A control method and system based on congestion backtracking

Abstract

The present invention provides a control method and system based on congestion backtracking. The method comprises: obtaining vehicle information at a congestion point; using the vehicle information to construct an impact intensity model of the congestion point to determine the congestion impact intensity value of any path; and using the congestion impact intensity value to guide vehicles at the congestion point and determine the road section for traffic relief. The present invention backtracks vehicle trajectories at the congestion point to analyze the spatiotemporal distribution of vehicle congestion. Simultaneously, the spatial correlation between upstream intersections and road sections and the congestion point is combined to analyze the intersections and road sections that influence the congestion point. Based on the degree of their impact, the method then refines the dissemination of guidance information and implements targeted and precise control of traffic signal control strategies, thereby slowing the congestion of downstream vehicles.

Description

Technical Field

[0001] The present invention relates to the field of urban traffic information technology, and in particular to a control method and system based on congestion backtracking. Background Art

[0002] With the increase in the number of cars, the problem of urban traffic congestion has become increasingly prominent. In addition, since urban commercial, scenic, medical and other functional areas are relatively separated and independent, and the travel time, travel date and spatial distribution of traffic volume have a strong correlation, traffic congestion is more likely to occur on some roads during key periods and key periods. The congestion is characterized by rapid occurrence and long duration, accompanied by a high incidence of accidents and disorder, which wastes a lot of travelers' time and reduces the operating efficiency of the transportation system.

[0003] At present, the industry's means and technologies for identifying traffic congestion points are relatively mature. For example, calculations are performed through floating vehicle data, calculations are performed using vehicle navigation speed data, or calculations are performed through smart terminal devices based on the collected results of vehicle driving speed and regional average driving speed. With the development of intelligent equipment, a large number of traffic flow collection devices themselves already have the ability to identify road queues and traffic congestion. When a congestion point is formed, how to avoid the worsening of congestion at the congestion point and the pressure on the traffic flow at the fast link congestion point, and restore traffic to normal traffic conditions are the difficulties currently faced by cities. Existing technologies provide drivers with road congestion information ahead through variable induction screens or dynamic information signs, but such information is less targeted and is unlikely to play a significant role in avoiding worsening congestion. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a control method and system based on congestion backtracking to solve the problem in the prior art that road congestion information provided by signs to drivers is not targeted enough.

[0005] According to the first aspect, an embodiment of the present invention provides a control method based on congestion backtracking, including: obtaining vehicle information of a congestion point; using the vehicle information to construct an impact intensity model of the congestion point to determine the congestion impact intensity value of any path; using the congestion impact intensity value to guide the vehicles at the congestion point to determine the relief section.

[0006] Optionally, the use of the vehicle information to construct the impact intensity model of the congestion point includes: using the vehicle information to construct a trajectory back-tracing path of the congestion point; constructing the impact intensity model of the congestion point based on the trajectory back-tracing path, and the impact intensity model is a model of the congestion impact intensity of vehicles on each different trajectory back-tracing path upstream of the congestion point.

[0007] Optionally, the use of the vehicle information to construct the trajectory backtracking path of the congestion point includes: the vehicle information includes license plate information, source intersection and source road section, and the license plate information, source intersection and source road section are used to calculate the traffic flow information within a preset backtracking time; and the trajectory backtracking path of the congestion point is determined based on the traffic flow information.

[0008] Optionally, the constructing of the impact intensity model of the congestion point based on the trajectory backtracking path includes: calculating the turning flow ratio of the traffic volume on the backtracking path at each level of intersection according to the preset intersection hierarchy, so as to determine the section traffic flow impact intensity model; judging whether each of the intersections is adjacent, and when the intersections are adjacent, determining the intersection correlation impact intensity model of each of the intersections according to the distance between the adjacent intersections; determining the impact intensity model of any path congestion point based on the section traffic flow impact intensity model and the intersection correlation impact intensity model.

