CN117496726A

CN117496726A - Traffic control coordination method and system based on intelligent traffic system

Info

Publication number: CN117496726A
Application number: CN202311369199.0A
Authority: CN
Inventors: 郜栩; 吴泓锐; 张钦芃; 张桐硕; 孟可豪; 李哲安; 胡刘鹏; 王帅; 岳起正; 周浩舟; 曾德全; 张龙杰; 林建锋; 李伟泽; 谢伍彬
Original assignee: East China Jiaotong University
Current assignee: East China Jiaotong University
Priority date: 2023-10-20
Filing date: 2023-10-20
Publication date: 2024-02-02

Abstract

The application discloses a traffic control cooperative method and system based on an intelligent traffic system, relates to the technical field of traffic control, and aims to solve the technical problem that the control cooperative efficiency is low when the existing traffic system faces changeable traffic environments. The traffic control cooperative method is applied to a management center and comprises the following steps: acquiring real-time traffic data sent by sensing equipment; inputting the effective real-time traffic data to an algorithm center module so that the algorithm center module feeds back a traffic control cooperative strategy; and sending the traffic control cooperative strategy and the target real-time traffic data to a control center so that the control center deploys the intelligent traffic system based on the traffic control cooperative strategy.

Description

Traffic control coordination method and system based on intelligent traffic system

Technical Field

The application relates to the technical field of traffic control, in particular to a traffic control coordination method and system based on an intelligent traffic system.

Background

Because urban traffic environments are often very complex, including high density traffic, multi-modal traffic, uncertainty in traffic rules and road conditions, existing traffic systems may not adequately accommodate these complexities. Thus resulting in a lower control synergy efficiency.

Therefore, there is a need for a traffic control synergy method with high control synergy efficiency.

Disclosure of Invention

The application provides a traffic control cooperative method and system based on an intelligent traffic system, and aims to solve the technical problem that the control cooperative efficiency is low when the existing traffic system faces changeable traffic environments.

In order to solve the above technical problems, embodiments of the present application provide: a traffic control cooperative method based on an intelligent traffic system is applied to a management center and comprises the following steps:

acquiring real-time traffic data sent by sensing equipment; wherein the real-time traffic data includes traffic flow data, vehicle position data, vehicle speed data, and road condition data; preprocessing the real-time traffic data to obtain target real-time traffic data;

inputting the effective real-time traffic data to an algorithm center module so that the algorithm center module feeds back a traffic control cooperative strategy; wherein the traffic control cooperative strategy comprises an optimized route strategy and a time sequence adjustment strategy;

and sending the traffic control cooperative strategy and the target real-time traffic data to a control center so that the control center deploys the intelligent traffic system based on the traffic control cooperative strategy.

As some optional embodiments of the present application, the optimized route policy includes an optimized route policy of the vehicle;

the time sequence adjustment strategy comprises a periodical time sequence strategy of the signal lamp, a phase time sequence strategy of the signal lamp or a signal lamp time sequence adjustment strategy of different intersections.

In order to solve the above technical problems, the embodiment of the present application further provides: the traffic control cooperative method based on the intelligent traffic system is characterized by being applied to an algorithm center and comprising the following steps of:

receiving effective real-time traffic data sent by the management center;

analyzing the effective real-time traffic data to obtain traffic jam early warning information; the traffic congestion early warning information comprises current traffic congestion information and future traffic congestion information;

outputting a traffic control cooperative strategy based on the traffic jam early warning information; wherein the traffic control cooperative strategy comprises an optimized route strategy and a time sequence adjustment strategy;

and sending the traffic control collaborative strategy to a management center.

As some optional embodiments of the present application, the analyzing the effective real-time traffic data to obtain traffic congestion warning information includes:

Inputting the effective real-time traffic data into a preset microscopic traffic model to obtain first current traffic congestion information; inputting the effective real-time traffic data into a preset macroscopic traffic model to obtain second current traffic jam information; acquiring current traffic congestion information based on the first current traffic congestion information and the second current traffic congestion information; the first current traffic congestion information comprises running speed information of an individual vehicle, lane change information of the individual vehicle and parking information of the individual vehicle; the second current traffic congestion situation comprises overall traffic flow information and overall traffic speed information;

inputting the effective real-time traffic data into a preset time sequence analysis model to obtain future traffic jam information;

and obtaining traffic jam early warning information based on the current traffic jam information and the future traffic jam information.

As some optional embodiments of the application, the preset microscopic traffic model is a Cellular Automaton model or a SUMO model; wherein each cell in the Cellular Automaton model represents a road, and vehicles in each cell are more than or equal to 0;

The preset macroscopic traffic model is a lightill-Whitham-Richards model or a Cell Transmission Model model;

the preset time sequence analysis model is an LSTM deep learning model.

As some optional embodiments of the present application, the obtaining traffic congestion early-warning information based on the current traffic congestion information and the future traffic congestion information includes:

inputting the current traffic congestion information and the future traffic congestion information into a congestion prediction model to obtain a congestion prediction value;

judging whether the congestion prediction value triggers a congestion alarm threshold value or not; and if the traffic jam early warning information is triggered, outputting traffic jam early warning information.

