deviation distance, methods such as Bezier curve interpolation can be used to segment the curved road, and the bearing angle can be calculated for each straight line segment. Furthermore, the bearing angle difference between adjacent segments can be detected. If it is greater than 45°, the curve is marked and the segment density is increased at the curve to more accurately reflect the direction changes of the route.

[0043] Optionally, in this embodiment, the calculated road section can also be verified by invalid route filtering. If the section length is less than 5 meters, or the direction angle calculation fails (such as the starting point and the end point coincide, resulting in the inability to calculate the direction angle), the section will be discarded and not included in subsequent analysis.

[0044] Optionally, in this embodiment, the topological relationship of the road segments can also be constructed: while generating the road segment direction angle, the connection relationship between adjacent road segments is recorded to provide basic data for the trajectory construction in step S103 so that adjacent line segments can be correctly connected when constructing the trajectory route. Specifically: Node extraction: Identify key nodes in the road network, such as intersections, junctions, and entrances and exits. These nodes connect road segments and serve as turning points in vehicle travel paths. Node extraction can be achieved by analyzing the intersections of road segments, using the intersections of intersecting road segments as nodes.

[0045] Edge Determination: Based on the extracted nodes, edges in the road network are determined. Each edge represents a continuous road segment connecting two adjacent nodes. Edge attributes include the starting node, ending node, road type, length, number of lanes, and design speed.

[0046] Connection relationship construction: Establishing the connection relationship between nodes and edges, that is, determining the edges connected to each node, as well as the starting and ending nodes of each edge. This can be achieved by finding the spatial relationship between nodes and edges. For example, for each node, find edges within a certain distance threshold (such as 1 meter) and connect these edges to the node.

[0047] Adjacency relationship building: Constructing adjacency relationships between edges, that is, determining the connection method and direction between adjacent edges. This is achieved by analyzing the starting and ending nodes of the edges. If two edges share a common node, they are adjacent. The directional relationship between adjacent edges is also recorded, such as forward connection (vehicles can travel directly from one edge to the other) or reverse connection (vehicles need to turn around to travel from one edge to the other).

[0048] Path connectivity check: Check the connectivity of the road network to ensure that all edges and nodes form a connected network. For disconnected parts, further analysis and processing are performed. These may be dead-end roads caused by missing or incorrect data, and the data needs to be supplemented or corrected.

[0049] Data structure selection: Choose an appropriate data structure to store topological relationships. Common data structures include adjacency lists, adjacency matrices, and linked lists. An adjacency list is a commonly used data structure that maintains an edge list for each node, recording information about all edges connected to that node. It also records the starting and ending nodes of each edge, as well as information about adjacent edges.

[0050] Data Storage: Store established topological relationships in a database or file for easy query and analysis. Topological relationship data can be stored in relational databases (such as MySQL and PostgreSQL) or non-relational databases (such as MongoDB and Neo4j). When storing topological relationship data, a reasonable table or document structure must be designed to efficiently store and query nodes, edges, and the topological relationships between them.

[0051] Dynamic data updates: Regularly obtain updated road network data from map data providers and dynamically update topological relationships. This includes operations such as adding new roads, modifying road attributes, and deleting abandoned roads. The update process requires re-preprocessing data, identifying nodes and edges, and establishing and storing topological relationships.

[0052] Topology Verification: After each update, the topology is verified to ensure that the updated topology remains correct and connected. A series of verification rules and algorithms are run to check the integrity of the topology, such as checking for isolated nodes, duplicate edges, self-loops, and other issues. Any problems found are corrected promptly.

[0053] By constructing topological relationships, an accurate, complete and efficient road network topology model can be provided for traffic congestion identification methods, thereby supporting subsequent traffic flow analysis, route planning, congestion identification and other applications.

[0054] Step S102: Obtain all capture devices on the road section, and obtain the distance from each capture device to the road section and its capture direction; determine the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section as the road section belonging device.

[0055] Optionally, in this embodiment, the step of obtaining the distance between each capture device and the road section is specifically: using a multi-threaded approach to judge the distance between the device and the path segment based on a set distance threshold, and initially determining the devices on the path; exemplarily, if there are approximately 40,000 device data, the number of threads is set to 20, and the distance threshold can be set to 10 meters, that is, if the distance between the device and the path segment is less than 10 meters, the device is considered to be on the path.