[0009] Optionally, the road section traffic flow impact intensity model is expressed by the following formula:

[0010]

[0011] Among them, T n Indicates the traffic flow impact intensity of a road section; n indicates the level of the downstream intersection directly connected to the road section; f n Indicates the proportion of traffic on this road section that focuses on turning;

[0012] The intersection correlation impact intensity model is expressed by the following formula:

[0013]

[0014] Among them, C n Indicates the impact strength of intersection correlation; f(n) represents the distance correction coefficient between the n-th level intersection and its connected n-1-th level intersection, where the correction coefficient is defined as follows:

[0015]

[0016] Among them, L n Indicates the distance between the current intersection and its downstream intersection.

[0017] The impact intensity model is expressed by the following formula:

[0018]

[0019] E n represents the impact intensity of path congestion, T nIndicates the traffic flow impact intensity of a road section; n indicates the level of the downstream intersection directly connected to the road section; C n Indicates the impact strength of intersection correlation.

[0020] Optionally, the use of the congestion impact intensity value to guide the vehicles at the congestion point and determine the relief section includes: determining the congestion impact intensity value of intersections at all levels with the congestion point as the center; guiding the vehicles at the congestion point according to the corresponding congestion impact intensity value and determining the relief section.

[0021] Optionally, the vehicles at the congestion point are guided according to the corresponding congestion impact intensity value to determine the relief section, including: judging whether the current congestion impact intensity value is lower than a preset value; when the current congestion impact intensity value is not lower than the preset value, returning to judge whether the congestion impact intensity value of the next level of the current congestion impact intensity value is lower than the preset value; when the current congestion impact intensity value is lower than the preset value, determining that information induction can be set at the current level intersection and its upstream sections and intersections, and determining them as relief sections.

[0022] According to the second aspect, an embodiment of the present invention provides a control system based on congestion backtracking, including: an acquisition module for acquiring vehicle information at a congestion point; a model construction module for using the vehicle information to construct an impact intensity model of the congestion point to determine the congestion impact intensity value of any path; and a guidance module for using the congestion impact intensity value to guide the vehicles at the congestion point to determine the relief section.

[0023] An embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, they implement the congestion backtracking-based control method described in the first aspect of the present invention and any optional method.

[0024] An embodiment of the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the congestion backtracking-based control method described in the first aspect of the present invention and any optional method by executing the computer instructions.

[0025] The technical solution of the present invention has the following advantages:

[0026] An embodiment of the present invention provides a control method and system based on congestion backtracking, which backtracks the vehicle trajectories at the congestion point, analyzes the spatiotemporal distribution of vehicle gathering, and combines the spatial correlation between upstream intersections, road sections and congestion points to analyze the intersections and road sections that affect the congestion point, and performs refined release of induction information and precise regulation of traffic signal control strategies according to the degree of their influence, thereby slowing down the speed of downstream vehicle gathering; and after congestion is formed, based on the start time of the congestion, moves forward for a period of time, and takes the congestion point as the end point of the journey, based on the vehicle trajectory, backtracks and analyzes the source distribution ratio of vehicles causing congestion at the upstream intersections and upstream connected road sections of the congestion point, determines the intersections and road sections that need to be controlled, and forms targeted driving direction induction information release and short-term signal control adjustment for different intersections and road sections, thereby reducing the gathering of vehicles at the downstream congestion point and avoiding the continuous aggravation of congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a flow chart of a control method based on congestion backtracking in an embodiment of the present invention;

[0029] Figure 2 This is a vehicle streamline collection diagram in an embodiment of the present invention;

[0030] Figure 3 A diagram showing the proportion of traffic turning at various intersections in an embodiment of the present invention;

[0031] Figure 4 A vehicle turning diagram for a multi-level intersection based on traffic flow backtracking in an embodiment of the present invention;

[0032] Figure 5 Schematic diagram of a control system based on congestion backtracking in an embodiment of the present invention;

[0033] Figure 6 Schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0035] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0036] An embodiment of the present invention provides a control method based on congestion backtracking. By tracing back the vehicle trajectories at the congestion point, the temporal and spatial distribution of vehicle gathering is analyzed. At the same time, the spatial correlation between upstream intersections, road sections and congestion points is combined to analyze the intersections and road sections that affect the congestion point. According to the degree of their influence, the induction information is refined and the traffic signal control strategy is precisely adjusted, thereby slowing down the speed of downstream vehicle gathering.