In order to solve the above technical problems, the embodiment of the present application further provides: the traffic control cooperative method based on the intelligent traffic system is characterized by being applied to a control center and comprising the following steps of:

receiving traffic control collaborative strategies and target real-time traffic data sent by the management center;

analyzing and verifying the traffic control cooperative strategy and the target real-time traffic data to obtain an effective traffic control cooperative strategy and effective real-time traffic data;

and deploying and executing based on the effective traffic control cooperative strategy and the effective real-time traffic data.

As some optional embodiments of the present application, the deploying and executing based on the effective traffic control cooperative policy and the effective real-time traffic data includes:

acquiring a time sequence adjustment strategy based on the effective traffic control cooperative strategy; the time sequence adjustment strategy comprises a periodic time sequence strategy of a signal lamp, a phase time sequence strategy of the signal lamp or signal lamp time sequence adjustment strategies of different intersections; generating a timing adjustment instruction based on the timing adjustment strategy; the time sequence adjustment instruction is sent to a signal controller, so that the signal controller changes the operation mode and time sequence of the signal lamp;

based on the effective real-time traffic data, obtaining traffic event information and current congestion information; obtaining an optimized route strategy based on traffic event information and the current congestion information; and sending the optimized route strategy to a user client so that the user client outputs the optimal route information of the vehicle to a user.

In order to solve the above technical problems, the embodiment of the present application further provides: a traffic control collaboration system based on an intelligent transportation system, comprising:

the management center is used for acquiring real-time traffic data sent by the sensing equipment; wherein the real-time traffic data includes traffic flow data, vehicle position data, vehicle speed data, and road condition data; preprocessing the real-time traffic data to obtain target real-time traffic data; inputting the effective real-time traffic data to an algorithm center module so that the algorithm center module feeds back a traffic control cooperative strategy; wherein the traffic control cooperative strategy comprises an optimized route strategy and a time sequence adjustment strategy; the traffic control cooperative strategy and the target real-time traffic data are sent to a control center, so that the control center deploys the intelligent traffic system based on the traffic control cooperative strategy;

The algorithm center is used for receiving the effective real-time traffic data sent by the management center; analyzing the effective real-time traffic data to obtain traffic jam early warning information; the traffic congestion early warning information comprises current traffic congestion information and future traffic congestion information; outputting a traffic control cooperative strategy based on the traffic jam early warning information; wherein the traffic control cooperative strategy comprises an optimized route strategy and a time sequence adjustment strategy; the traffic control collaborative strategy is sent to a management center;

the control center is used for receiving the traffic control cooperative strategy and the target real-time traffic data sent by the management center; analyzing and verifying the traffic control cooperative strategy and the target real-time traffic data to obtain an effective traffic control cooperative strategy and effective real-time traffic data; deploying and executing based on the effective traffic control cooperative policy and the effective real-time traffic data;

and the data transmission module is used for carrying out data transmission among the management center, the algorithm center and the control center.

As some optional embodiments of the present application, the traffic control cooperative system based on the intelligent traffic system further includes:

The sensing equipment is used for collecting real-time traffic data;

the signal controller is used for receiving and executing the time sequence adjustment instruction sent by the control center;

and the user client is used for receiving the optimized route strategy sent by the control center and outputting the optimized route information of the vehicle to the user.

Compared with the prior art, the traffic control collaborative method based on the intelligent traffic system has the advantages that the real-time traffic data sent by the sensing equipment is used as a calculation optimization basis, so that the reliability of the traffic data is improved, the position, the speed, the flow and the like of the vehicle can be accurately monitored, and the inaccuracy of the data is reduced. In addition, the method and the device ensure the accuracy of subsequent calculation, so that after the original real-time traffic data is acquired, the original real-time traffic data is normalized, on one hand, abnormal values or other invalid data in the original data are removed, and on the other hand, characteristic information related to traffic jam is identified and extracted, so that the calculation efficiency of a subsequent algorithm center is improved. Furthermore, the effective real-time traffic data are input to the algorithm center module, so that the algorithm center module feeds back a traffic control cooperative strategy; the calculation efficiency in the case of complex traffic conditions is improved, the occurrence of congestion is reduced through the traffic control cooperative strategy output by the algorithm center, and the intelligent planning performance of route planning is improved. The existing traffic system is poor in data sharing performance and therefore congestion is often caused, so that the control center deploys the intelligent traffic system based on the traffic control cooperative strategy by sending the traffic control cooperative strategy and the target real-time traffic data to the control center; and then, a plurality of data sources are better integrated, and traffic signals, event responses and road condition release are cooperatively managed.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will make brief description of the drawings used in the description of the embodiments or the prior art. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.

FIG. 1 is a schematic diagram of a computer device architecture of a hardware operating environment referred to in the present application;

FIG. 2 is a schematic flow chart of a first traffic control cooperative method based on an intelligent traffic system in the present application;

FIG. 3 is a schematic flow chart of a second traffic control cooperative method based on an intelligent traffic system in the present application;

FIG. 4 is a schematic flow chart of a third traffic control cooperative method based on an intelligent traffic system in the present application;

fig. 5 is a schematic structural diagram of a traffic control cooperative system based on an intelligent traffic system according to the present application.

The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present application are merely used to explain the relative positional relationship between the components, the movement condition, and the like in a specific posture, and if the specific posture is changed, the directional indicator is correspondingly changed.

In the present application, unless explicitly specified and limited otherwise, the terms "coupled," "secured," and the like are to be construed broadly, and for example, "secured" may be either permanently attached or removably attached, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.