[0056] The distance D between the device and the path segment is calculated based on the relationship between the longitude and latitude of the device point P (LonP, LatP) and the two endpoints A (LonA, LatA) and B (LonB, LatB) of the path segment in three cases: (1) When the angle between AP and BP is an obtuse angle, the distance D is the perpendicular distance from point P to the path segment AB. , then the angle between AP and BP is an obtuse angle. Use Heron's formula to calculate the vertical distance, as shown below:

[0057] Among them, AB, BP, and AP are the line segments formed by the device point and the two endpoints of the path segment, D is the distance from the device point to the path segment, and S is the area of the triangle PAB.

[0058] (2) When the angle between AP and AB is an obtuse angle, the distance D is the distance of AP. , then the angle between AP and AB is an obtuse angle. The distance calculation formula between the device point P and the endpoint A of the path segment is as follows:

[0059] Among them, a=LatA-LatP is the difference between the latitudes of two points, b=LonA-LonP is the difference between the longitudes of two points, and 6378.137 is the equatorial radius of the earth in kilometers.

[0060] (3) When the angle between AB and BP is an obtuse angle, the distance D is the distance of BP. , then the angle between AP and AB is an obtuse angle. The distance calculation formula between the device point P and the endpoint B of the path segment is the same as the formula for calculating the distance D in (2).

[0061] Optionally, in this embodiment, spatial indexing technology can also be introduced to achieve rapid retrieval of device locations by converting geographic location coordinates into string hash values, thereby optimizing distance calculation. Specifically, the location information of the device can be converted into GeoHash code. For example, the grid accuracy is selected to level 6, corresponding to a side length of approximately 10 meters, which matches the distance threshold. 40,000 devices are stored in Redis according to GeoHash code, where the Key is the GeoHash value and the Value is a list of device IDs. The devices are then grouped according to the first few digits of the GeoHash code. The geographical locations of devices in the same group are relatively close. In this way, when calculating the distance from the device to the route segment, the device group that may be near the route can be screened out first, and then accurate distance calculation can be performed to improve retrieval efficiency.

[0062] Furthermore, an R-tree index can be constructed in memory, with the minimum bounding rectangle (MBR) of each node containing at least 50 device coordinates. During a query, the MBR of the target road segment AB is expanded by 10 meters (consistent with the distance threshold) to perform a range search and quickly find devices that may be near the road segment.

[0063] Optionally, in this embodiment, the device direction matching rule can be set as follows: a threshold for the angle between the captured device direction and the route direction is defined as ±30°. For example, if the direction angle of a road section is 90° (due east), the captured device direction is considered valid if it is within the range of 60° - 120°. Specifically, to calculate the angle between the device direction and the route direction, the formula can be used: angle = abs(device direction angle - route direction angle). If the angle is greater than 180°, then angle = 360° - angle. If the angle is within the range of ±30°, the device direction is considered to match the route direction, i.e., it is consistent with the direction of travel of vehicles on that road section.

[0064] Step S103: Calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle traveling on the road section.

[0065] Optionally, in this embodiment, a linear referencing system (Linear Referencing) can be used to project the devices belonging to the road section onto the route segment. The specific method is to calculate the perpendicular point from the device to the route segment, use the cumulative distance from the perpendicular point to the starting point of the route as the position parameter of the device on the route, and sort the devices according to this parameter. For example, the starting point of the route is A, and the distance from the perpendicular point of the device P to point A is d, then the devices are sorted according to the size of d. Alternatively, the device coordinates P are projected onto the road section AB, and the normalized position parameter t of the projection point P' is calculated. The specific calculation method is t = [(PA)·(BA)] / |BA|², and the value range of t is between [0,1]. According to the size of the t value, the devices are sorted in ascending order, and the actual sorting basis is S= A+ t (BA).

[0066] At the same time, for branch sections, the topology map constructed in step S101 can also be used to match the shortest reasonable path using the Dijkstra algorithm to construct a complete trajectory route. Specifically: First, the trajectory route is constructed using the road network data provided by S101 (including nodes, edges, connection relationships, etc.). This data is stored in a database or file, and S103 can directly query and use this data when constructing the trajectory.

[0067] Then, the coordinates of the device are projected onto the nearest road, and the edge information in the topological relationship constructed in S101 (such as the start and end coordinates of the road) is used to calculate the perpendicular point of the device to each road and determine the position of the perpendicular point in the road network, or calculate the normalized position parameter t of the device on the edge.