[0037] After a congestion forms, the system uses the congestion start time as a benchmark and moves forward a period of time (the forward time can be determined based on business needs) to trace the source of vehicles passing through the congestion point. This yields a set of vehicle trajectories containing detailed information such as license plates, spatial order of roads traveled, and time sequence of vehicle travel. With the congestion point as the end point, the system uses vehicle trajectories to retrospectively analyze the distribution ratio of vehicles that caused the congestion at upstream intersections and connected sections. This retrospective analysis process can be extended to multiple levels of upstream intersections or sections. When tracing back upstream at each level, the source distribution ratio at that level can be used to determine the key intersections or sections to trace back.

[0038] Vehicle trajectory tracing through license plate recognition accurately reflects the source road section and upstream intersection turns of vehicles on the current road section, providing accurate data for calculating the source road section and intersection traffic direction distribution of vehicles at congestion points. Based on the vehicle source distribution, combined with traffic flow characteristics and the spatial relationship between upstream intersections and road sections and congestion points, a congestion impact intensity model is constructed for the roads upstream of the congestion point, identifying one or more intersections and road sections with the highest impact.

[0039] After determining the intersections and road sections that need to be controlled, the traffic signal control strategy and the information display content of the road section guidance screen are adjusted according to the required degree of traffic control. Targeted driving direction guidance information and short-term signal control adjustments are made for different intersections and road sections to reduce the gathering of vehicles at downstream congestion points and avoid the continuous aggravation of congestion.

[0040] Specifically, if Figure 1 As shown, the control method based on congestion backtracking specifically includes:

[0041] Step S1: Obtain vehicle information at the congestion point. In practical applications, vehicle information refers to specific information about vehicles at the congestion point, including license plate information, source intersection, and source road section. The specific data acquisition process can be performed using existing technologies and equipment, and this embodiment is not limited thereto.

[0042] Step S2: Use vehicle information to construct a congestion point impact intensity model to determine the congestion impact intensity value of any path. The congestion impact intensity of any path is calculated as the congestion impact intensity of the traffic flow backtracking path ending at the n-level road segment on the accident point.

[0043] Specifically, the above step S2 further includes the following steps:

[0044] Step S21: Constructing a trajectory backtracking path at a congestion point using vehicle information. In this embodiment, the short-term trajectory backtracking path of the vehicle at the congestion point is constructed using vehicle license plate data.

[0045] Specifically, the above step S21 includes the following steps:

[0046] Step S211: Calculate traffic flow information within a preset lookback time using license plate information, the source intersection, and the source road section. In this embodiment, intelligent terminals such as traffic checkpoints and integrated radar and vision devices in all directions of the intersection are used to enable real-time capture, recognition, and storage of license plate information of vehicles passing through the intersection. When a traffic jam occurs, users can set a lookback time range based on business needs. For example, they can set a lookback time range for vehicles passing through the jam point within two hours before the jam occurred. (In theory, a larger sample size will more accurately characterize traffic flow distribution characteristics and upstream flow characteristics through vehicle lookback.)

[0047] According to the vehicle license plate, the vehicle source intersection and source road section are traced back to form a vehicle flow line collection map as shown below Figure 2 As shown in the figure, the thickness of the streamlines represents the amount of traffic flow, and the intensity of traffic sources on different roads is intuitively displayed.

[0048] Step S212: Determine the trajectory backtracking path of the congestion point based on the traffic flow information.

[0049] In this embodiment, based on the acquired vehicle trajectory data, the turning structure of the traffic flow passing through the congestion point at the upstream intersection during the analysis period is split, such as Figure 2 As shown, this intersection is the upstream intersection directly connected to the driving direction of the congestion point (i.e. Figure 2For intersection a), the upstream intersection directly connected to the congestion point section is defined as a first-level intersection, and the downstream section connected to the first-level intersection is a first-level section; following the backtracking trajectory, the three roads connected to the first-level intersection are respectively connected to the three intersections in the outer layer (i.e., intersection b, intersection c, and intersection d), which are defined as second-level intersections. Similarly, intersections e, intersection f, intersection g, intersection h, and intersection i connected to the second-level intersection are defined as third-level intersections, and so on, forming a multi-level intersection set centered on the congestion point.