In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and/or" as it appears throughout includes three parallel schemes, for example "A and/or B", including the A scheme, or the B scheme, or the scheme where A and B are satisfied simultaneously. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a computer device of a hardware running environment according to an embodiment of the present invention, as shown in fig. 1, the computer device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.

Those skilled in the art will appreciate that the architecture shown in fig. 1 is not limiting of a computer device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.

As shown in fig. 1, the memory 1005, which is a storage medium, may include an operating system, a network communication module, a user interface module, and an electronic program, and may further include a data storage module.

In the computer device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the computer device of the present embodiment may be provided in the computer device, and the computer device invokes the traffic control coordination system based on the intelligent traffic system stored in the memory 1005 through the processor 1001, and executes the traffic control coordination method based on the intelligent traffic system provided in the present embodiment.

It should be noted that, the computer device may be an external hardware device capable of running independently, or may be a hardware device in the intelligent transportation system itself.

Referring to fig. 2, based on the foregoing hardware environment, the present embodiment further provides a traffic control coordination method based on an intelligent traffic system, which is applied to a management center, and includes the following steps:

step S10, acquiring real-time traffic data sent by sensing equipment; wherein the real-time traffic data includes traffic flow data, vehicle position data, vehicle speed data, and road condition data; and preprocessing the real-time traffic data to obtain target real-time traffic data.

It should be noted that the sensing device may be a sensing device such as a camera, a radar, a geomagnetic coil, a GPS receiver, etc. to monitor the position, the speed, and the flow of the vehicle.

It should be noted that, in the embodiment of the present application, after the real-time traffic data is obtained through the sensing device, the real-time traffic data is not directly applied to calculation prediction, but is first normalized or other preprocessed and then used, so as to ensure data consistency and comparability; and the abnormal value is deleted, so that the quality and the credibility of the data are improved. In particular, first, since different data sources and sensors may use different units of measure, resolution, and data formats, normalization may unify the data into the same unit of measure to ensure consistency and comparability of the data. Second, there may be outliers in the real-time traffic data, such as sensor failures, erroneous data inputs, or unusual conditions caused by an incident. Preprocessing may help detect and process outliers to improve the quality and reliability of the data. Third, intelligent transportation systems typically rely on multiple data sources, including traffic sensors, GPS devices, traffic cameras, etc., and normalization and preprocessing helps to fuse the data of these different data sources together to create a more comprehensive data set. Fourth, real-time traffic data may contain noise and fluctuations. By using smoothing techniques, noise in the data can be reduced, making it more suitable for analysis and decision making. Fifth, in machine learning and data analysis, data preprocessing typically includes feature engineering. This involves selecting, building and transforming data features to better capture key information of the data. Sixth, certain data preprocessing steps may reduce the computational complexity of subsequent analysis and decision making. For example, normalization may make certain computations more efficient. Seventh, in some cases, data preprocessing may be used for privacy protection. For example, specific location data may be obscured or desensitized to reduce individual identifiability. Eighth, data preprocessing helps to generate clearer data visualizations to facilitate human understanding of traffic conditions. Visualization is an important tool for traffic management and decision making. In summary, normalizing or other preprocessing of real-time traffic data helps to improve data quality, consistency, and availability, making it more suitable for use with intelligent traffic systems, machine learning models, and decision support systems, which are important steps to ensure data reliability and availability.

The traffic flow data refers to information related to the flow of vehicles, such as the number or speed of vehicles passing through a certain period of time, in a specific location or area on a road or a traffic network. Such data is typically used to monitor and analyze traffic conditions for traffic management, congestion monitoring, planning, and decision making. Traffic flow data generally includes the following several main types: vehicle count data, which records the number of vehicles passing through a certain point or area. It may include the total number of vehicles or may record different types of vehicles (cars, trucks, bicycles, etc.) separately. Vehicle speed data representing the running speed of the vehicle at a specific point or road. Such data may be used to monitor traffic flow, identify areas of congestion, and evaluate road conditions. Traffic flow data representing the number of vehicles passing through a point or area within a certain period of time. It is typically expressed in terms of number of vehicles per hour or day. Lane occupancy, which represents the proportion of lanes occupied by a vehicle on a road. This can be used to know the usage level and congestion level of the road. Traffic pattern data describing a driving pattern of the vehicle, such as acceleration, deceleration, parking, etc. This is helpful in analyzing the cause of traffic congestion and optimizing signal timing. Such data may be collected by various sensors, traffic cameras, GPS devices, and intelligent transportation systems. It can be seen that traffic flow data plays an important role in urban traffic management, road planning, congestion monitoring, real-time navigation and the like.