[0068] After determining where the devices are projected onto the road network, the topological connections are used to sort the devices. This reflects the actual order in which vehicles travel on the road, ensuring that the device sorting conforms to the road's connectivity. For example, the topological connections between nodes and edges can be used to sort the devices based on the cumulative distance (or normalized position parameter) between the device projections on the road.

[0069] In complex road networks, since vehicles may change their driving direction at intersections or branches, the branch and intersection information in the topological relationship can be used to identify intersection nodes, and the most likely driving path of the vehicle can be determined based on the adjacency relationship in the topological relationship. Then, a path planning algorithm (such as the Dijkstra algorithm) is used to match the shortest reasonable path to ensure the continuity and accuracy of the trajectory.

[0070] When constructing a trajectory route, if there is a situation where the device is missing or the data is discontinuous, the adjacency relationship and connectivity information in the topology relationship can be used to interpolate and correct the trajectory to ensure the continuity and integrity of the trajectory.

[0071] Optionally, processing of abnormal data can be added. For example, the change in the device position in multiple consecutive snapshots can be calculated to determine whether the device coordinates are drifting, and the device position drift can be corrected. For example, the maximum offset tolerance of the device distance projection point P' can be set to 15 meters. That is, when calculating the distance from the device to the route segment, if it is found that the device position is offset by more than 15 meters compared with the last recorded position and exceeds the normal error range, it will be marked as an abnormal point. At this time, the historical average position can be used to replace the current coordinates to ensure the accuracy of the data. Regarding missing sections, the continuous trajectory can be supplemented by Bezier curve fitting. According to the existing device position data, the control points of the Bezier curve are determined to generate a smooth curve to supplement the missing section trajectory.

[0072] Step S104: Obtain the driving time and driving speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and determine that the road section is congested when the driving speed of the vehicle is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold.

[0073] Optionally, in this embodiment, the step of obtaining the travel time and travel speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route is specifically as follows: The Flink sliding window computation engine is used to obtain information about all devices along the trajectory route. The HDFS query retrieves the vehicle passing data for the first and last devices, constructs a time and distance set, and calculates the vehicle's speed along the trajectory route. For example, the Flink sliding window size is set to 5 minutes, with a sliding step of 1 minute. This method enables timely updating and processing of vehicle travel data, enabling real-time monitoring and analysis of traffic congestion. Furthermore, ValueState is used to store the latest congestion level for each road section, avoiding repeated calculations, improving computational efficiency, and ensuring the system can quickly respond to changing traffic conditions.

[0074] The formula for calculating vehicle speed is: , where S_k is the device coordinate, t_first and t_last are the first and last capture times of the vehicle, respectively. This formula calculates the average speed of the vehicle by dividing the distance traveled by the travel time along the trajectory.

[0075] Furthermore, to ensure accurate vehicle matching, a time window constraint can be used to match only data from the same vehicle within the time range [t_first, t_first + segment length / minimum speed limit]. For example, if the segment length is 1 km and the minimum speed limit is 30 km / h, the time window is 2 minutes. This avoids matching data from vehicles unrelated to the current segment and improves the accuracy of congestion detection.

[0076] Optionally, in this embodiment, a dynamic congestion threshold may be set, that is, the congestion level may be divided into four conditions: smooth traffic, mild congestion, severe congestion, and abnormal state, based on the relationship between the vehicle speed and the road speed limit. Specific details are as follows: Smooth: When the driving speed v is greater than or equal to 70% of the speed limit, the road is in a smooth state and no alarm is triggered.

[0077] Mild congestion: When the driving speed v is between 50% and 70% of the speed limit, it is judged as mild congestion, triggering a yellow warning and recording relevant log information for subsequent analysis and processing.

[0078] Severe congestion: When the driving speed v is less than 50% of the speed limit, it is judged as severe congestion, triggering a red alert, and the congestion information is pushed to the traffic management platform, and relevant departments are notified in time to divert traffic.

[0079] Abnormal state: If the driving speed v exceeds 120% of the speed limit or is less than 5 km / h, it is considered an abnormal state. This may be due to equipment failure, abnormal vehicle driving, or other reasons. In this case, the relevant data should be discarded and an equipment maintenance alarm should be triggered, prompting timely inspection and maintenance of the equipment.