[0050] Step S22: constructing an impact intensity model of the congestion point according to the trajectory backtracking path. The impact intensity model is a model of the congestion impact intensity of vehicles collected along different trajectory backtracking paths upstream of the congestion point.

[0051] Specifically, the above step S22 includes the following steps:

[0052] Step S221: Calculating the turning flow ratio of traffic volume on the backtracking path at each level of intersection according to the preset intersection hierarchy, so as to determine the road section traffic flow impact intensity model;

[0053] Among them, according to the intersection level, the proportion of the turning flow of the retrospective traffic volume at each level of intersection is calculated step by step. Figure 3 Taking the first-level intersection as an example, based on the traffic flow backtracking results, the proportion of vehicles merging into the road section where the congestion point is located is calculated according to different directions. The left-turning, straight-going, and right-turning traffic volumes account for 10%, 70%, and 20% of the merging vehicles respectively.

[0054] From the flow inflow structure, we can intuitively see which flow direction contributes more to the traffic flow of the downstream section, that is, the flow direction that may have the main impact on the aggravation of downstream traffic congestion. For the flow direction with the main impact, we continue to analyze the flow diversion ratio of vehicles in the main flow direction at the upstream intersection. Similarly, we continue to calculate the diversion flow ratio at the secondary and tertiary intersections, forming a multi-level intersection vehicle diversion map based on traffic flow backtracking, such as Figure 4 The determination of the flow direction that has the most impact can be determined based on regional suitability, such as the size of the intersection, the number of vehicles in the city, and the level of congestion. For non-major impact flow sections and upstream intersections, the vehicle flow calculation for the next-level intersection can be omitted. The determination of the main flow direction can be set based on actual management needs.

[0055] The road traffic flow impact intensity model is expressed by the following formula:

[0056]

[0057] Among them, T n Indicates the traffic flow impact intensity of a road section; n indicates the level of the downstream intersection directly connected to the road section; f nIndicates the proportion of traffic flow on this road section that focuses on turning. At this point, through vehicle flow analysis, the traffic flow impact intensity of any road source of traffic at the congestion point can be calculated, and the key control path can be derived from the perspective of traffic flow influencing factors.

[0058] Step S222: Determine whether the intersections are adjacent. If the intersections are adjacent, determine the intersection correlation influence strength model of each intersection based on the distance between the adjacent intersections.

[0059] Whether intersections are adjacent and the distance between them are key factors in determining the strength of intersection correlation. Historical research shows that non-adjacent intersections are less correlated than adjacent intersections, and that intersections with long distances between them are less correlated than those with short distances.

[0060] The intersection correlation is selected to ensure the spatial correlation influencing factors. The correlation includes whether the intersections are adjacent and the distance between adjacent intersections. The intersection correlation influence intensity model is expressed by the following formula:

[0061]

[0062] Among them, C n represents the impact strength of intersection correlation; f(n) represents the distance correction coefficient between the n-th level intersection and its connected n-1-th level intersection. Based on the research results of the industry on the coordinated control of adjacent intersections, with 800 meters as the boundary, the correction coefficient values are defined as follows:

[0063]

[0064] Among them, L n Indicates the distance between the current intersection and its downstream intersection;

[0065] Step S223: Determine the impact intensity model of any congestion point on a path based on the road section traffic flow impact intensity model and the intersection correlation impact intensity model.

[0066] The impact intensity model is expressed by the following formula:

[0067]

[0068] E n represents the impact intensity of path congestion, T n Indicates the traffic flow impact intensity of a road section; n indicates the level of the downstream intersection directly connected to the road section; C n Indicates the strength of intersection correlation. The congestion intensity of a route is calculated by summing the values of each road segment after correcting for the spatial correlation effect.