The vehicle position data refers to data for recording geographic position information of the vehicle at a specific time. Such data typically includes longitude and latitude coordinates of the vehicle, and may include information related to altitude, speed, direction, and time stamp. The collection and analysis of vehicle position data is of great importance to the fields of traffic management, navigation, vehicle tracking, geographic Information Systems (GIS) and the like. The following are some important aspects with respect to vehicle position data: data sources, vehicle location data may be collected in a variety of ways, including Global Positioning System (GPS) devices, vehicle sensors, traffic cameras, intelligent transportation systems, and mobile applications. Geographic coordinates, vehicle location data is typically stored in geographic coordinates, including Longitude (Longitude) and Latitude (Latitude); these coordinates can be used to precisely locate the position of the vehicle on the earth's surface. The speed and direction, vehicle position data may include the speed and direction of travel of the vehicle. The time stamp, each location data point including a time stamp indicating the exact time of data acquisition, facilitates analysis of the trajectory and behavior of the vehicle. Real-time tracking, vehicle position data may be used to track the current position and movement of the vehicle in real-time; this is very useful in fleet management, cargo tracking, and real-time navigation. A history track, which is a past moving path of the vehicle, can be recorded by the vehicle position data; this is very helpful in analyzing trip history, safety, and planned routes. Geographic Information Systems (GIS), vehicle location data may be integrated with Geographic Information Systems (GIS) to create traffic maps, analyze road networks, plan routes, and support decision-making. Privacy protection, which is an important consideration as vehicle location data relates to individual location information; it is critical that appropriate measures be taken to protect the privacy of the vehicle owner. It can be seen that vehicle location data plays a critical role in modern traffic management, fleet management, navigation applications, logistics and geographic information systems. They provide us with useful information about vehicles and traffic flow, helping to improve traffic efficiency and safety.

The vehicle speed data refers to information that records the traveling speed of the vehicle at a certain time or during a certain period of time. These data are typically expressed in kilometers per hour (km/h) or miles per hour (mph) for monitoring and analyzing the speed of movement of a vehicle over a roadway. Vehicle speed data is a key component of traffic management and road condition analysis. Such as: a data source, vehicle speed data may be collected in a variety of ways; such as using Global Positioning System (GPS) devices, traffic cameras, traffic sensors, and intelligent transportation systems; GPS devices are typically capable of providing real-time speed information of a vehicle. The real-time speed, vehicle speed data provides real-time speed of the vehicle on the road, which helps to monitor traffic flow, congestion conditions, and road conditions. Historical speed, in addition to real-time speed, vehicle speed data may also be used to record the speed history of the vehicle over a period of time; this is very useful for analyzing traffic congestion tendency, studying speed change of road sections, and the like. The average speed, vehicle speed data may also be used to calculate the average speed of the vehicle, typically over a period of time (e.g., per hour); the average speed data is useful for assessing the smoothness of road segments. Speed profile, in addition to average speed, vehicle speed data may also provide speed profile information; this includes speed ranges for different vehicles, thereby helping to understand the diversity of traffic. The speed limit comparison compares the actual speed of the vehicle with the speed limit on the road, helping to supervise driving violations and improve road safety. Congestion detection, vehicle speed data is often used for congestion detection; when the vehicle speed decreases significantly, it may be indicated that traffic congestion is present on the road. Navigation and route planning, vehicle speed data is critical to navigation applications and route planning, which facilitate selection of optimal routes to avoid congestion or save time. Real-time decision support, in an intelligent transportation system, vehicle speed data can be used for real-time decision support; for example, traffic flow may be improved by adjusting the signal timing based on real-time speed data. Vehicle speed data plays an important role in the fields of traffic management, road planning, congestion monitoring, navigation applications, and vehicle tracking. It can be seen that this data helps to improve traffic efficiency, reduce congestion and provide a better traffic experience.

It should be noted that the road condition data refers to information for describing and monitoring the current condition of a road or a traffic road section. Such data includes various information related to road status, traffic flow and safety, typically for traffic management, navigation, traffic decisions and information provision by road users. Such as: traffic congestion conditions, road condition data typically includes information about the degree of traffic congestion on a road. This may be represented by vehicle speed, vehicle density, or congestion level. The congestion data assists the driver in selecting a more unobstructed route. Road conditions, road condition data can describe conditions of the road surface, including road surface conditions, pits and road construction; this is important for planning road maintenance and repair works. Accident and road closure, the data may provide information about the accident and road closure, helping the driver avoid the affected road segments. Weather conditions, road condition data typically include information about the current weather conditions, such as rainfall, snow, hail, and visibility; this helps the driver to select an appropriate driving style according to weather. Road sign and landmark information, the road condition data may also include information about road signs, landmarks and traffic signals to aid navigation and route planning. Event information, road condition data may include information related to events on roads, such as sports games, road construction, and special events; this is important to both the driver and traffic manager. Road segment speeds, road condition data typically include speed information for individual road segments to assist navigation applications and traffic management systems in planning optimal routes. Updating in real time, wherein the road condition data is generally updated in real time to reflect the current condition and event; this can be achieved by sensors, traffic cameras, mobile applications and communication networks. User feedback, some road condition data may also include feedback information collected from road users (drivers or pedestrians), such as reporting traffic congestion or road problems. It can be seen that the road condition data plays a key role in modern traffic management, intelligent traffic systems, navigation applications, vehicle tracking and the like. They help to provide accurate traffic information and help drivers, traffic managers and road users make informed decisions.

Step S20, inputting the effective real-time traffic data into an algorithm center module so that the algorithm center module feeds back a traffic control cooperative strategy; wherein the traffic control cooperative policy includes an optimized route policy and a timing adjustment policy.

It should be noted that the optimized route policy includes an optimized route policy of the vehicle; the time sequence adjustment strategy comprises a periodical time sequence strategy of the signal lamp, a phase time sequence strategy of the signal lamp or a signal lamp time sequence adjustment strategy of different intersections.