[0080] This application proposes a traffic congestion identification method based on map coordinates and vehicle big data. This method leverages the high-precision coordinate information and massive amounts of vehicle travel data provided by modern map services. By comprehensively analyzing multi-dimensional data such as vehicle travel direction and speed, it enables rapid and accurate identification of traffic congestion conditions. This provides timely and reliable decision-making support for traffic management departments, optimizing traffic flow management, improving road efficiency, and reducing traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and potential for widespread adoption.

[0081] In order to solve the problem that existing methods cannot judge traffic congestion in a timely and accurate manner, the present application provides an embodiment of a traffic congestion determination device based on map coordinates and vehicle big data for realizing all or part of the content of the traffic congestion determination method based on map coordinates and vehicle big data, see Figure 3 The traffic congestion determination device based on map coordinates and vehicle big data specifically includes the following contents: The road segment acquisition module 10 is used to obtain the general route of the vehicle, which is divided into multiple road segments according to the connection lines of the selected points on the map; calculate the direction angle of the road segment and the driving direction of the vehicle on the road segment according to the starting coordinates and the ending coordinates of the road segment; The device acquisition module 20 is used to acquire all capture devices on the road section, and obtain the distance of each capture device to the road section and its capture direction; the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section is determined as the road section belonging device; The trajectory acquisition module 30 is used to calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all the equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle traveling on the road section; The congestion judgment module 40 is used to obtain the driving time and driving speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and determine that the road section is congested when the driving speed of the vehicle is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold.

[0082] As can be seen from the foregoing description, the traffic congestion identification device based on map coordinates and vehicle big data provided by the embodiments of this application fully utilizes the high-precision coordinate information and massive vehicle driving data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it achieves rapid and accurate identification of traffic congestion conditions, providing timely and reliable decision-making support for traffic management departments, thereby optimizing traffic flow management, improving road traffic efficiency, and reducing traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promotional prospects.

[0083] From a hardware perspective, to address the problem that existing methods cannot promptly and accurately determine traffic congestion, this application provides an embodiment of an electronic device for implementing all or part of the traffic congestion determination method based on map coordinates and vehicle big data. The electronic device specifically includes the following: A processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to transmit information between the traffic congestion determination device based on map coordinates and vehicle big data and related devices such as core business systems, user terminals, and related databases; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiments of the traffic congestion determination method based on map coordinates and vehicle big data and the embodiments of the traffic congestion determination device based on map coordinates and vehicle big data in the embodiments, the contents of which are incorporated herein and repeated parts are not repeated.

[0084] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0085] In practical applications, part of the traffic congestion determination method based on map coordinates and vehicle big data can be executed on the electronic device as described above, or all operations can be completed on the client device. The specific selection can be based on the processing capabilities of the client device and the limitations of the user's usage scenario. This application does not impose any restrictions on this. If all operations are completed on the client device, the client device may also include a processor.

[0086] The aforementioned client device may include a communication module (i.e., a communication unit) capable of establishing a communication connection with a remote server to facilitate data transmission with the server. The server may include a server at the task scheduling center or, in other implementation scenarios, a server on an intermediate platform, such as a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may comprise a single computer device, a server cluster consisting of multiple servers, or a distributed server configuration.

[0087] Figure 4 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 4 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 4 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0088] In one embodiment, the traffic congestion determination method based on map coordinates and vehicle big data can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: Step S101: Obtaining a general route of the vehicle, wherein the general route is divided into a plurality of road sections according to the lines connecting the selected points on the map; calculating the direction angle of the road section and the driving direction of the vehicle on the road section according to the starting coordinates and the ending coordinates of the road section; Step S102: Acquire all capture devices on the road section, and obtain the distance from each capture device to the road section and its capture direction; determine the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section as the road section belonging device; Step S103: Calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle traveling on the road section; Step S104: Obtain the driving time and driving speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and determine that the road section is congested when the driving speed of the vehicle is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold.

[0089] As can be seen from the foregoing description, the electronic device provided in the embodiments of this application fully utilizes the high-precision coordinate information and massive amounts of vehicle travel data provided by modern map services. By comprehensively analyzing multi-dimensional data such as vehicle travel direction and speed, it enables rapid and accurate identification of traffic congestion conditions, providing timely and reliable decision-making support for traffic management departments to optimize traffic flow management, improve road efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promotional prospects.