[0069] Step S3: Use the congestion impact intensity value to guide vehicles at the congestion point and determine the route for traffic diversion. The starting point for control and guidance should be selected on a relatively good upstream road section to avoid the creation of new congestion points due to control. The above steps have already calculated the congestion impact of any route. Combined with the traffic congestion situation (mainly represented by the traffic congestion index), this helps determine the starting point for control and traffic guidance.

[0070] Specifically, the above step S3 further includes the following steps:

[0071] Step S31: Determine the congestion impact intensity values of intersections at all levels with the congestion point as the center.

[0072] Step S32: guiding vehicles at the congestion point according to the corresponding congestion impact intensity value and determining the road section for diversion;

[0073] Specifically, the above step S32 further includes the following steps:

[0074] Step S321: determining whether the current congestion impact intensity value is lower than a preset value;

[0075] Step S322: When the current congestion impact intensity value is not lower than the preset value, return to determine whether the next level of congestion impact intensity value of the current congestion impact intensity value is lower than the preset value;

[0076] Step S323: When the current congestion impact intensity value is lower than the preset value, it is determined that information guidance can be set at the current level intersection and its upstream sections and intersections to determine them as relief sections.

[0077] After determining the induction or control starting point, based on the key turn analysis results of the main path, the signal control timing plan is used to delay the key turn phase or appropriately shorten the green light time of the corresponding phase to reduce the convergence of vehicles downstream. When adjusting the signal control plan, it is necessary to consider the phase sequence and time of one or more signal control phases adjacent to this intersection to avoid adding new congestion bottlenecks. The formulation of control strategies and the issuance of plans are implemented through the signal control platform. In conjunction with the adjustment of the signal control plan, the roadside induction screen realizes the notification and release of downstream congestion, signal control adjustments and induction information. The configuration of the display content of the induction screen is achieved through the induction screen management system.

[0078] In a specific embodiment, the following scenario is set as an example. The short-term trajectory backtracking result determined by the above method is formed based on the traffic flow diversion conditions at each upstream intersection formed during the short-term backtracking process of the traffic flow passing through the congestion point.

[0079] To calculate the traffic flow impact, we first identify the main flow direction at the intersection. In this case, we use a gradient approach to identify the main flow direction. The rules are as follows:

[0080] ① All flow sources of vehicles at the first-level intersection are main flow directions;

[0081] ② The vehicle flow ratio of the secondary intersection exceeds 30% and is the main flow direction;

[0082] ③ The vehicle flow ratio of the third-level and above intersections exceeds 40% and is the main flow direction;

[0083] According to the calculation formula in the above method, the traffic flow impact intensity of the seven paths is calculated as follows (the calculation results are limited to the intersections and road network ranges identified in the case, and the values are rounded to 3 decimal places):

[0084] Table 1 Results of traffic flow impact intensity of the path

[0085]

[0086] Calculate the spatial correlation effect. According to the road network structure, the distance between adjacent intersections in this road network is less than 800 meters. Therefore, in this calculation, f(n) = 1. According to the calculation formula in the above method, no matter which level the intersection is in terms of spatial attributes, C n =1.

[0087] Calculate the path congestion intensity. Based on the results of the above steps, calculate the path congestion impact intensity of each path as shown in the following table (calculated values are rounded to 2 decimal places):

[0088] Table 2 Impact intensity of path congestion

[0089] ① ② ③ ④ ⑤ ⑥ ⑦ E 4.15 3.00 3.10 3.10 12.94 11.08 5.52

[0090] According to the results of path congestion intensity, it can be seen that paths ⑤ and ⑥ are the main paths causing congestion.

[0091] Specifically, key guidance sections and signal-controlled intersection starting points are identified and configured. Based on the main routes and superimposed on traffic flow trends, the traffic flow conditions of the routes can be determined. Based on the two tables and analysis above, it can be concluded that traffic congestion is severe at Level 1, 2, and 4 intersections on Main Path ⑤, while traffic flow at Level 3 intersections is relatively good. Traffic congestion is severe at Level 1, 2, and 3 intersections on Path ⑥, while traffic flow at Level 4 and 5 intersections is relatively good.