Wherein, the optimized route strategy refers to selecting and planning the optimal driving route through intelligent algorithm and real-time data analysis, so as to provide higher efficiency, shorter travel time and less traffic jam. Such strategies may be used in traffic management, navigation applications, and intelligent traffic systems to assist drivers and traffic managers in making more intelligent route selections. The following are some detailed descriptions of the optimized route strategy: 1) Real-time traffic data: optimized route strategies rely on the collection and analysis of real-time traffic data. This includes information on the vehicle position, speed, congestion level, etc. on the road. Such data may come from traffic sensors, GPS devices, traffic cameras, and mobile applications. 2) Targets and constraints: route optimization strategies require explicit definition of targets and constraints. The objectives may include minimum time, minimum distance, minimum congestion, minimum fuel consumption, etc. Constraints may include speed limits for a particular road, vehicle type (e.g., truck or bicycle), etc. 3) Algorithm selection: an appropriate route optimization algorithm is selected, such as Dijkstra algorithm, a-algorithm, or a more complex dynamic path planning algorithm. These algorithms take into account the weights of the different road segments to calculate the best path. 4) Updating in real time: route optimization needs to be performed on the basis of real-time data, as traffic conditions may change over time. The algorithm needs to update route suggestions periodically to accommodate new traffic conditions. 5) Multimode traffic: the optimized route strategy may take into account a variety of traffic patterns including walking, bicycle, public transportation, and driving. This helps to provide comprehensive traffic options. 6) Navigation indication: once the optimal route is generated, the navigation application may provide detailed navigation instructions to the driver, including turn prompts, exit information, and time of arrival estimates. 7) User preferences: it is important to consider the user's preferences. Some drivers may be more focused on reaching the destination quickly, while others may be more focused on avoiding highways or on choosing an eco-route. 8) Interactivity: modern navigation applications are often interactive, allowing drivers to make route adjustments according to personal needs and preferences. This may include avoiding traffic accidents or selecting sightseeing. 9) Multi-level decision: in urban traffic systems, route optimization strategies may work in conjunction with signal light control and event management. This helps to reduce congestion and provide coordinated traffic flow. 10 Data privacy): data privacy is an important issue in optimizing route strategies. It is critical to ensure that the user's location data is protected and encrypted. The optimized route strategy helps drivers avoid congestion, save time and reduce fuel consumption by combining real-time data with intelligent algorithms. This helps to improve traffic efficiency and reduce urban congestion.

The periodic time sequence strategy of the signal lamp is a time allocation strategy for specifying the signal lamp to display different lamp colors (such as green lamp, yellow lamp and red lamp) in one period in traffic control. This period is commonly referred to as a signal period, and its length can be adjusted according to the characteristics of the road, the intersection, and the traffic demand. The method comprises the following important parameters: 1) The length of the signal period, which is an important parameter, is usually in seconds. The length of the cycle depends on the traffic flow of the road, the complexity of the intersection and the pedestrian traffic demand. Shorter periods may provide more frequent signal changes for high traffic density areas, while longer periods are suitable for areas with less traffic. 2) The green light period is the time the vehicle is allowed to pass in the green light period, signal period. The length of this period is typically determined based on traffic flow and road capacity. During peak hours, it may be desirable to allocate longer green times to accommodate more vehicles. 3) Yellow light duration, yellow light is usually used to indicate that the green light is about to change into red light, reminds the driver to slow down and stop. The duration of the yellow light is usually fixed to ensure safety. Longer yellow light durations help reduce traffic accidents. 4) Red light time period, red light time period indicates traffic flow need parking waiting. The duration of the red light period is typically small in the cycle to minimize vehicle waiting time. 5) Left-turn and straight-turn, in some cases, the signal period needs to take into account the time allocation of the left-turn and straight-turn. Typically, left turning requires additional time to ensure safety. 6) The design of the intersection, the periodic timing strategy of the signal lamp needs to be matched with the design of the intersection. Different types of intersections (T-intersections, X-intersections, ring intersections, etc.) may require different cycle strategies. 7) Modern traffic management systems typically have the ability to dynamically adjust the timing of signal cycles. According to the real-time traffic data, the system can adjust the periodic time sequence of the signal lamp so as to adapt to traffic demands of different time periods. 8) In cooperation, in an intelligent traffic system, the periodic timing strategy of the signal lamp can cooperate with other system elements, such as congestion prediction, traffic event response and the like. This helps to better manage traffic flow. That is, the periodic timing strategy of the signal lamp is critical to urban traffic management, which can affect traffic flow, traffic efficiency, and traffic safety.