[0090] In another embodiment, the traffic congestion determination device based on map coordinates and vehicle big data can be configured separately from the central processing unit 9100. For example, the traffic congestion determination device based on map coordinates and vehicle big data can be configured as a chip connected to the central processing unit 9100, and the function of the traffic congestion determination method based on map coordinates and vehicle big data can be realized through the control of the central processing unit.

[0091] like Figure 4 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 4 In addition, the electronic device 9600 may also include all components shown in Figure 4 For components not shown, reference may be made to the prior art.

[0092] like Figure 4 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0093] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0094] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0095] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), or SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is capable of storing additional data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs, or processes used by the central processing unit 9100 to execute operations of the electronic device 9600.

[0096] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, images, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0097] The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0098] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless local area network modules. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130, providing audio output via the speaker 9131 and receiving audio input from the microphone 9132, thereby implementing common telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0099] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the traffic congestion determination method based on map coordinates and vehicle big data in the above-mentioned embodiments, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the computer program implements all steps of the traffic congestion determination method based on map coordinates and vehicle big data in the above-mentioned embodiments, where the execution subject is a server or a client. For example, when the processor executes the computer program, the following steps are implemented: Step S101: Obtaining a general route of the vehicle, wherein the general route is divided into a plurality of road sections according to the lines connecting the selected points on the map; calculating the direction angle of the road section and the driving direction of the vehicle on the road section according to the starting coordinates and the ending coordinates of the road section; Step S102: Acquire all capture devices on the road section, and obtain the distance from each capture device to the road section and its capture direction; determine the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section as the road section belonging device; Step S103: Calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle traveling on the road section; Step S104: Obtain the driving time and driving speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and determine that the road section is congested when the driving speed of the vehicle is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold.

[0100] As can be seen from the foregoing description, the computer-readable storage medium provided in the embodiments of this application fully utilizes the high-precision coordinate information and massive amounts of vehicle travel data provided by modern map services. By comprehensively analyzing multi-dimensional data such as vehicle travel direction and speed, it enables rapid and accurate identification of traffic congestion conditions, providing timely and reliable decision-making support for traffic management departments, thereby optimizing traffic flow management, improving road efficiency, and reducing traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promotional prospects.

[0101] The embodiments of the present application also provide a computer program product capable of implementing all steps of the traffic congestion determination method based on map coordinates and vehicle big data in the above-mentioned embodiments, where the execution subject is a server or a client. When the computer program / instructions are executed by a processor, the computer program / instructions implement the steps of the traffic congestion determination method based on map coordinates and vehicle big data. For example, the computer program / instructions implement the following steps: Step S101: Obtaining a general route of the vehicle, wherein the general route is divided into a plurality of road sections according to the lines connecting the selected points on the map; calculating the direction angle of the road section and the driving direction of the vehicle on the road section according to the starting coordinates and the ending coordinates of the road section; Step S102: Acquire all capture devices on the road section, and obtain the distance from each capture device to the road section and its capture direction; determine the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section as the road section belonging device; Step S103: Calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle traveling on the road section; Step S104: Obtain the driving time and driving speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and determine that the road section is congested when the driving speed of the vehicle is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold.

[0102] As can be seen from the foregoing description, the computer program product provided in the embodiments of this application fully utilizes the high-precision coordinate information and massive amounts of vehicle travel data provided by modern map services. By comprehensively analyzing multi-dimensional data such as vehicle travel direction and speed, it enables rapid and accurate identification of traffic congestion conditions, providing timely and reliable decision-making support for traffic management departments, thereby optimizing traffic flow management, improving road efficiency, and reducing traffic delays and environmental pollution. This method innovatively combines map geometry analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promotional prospects.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0107] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A traffic congestion identification method based on map coordinates and vehicle big data, characterized in that: The method comprises: Obtaining a general route of the vehicle, where the general route is divided into multiple sections based on lines connecting selected points on a map; calculating the direction angle of the section and the direction of travel of the vehicle on the section based on the starting and ending coordinates of the section; Obtain all capture devices on the road section, and obtain the distance from each capture device to the road section and its capture direction; determine the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section as the road section belonging device; Calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle on the road section; The driving time and speed of all vehicles on the trajectory route are obtained based on the information collected by the capture device on the trajectory route, and when the vehicle's driving speed is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold, the road section is determined to be congested.