[0092] Based on the control and guidance principles to avoid increasing congestion points, and taking traffic conditions as a reference, the system can output the names of road sections with a traffic index rating higher than the mild congestion index, which serve as the starting points for road section control and intersection signal control. Furthermore, the first, second, and fourth-level intersections on Path ⑤ can be used as the primary intersections for implementing intersection signal control measures, with the fourth-level section on Path ⑤ serving as the starting point for traffic guidance. The first, second, third, fourth, and fifth-level intersections on Path ⑥ can be used as the primary intersections for implementing intersection signal control measures, with the fifth-level section on Path ⑤ serving as the starting point for traffic guidance (or tracing back one level upstream based on the fourth-level intersection on Path ⑤ and the fifth-level intersection on Path ⑥). The primary control direction on Path ⑤ is straight south to north; the primary control direction on Path ⑥ is straight east to west.

[0093] Specifically, starting point vehicles are controlled or guided to pass through, with primary control paths and directions determined. Traffic congestion can be reduced by adjusting the through-traffic phase timing at corresponding intersections or by coordinating control with upstream and downstream intersections. Downstream congestion conditions, along with signal adjustment strategies and primary control directions, are displayed on information guidance screens along the road section. For example, a message like "The green light time for through traffic at the next intersection will be reduced" is displayed to encourage through-traffic vehicles to choose alternative routes.

[0094] Through the above steps S1 to S3, the control method based on congestion backtracking provided by this embodiment traces back the vehicle trajectories at the congestion point, analyzes the spatiotemporal distribution of vehicle gathering, and combines the spatial correlation between upstream intersections, road sections and congestion points to analyze the intersections and road sections that have an impact on the congestion point, and performs refined release of guidance information and precise regulation of traffic signal control strategies according to the degree of their impact, thereby slowing down the speed of downstream vehicle gathering; and after congestion is formed, based on the start time of the congestion, moving forward for a period of time, with the congestion point as the end point of the journey, based on the vehicle trajectories, backtracking and analyzing the source distribution ratio of vehicles causing congestion at the upstream intersections and upstream connected road sections of the congestion point, determining the intersections and road sections that need to be controlled, and forming targeted driving direction guidance information release and signal control short-term adjustment for different intersections and road sections, thereby reducing the gathering of vehicles at the downstream congestion point and avoiding the continuous aggravation of congestion.

[0095] The embodiment of the present invention also provides a control system based on congestion backtracking, such as Figure 5 Shown, including:

[0096] The acquisition module 1 is used to obtain vehicle information at the congestion point; for details, please refer to the relevant description of step S1 in the above method embodiment.

[0097] The model building module 2 is used to build an impact intensity model of the congestion point using vehicle information to determine the congestion impact intensity value of any path; for details, please refer to the relevant description of step S2 in the above method embodiment.

[0098] The guidance module 3 is used to guide vehicles at the congestion point using the congestion impact intensity value and determine the diversion section; for details, please refer to the relevant description of step S3 in the above method embodiment.

[0099] 1. Refined Guidance and Control System Based on Vehicle Trajectory Backtracking at Congestion Points (hereinafter referred to as the System)

[0100] The map reproduction of vehicle trajectories at congestion points and the distribution of traffic volume at upstream roads and intersections at congestion points can be achieved, and the traffic operation situation can be superimposed to provide a basis for selecting traffic guidance sections and traffic control intersections.

[0101] Real-time optimization of signal control schemes for key intersections is achieved. For key intersections, based on the identified starting point (or points) for signal control, the signal control scheme is adjusted within the set control timeframe to reduce the number of vehicles in a specific direction and slow the flow of vehicles downstream, taking into account the basic traffic needs of traffic in other directions.

[0102] Real-time control of information guidance screens on key roads is achieved. In conjunction with the aforementioned important intersections, refined guidance information is released on information guidance screens on key roads connected to the controlled intersections. Highly targeted driving information services are provided based on the impact of downstream congestion points, traffic volume, and key traffic diversions. The information released by the guidance screens, including their duration, can be configured through the system.

[0103] The embodiment of the present invention further provides an electronic device, such as Figure 6 As shown, the electronic device may include a processor 901 and a memory 902, wherein the processor 901 and the memory 902 may be connected via a bus or other means. Figure 6 The bus connection is taken as an example.