The phase timing strategy of the signal lamp is to define time sequences of signal lamp display in different directions in traffic signal control so as to ensure smooth and safe traffic at intersections or crossings. The phase is a period of signal lamp control that defines which directions of the vehicle are allowed to pass. The following is a detailed description of the phase timing strategy of the signal lamp: 1) Phase definitions, in signal control, each phase represents a particular traffic behavior, such as straight, left turn, right turn. Each phase has a corresponding signal light configuration, i.e. which direction the signal light is on, and the timing of the green, yellow, red lights. 2) Phase timing, which is the timing of the signal lamps over a complete cycle. For example, a typical signal control period may include a straight-going phase, a left-turn phase, a straight-going phase, a right-turn phase, and the like. This timing may be repeated over different time periods. 3) A signal control period, which is one complete cycle of the signal phase timing. The length of the period is typically in seconds and includes the duration of each phase. 4) The phase duration, the duration of each phase, is an important parameter in the phase timing. The duration depends on traffic demand, road capacity and safety considerations. Typically, the straight phase requires a longer green light duration, while the left turn phase may require more time to accommodate the vehicle. 5) Coordination and synchronization phase timing requires coordination and synchronization to ensure traffic coordination between different intersections and signal lights. For example, a signal on a primary road may need to be synchronized with a signal on a secondary road to reduce traffic congestion. 6) Traffic demand is dynamically adjusted, and modern traffic management systems typically have the ability to dynamically adjust phase timing strategies. Based on the real-time traffic data and events, the system can adjust the phase timing to accommodate traffic demands for different time periods. 7) Pedestrian and bicycle traffic, the phase timing strategy also needs to take into account pedestrian and bicycle traffic. In some phases, it is desirable to provide sufficient time for pedestrians and bicycles to pass. 8) Special event handling, phase timing strategies also require consideration of special event handling, such as accident, construction site, emergency vehicle traffic, etc. This may require immediate adjustment of the phase timing. 9) And the intelligent decision support and the phase timing strategy of the signal lamp can work together with an intelligent algorithm and a prediction model. For example, the congestion prediction model may affect the adjustment of phase timing to minimize congestion. The phase timing strategy of the signal lamp is important for the management and safety of traffic flow. It requires a combination of factors including vehicle flow, pedestrian demand, special events and traffic regulations to ensure efficient operation of traffic at or across intersections.

The signal lamp time sequence adjustment strategies of different intersections are generally personalized adjustment according to the unique characteristics and traffic requirements of each intersection. The following are some possible policy and regulatory considerations: 1) Traffic flow analysis: first, traffic flow analysis is performed to understand the traffic demand at each intersection. This includes vehicle flow, pedestrian flow, bicycle flow, etc. over different time periods. And determining the busy period and the low peak period of the intersection according to the analysis result. 2) Signal period length: an appropriate signal period length is determined for the different ports. A busy intersection may require a shorter period to provide more frequent signal changes, while a low traffic intersection may require a longer period to reduce unnecessary waiting time. 3) Phase configuration: the phase configuration of each intersection can be adjusted according to the complexity and flow requirements of the intersection. The phase assignments for the primary and secondary links may be different. Phase assignments for left, right and straight turns are also considered. 4) Special event consideration: consider the effect of a special event on the timing of the intersection signal. For example, if an accident frequently occurs at an intersection, the signal timing may be adjusted to reduce the risk of the accident. Special timing arrangements may also be required for construction sites and emergency vehicle traffic. 5) Pedestrian and bicycle timing: for intersections where pedestrians and bicycles are required to pass, the signal timing is ensured to fully consider the requirements of the pedestrians and the bicycles. The time for the pedestrian to traverse the road is sufficient and the bike path requires a corresponding green light duration. 6) Coordination and synchronization: in urban traffic networks, signal lamp timing needs to be coordinated and synchronized to ensure traffic flow coordination between different portals. The signal timing of the primary and secondary roads may need to work cooperatively. 7) Dynamic adjustment: modern traffic management systems typically have the ability to dynamically adjust the timing of signal lights. Based on the real-time traffic data and events, the system can adjust the signal timing of the intersection to account for changes in traffic demand. 8) The intelligent algorithm supports: intelligent algorithms and traffic models may be used to support decision making of signal timing. For example, the congestion prediction model may affect the adjustment of the traffic light timing to alleviate congestion. 9) User feedback: user feedback and complaints are considered. The signal timing of the intersection should be appropriately adjusted according to feedback from citizens and drivers. The signal lamp time sequence adjustment strategies of different intersections need to comprehensively consider various factors so as to realize better traffic flow and safety.

And step S30, the traffic control cooperative strategy and the target real-time traffic data are sent to a control center, so that the control center deploys the intelligent traffic system based on the traffic control cooperative strategy.

In order to avoid repetition, the content of the actions executed at the control center after the control center acquires the traffic control cooperative policy and the target real-time traffic data is elaborated at the control center.

In still another aspect, as shown in fig. 3, to solve the above technical problem, an embodiment of the present application further provides: the traffic control cooperative method based on the intelligent traffic system is characterized by being applied to an algorithm center and comprising the following steps of:

and step SS10, receiving effective real-time traffic data sent by the management center.

It should be noted that, the effective real-time traffic data received by the algorithm center may include noise and abnormal values, so in order to further improve the calculation efficiency, the received effective real-time traffic data may be processed to remove abnormal values, fill missing data, and normalize data, so as to ensure data quality.

Step SS20, analyzing the effective real-time traffic data to obtain traffic jam early warning information; the traffic jam early warning information comprises current traffic jam information and future traffic jam information.

It should be noted that, the analyzing the effective real-time traffic data in step SS20 to obtain traffic congestion warning information includes:

step SS21, inputting the effective real-time traffic data into a preset microscopic traffic model to obtain first current traffic jam information; inputting the effective real-time traffic data into a preset macroscopic traffic model to obtain second current traffic jam information; acquiring current traffic congestion information based on the first current traffic congestion information and the second current traffic congestion information; the first current traffic congestion information comprises running speed information of an individual vehicle, lane change information of the individual vehicle and parking information of the individual vehicle; the second current traffic congestion situation includes overall traffic flow information and overall traffic speed information.