2. The traffic congestion identification method based on map coordinates and vehicle big data according to claim 1, characterized in that: The step of calculating the direction angle of the road section according to the starting point coordinates and the end point coordinates of the road section includes: Convert the latitude and longitude of the starting and ending coordinates of the road segment into radians; Using a four-quadrant inverse tangent function, and based on the radian value, calculating the radian value of the direction angle of the road section from the starting point coordinate to the end point coordinate; The direction angle radian value is converted into a standard longitude and latitude angle value, and the direction angle of the road section is determined according to the standard longitude and latitude angle value.

3. The traffic congestion identification method based on map coordinates and vehicle big data according to claim 1, characterized in that: The step of calculating the direction angle of the road section according to the starting point coordinates and the end point coordinates of the road section includes: If the calculated result is a negative value, add 360° to keep the direction angle range from 0° to 360°; Set the maximum allowable deviation distance of the road segment. When the road segment deviates from its straight line segment by more than the deviation distance, it will be split and the direction angle of each straight line segment will be calculated separately.

4. The traffic congestion identification method based on map coordinates and vehicle big data according to claim 1, characterized in that: The step of obtaining the distance from each capture device to the road section includes: The distance D from the capture device P to the road section AB is calculated based on the relationship between the capture device P and the two endpoints A and B of the road section AB: like , then the angle between AP and BP is an obtuse angle, and the distance D is the vertical distance from the capture device P to the road section AB; like , then the angle between AP and AB is an obtuse angle, and the distance D is the distance between AP; like , then the angle between AB and BP is an obtuse angle, and the distance D is the distance of BP.

5. The traffic congestion identification method based on map coordinates and vehicle big data according to claim 1, characterized in that: The step of obtaining the distance from each capture device to the road section includes: Use GeoHash encoding to convert the coordinates of the captured device into a string hash value, and group the captured devices according to the first few digits of the GeoHash code; Filter out the capture devices grouped near the road section and calculate the distances of the capture devices in the group; The method for determining whether the capturing direction is consistent with the driving direction of the vehicle on the road section is as follows: Define the angle threshold between the capture direction and the road section direction angle, and set the angle = abs(capture direction - road section direction angle). If the angle is greater than 180°, then the angle = 360° - angle. If the angle is within the range of ±30°, it is determined that the capture direction matches the direction angle of the road section, that is, it is consistent with the driving direction of the vehicle on the road section.

6. The traffic congestion identification method based on map coordinates and vehicle big data according to claim 1, characterized in that: The step of calculating the distance between the device to which the road section belongs and the starting point of the road section includes: Calculate the perpendicular point from the equipment belonging to the road section to the road section, use the cumulative distance from the perpendicular point to the starting point of the road section as the position parameter of the equipment belonging to the road section on the road section, and sort the equipment belonging to the road section according to this position parameter.

7. The traffic congestion identification method based on map coordinates and vehicle big data according to claim 1, characterized in that: The step of obtaining the travel time and travel speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route includes: Obtain all vehicle data collected by the capture devices along the trajectory route; Query the passing data of the first and last capture devices on each vehicle's passing trajectory; Calculate the travel speed of all vehicles on the trajectory route based on the travel time and road segment distance.

8. A traffic congestion identification device based on map coordinates and vehicle big data, characterized in that: The device comprises: A road segment acquisition module is used to obtain the vehicle's overall route, which is divided into multiple road segments based on the selected points on the map; and calculate the direction angle of the road segment and the vehicle's driving direction on the road segment based on the starting and ending coordinates of the road segment; The device acquisition module is used to acquire all the capture devices on the road section, and obtain the distance of each capture device to the road section and its capture direction; the capture device whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicle on the road section is determined as the road section belonging device; The trajectory acquisition module is used to calculate the distance between the equipment belonging to the road section and the starting point of the road section, and sort the order of all equipment belonging to the road section in ascending order of distance to obtain the trajectory route of the vehicle traveling on the road section; The congestion judgment module is used to obtain the driving time and speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and determine that the road section is congested when the vehicle's driving speed is less than a first threshold and the proportion of vehicles with a driving speed less than the first threshold is greater than a second threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the traffic congestion determination method based on map coordinates and vehicle big data described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the traffic congestion determination method based on map coordinates and vehicle big data described in any one of claims 1 to 7 are implemented.

Images