[0104] The processor 901 may be a central processing unit (CPU). The processor 901 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0105] Memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of the present invention. Processor 901 executes the non-transitory software programs, instructions, and modules stored in memory 902 to perform various processor functions and data processing, thereby implementing the aforementioned methods.

[0106] The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created by the processor 901, etc. In addition, the memory 902 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 902 may optionally include a memory remotely located relative to the processor 901, and these remote memories may be connected to the processor 901 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0107] One or more modules are stored in the memory 902 and, when executed by the processor 901 , perform the above method.

[0108] The specific details of the above electronic device can be understood by referring to the corresponding descriptions and effects in the above method embodiments, and will not be repeated here.

[0109] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The storage medium can also include a combination of the above-mentioned types of memory.

[0110] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A control method based on congestion backtracking, characterized in that: include: Obtain vehicle information at congestion points; constructing an impact intensity model of the congestion point using the vehicle information to determine a congestion impact intensity value of any path; Using the congestion impact intensity value to guide vehicles at the congestion point and determine a road section for diversion; The constructing the impact intensity model of the congestion point by using the vehicle information includes: Constructing a trajectory backtracking path of the congestion point using the vehicle information; Constructing an impact intensity model of the congestion point according to the trajectory backtracking path, wherein the impact intensity model is a model of the congestion impact intensity of vehicles aggregated along different trajectory backtracking paths upstream of the congestion point; The constructing of the impact intensity model of the congestion point according to the trajectory backtracking path includes: According to the preset intersection hierarchy, the turning flow ratio of the traffic volume on the backtracking path at each level of intersection is calculated step by step to determine the traffic flow impact intensity model of the road section; Determining whether the intersections are adjacent to each other, and when the intersections are adjacent to each other, determining an intersection correlation influence strength model for each intersection according to the distance between the adjacent intersections; Determine the impact intensity model of any congestion point on a path based on the road section traffic flow impact intensity model and the intersection correlation impact intensity model; The traffic flow impact intensity model of the road section is expressed by the following formula: Among them, T n Indicates the traffic flow impact intensity of a road section; n indicates the level of the downstream intersection directly connected to the road section; f n Indicates the proportion of traffic on this road section that focuses on turning; The intersection correlation impact intensity model is expressed by the following formula: Among them, C n Indicates the impact strength of intersection correlation; f(n) represents the distance correction coefficient between the n-th level intersection and its connected n-1-th level intersection, where the correction coefficient is defined as follows: Among them, L n Indicates the distance between the current intersection and its downstream intersection; The impact intensity model is expressed by the following formula: E n represents the impact intensity of path congestion, T n Indicates the traffic flow impact intensity of a road section; n indicates the level of the downstream intersection directly connected to the road section; C n Indicates the impact strength of intersection correlation.

2. The control method based on congestion backtracking according to claim 1, characterized in that: The method of constructing a trajectory backtracking path of the congestion point by using the vehicle information includes: the vehicle information includes license plate information, source intersection and source road section, and the license plate information, source intersection and source road section are used to calculate traffic flow information within a preset backtracking time; A trajectory backtracking path of the congestion point is determined according to the traffic flow information.

3. The control method based on congestion backtracking according to claim 1, characterized in that: The step of guiding vehicles at the congestion point by using the congestion impact intensity value to determine a road section for diversion includes: Taking the congestion point as the center, determining the congestion impact intensity value of each level of intersection; According to the corresponding congestion impact intensity value, the vehicles at the congestion point are guided and the relief road section is determined.

4. The control method based on congestion backtracking according to claim 3 is characterized in that: The step of guiding vehicles at the congestion point and determining a road section for diversion according to the corresponding congestion impact intensity value includes: Determining whether the current congestion impact intensity value is lower than a preset value; When the current congestion impact intensity value is not lower than the preset value, returning to determine whether the congestion impact intensity value of the next level below the current congestion impact intensity value is lower than the preset value; When the current congestion impact intensity value is lower than a preset value, it is determined that information guidance can be set at the current level intersection and its upstream sections and intersections to determine them as diversion sections.

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