The preset microscopic traffic model is Cellular Automaton model (hereinafter referred to as CA model) or SUMO model; wherein each cell in the Cellular Automaton model represents a road, and vehicles in each cell are more than or equal to 0.

The CA model simulates the movement of the vehicle on the road based on the simple rule of the vehicle. Such as:

Acceleration rules: if the cell in front is empty, the vehicle may accelerate one cell.

A speed reduction rule: if there is a vehicle in front of it, the vehicle must slow down to avoid a collision.

Random slowing rule: the random slowing-down behavior of the driver is simulated, and even if no vehicle is in front, the vehicle can possibly slow down.

Lane change rule: the vehicle may choose whether to change lanes to avoid congestion.

Through these rules, the CA model may simulate movement of vehicles on roads and traffic flow, including congestion formation and resolution. Such models are commonly used to study the dynamics of traffic flow, including how traffic congestion begins and how it resolves.

It should be noted that the SUMO (Simulation of Urban MObility) model provides a tool and model for simulating urban traffic; the SUMO model can simulate the running of vehicles on urban roads based on a plurality of factors such as vehicles, road networks, signal lamps and the like, and study traffic flow, congestion, accidents and the like.

The preset macroscopic traffic model is a lightill-Whitham-Richards model or a Cell Transmission Model model.

It should be noted that the lightill-Whitham-Richards model (hereinafter, abbreviated as LWR model) is a macroscopic traffic flow model for describing traffic flow on roads. The model can be used for predicting and managing traffic jam, optimizing signal lamp time sequence and analyzing traffic flow in an intelligent traffic system. When the LWR model is applied to the technical solution described in the embodiments of the present application, the LWR model may be used for congestion prediction, signal timing optimization, traffic flow analysis, and the like, and specifically includes the following steps:

Congestion prediction: the LWR model may be used to predict the occurrence and development of traffic congestion. The system can help a management center to take measures before congestion occurs based on factors such as bottleneck of a road, intersection, traffic density and the like.

Signal lamp time sequence optimization: the LWR model can be used to evaluate the effect of different signal timings. By simulating the dynamic change of traffic flow, the management center can adjust the time sequence of the signal lamp to improve the road capacity and reduce the congestion.

Traffic flow analysis: the LWR model helps analyze traffic flow on the road. The management center may use this model to determine traffic density, speed, and road capacity for different road segments to better understand traffic.

Traffic decision support: based on the analysis of the LWR model, the management center can make more intelligent traffic decisions. For example, the signal timing is adjusted according to the prediction result of the model to reduce traffic congestion.

And (3) simulating an event: the LWR model may be used to model the impact of different events on traffic flow. This helps the management center to plan traffic control strategies for special events, road construction or emergency situations.

Comparison of historical data: the LWR model is compared with the historical traffic data, so that the accuracy and the reliability of the model can be verified. This may help the management center to better understand the applicability of the model.

The LWR model is a powerful tool for understanding and managing traffic flow at a macroscopic level. By integrating the traffic signal timing optimization model with real-time data and other models, a management center can better predict congestion, optimize signal timing and take measures to improve traffic fluidity; this helps to improve the traffic efficiency and safety of the city.

It should be noted that the Cell Transmission Model model (hereinafter referred to as CTM model) is another macroscopic traffic flow model for describing traffic flow on roads. The CTM model is similar to the lightill-Whitham-Richards (LWR) model, but is more flexible in some respects, and is applicable to different traffic scenarios. The following are some cases when CTM models are applied to embodiments of the present application:

traffic flow segmentation: the CTM model divides a road into traffic segments (or cells), each cell representing a portion of the road. This helps to describe traffic conditions for different road segments more accurately.

Congestion prediction: CTM models can be used to predict the occurrence and development of traffic congestion, similar to LWR models. Based on traffic flow density and speed of traffic flow section, it is helpful for management center to take measures before congestion occurs.

Signal lamp time sequence optimization: the CTM model may be used to evaluate the signal timing of different traffic segments. The management center can adjust the signal lamp time sequence according to the result of the model so as to improve the road flow.

Traffic congestion propagation: the CTM model is able to model how traffic congestion propagates from one traffic segment to another. This helps the management center to better understand how the congestion spreads in the road network.

Special event processing: CTM models may be used to simulate the impact of special events on traffic flow, such as accidents, road construction, or road segment closure. This helps the management center to plan event handling policies.

Road section optimization: the CTM model allows the management center to individually optimize each traffic segment. This includes adjusting speed limits, vehicle flow density, and other parameters to improve flowability.

Data comparison and validation: the results of the CTM model may be compared and validated against actual traffic data. This helps to evaluate the accuracy and reliability of the model.

The CTM model has the advantage that it models the fine granularity of the road network, allowing for more detailed traffic flow analysis. It can be integrated with real-time data and other models to support decision-making and prediction by traffic management centers, helping to optimize road traffic mobility and reduce congestion. This makes CTM models very useful in coping with different traffic scenarios and road networks.

It can be seen that the lightill-Whitham-Richards (LWR) model and the Cell Transmission Model (CTM) model are macroscopic traffic flow models for describing traffic flow on roads. They share some similarities, but also some differences. In particular, both LWR and CTM models are macroscopic traffic models that are used to describe the overall traffic flow on a road, rather than modeling individual vehicles in detail. Both models describe traffic conditions based on traffic density (number of vehicles per unit length) and speed. They predict traffic congestion and road capacity through the relationship between these variables. They can be used to predict the occurrence and development of traffic congestion to assist traffic management centers in taking steps to alleviate the congestion. Both models can be used to evaluate and optimize the timing of the signal to increase road traffic and reduce congestion.

However, there are also differences between them, such as: CTM models more often employ segment modeling to divide a road into multiple traffic segments (or cells), each cell representing a portion of the road. This allows CTMs to more accurately describe traffic conditions for different road segments. LWR models typically employ continuous density and velocity equations. CTM models have a stronger congestion propagation simulation capability that can simulate how congestion propagates from one traffic segment to another. This is very useful for analysing the spread of congestion in a road network. The LWR model does not have such propagation modeling. CTM models allow for more precise control and optimization, and individual adjustments, such as speed limits and traffic density, may be made to each traffic segment. LWR models are often difficult to achieve such fine-grained control. CTM models are more flexible in modeling complex road networks, but may require more parameters and computational resources. LWR models are typically simpler and are suitable for rapid analysis in some situations.

Therefore, in practical application, the two models can be selected based on the specific traffic analysis requirements and the complexity of the road network in the practical scene, and the two models can be used in combination to fully utilize the advantages of the two models so as to realize better traffic flow control and prediction.

And step SS22, inputting the effective real-time traffic data into a preset time sequence analysis model to obtain future traffic jam information.

The preset time sequence analysis model is an LSTM deep learning model.

Note that LSTM (Long Short-Term Memory) is a deep learning model, and is commonly used for processing time-series data. In intelligent traffic systems, LSTM models may be used to perform time series analysis, such as modeling of historical traffic data, congestion prediction, signal timing optimization, and the like.

The following are some application cases of the LSTM model in the technical solutions described in the embodiments of the present application:

modeling historical traffic data: the LSTM model is used to model historical traffic data such as vehicle speed, traffic density, and congestion. This helps the system understand the historical trend of traffic flow.

Congestion prediction: based on historical data, LSTM may be used to predict future traffic congestion conditions. The model may learn the impact of different factors on traffic, such as time, weather, special events, etc., and provide a prediction of the probability or strength of congestion.

Signal lamp time sequence optimization: the LSTM model may analyze the relationship between historical traffic data and signal timing. This helps to optimize signal timing to accommodate traffic congestion and different time periods.

Real-time traffic prediction: LSTM is utilized for real-time traffic flow prediction. By continually updating the model in conjunction with real-time data, the system can discover potential congestion or flow fluctuations ahead of time in order to take precautions.

Improvement of data quality: the LSTM model may be used to identify and populate missing data points, mitigating imperfections in historical traffic data. This helps to improve data quality, thereby improving analysis and prediction.

Modeling special events: the model may learn how to handle special events such as accidents or road construction effects on traffic. This allows the system to better cope with these situations.

Model adjustment and iteration: the LSTM model may be periodically tuned and iterated to accommodate changing traffic conditions and city dynamics. The method can continuously learn from new data, and improves prediction accuracy.

Abnormality detection: the model may be used to detect abnormal conditions such as traffic anomalies, congestion, or traffic accidents. This may trigger an alarm or support decision making.

It can be seen that the application of the LSTM model may help intelligent traffic systems better understand and utilize time series data to optimize traffic control strategies, improve traffic fluidity, and provide more accurate congestion predictions.

And step SS23, obtaining traffic jam early warning information based on the current traffic jam information and the future traffic jam information.

It should be noted that, based on the current traffic congestion information and the future traffic congestion information, obtaining traffic congestion early warning information includes: inputting the current traffic congestion information and the future traffic congestion information into a congestion prediction model to obtain a congestion prediction value; judging whether the congestion prediction value triggers a congestion alarm threshold value or not; and if the traffic jam early warning information is triggered, outputting traffic jam early warning information.

It should be noted that the congestion prediction model may be a model based on machine learning or time series analysis, i.e. input data is used to predict future traffic congestion situations. If the model processes the input data, a congestion prediction value is generated; this value represents the likelihood or severity of future congestion; in general, higher predictors represent higher congestion risk. Therefore, the embodiment of the application reasonably sets the congestion alarm threshold based on the historical data and the traffic management policy, and is used for triggering the congestion alarm, namely judging whether the congestion predicted value exceeds or is equal to the congestion alarm threshold; and if the traffic congestion pre-warning information exceeds or is equal to the traffic congestion pre-warning information, outputting the traffic congestion pre-warning information.

The traffic congestion warning information described herein may be an alert message, a notification, an audible alert, or a display on a variable message sign, etc., which should include at least information that may include the location, severity, duration, possible cause, etc. of the congestion. The traffic jam early warning information can be output to an algorithm center and can be sent to a driver, a traffic management center, emergency service or other related parties through different clients. To ensure that the driver, traffic management center, emergency service or other relevant party adopts corresponding measures based on traffic jam early warning information, such as changing the route or slowing down the driver, etc.

Step SS30, outputting a traffic control cooperative strategy based on the traffic jam early warning information; wherein the traffic control cooperative policy includes an optimized route policy and a timing adjustment policy.

The aim of the traffic control cooperative strategy is to alleviate traffic jam, optimize road mobility and provide more effective traffic control. Specifically, after the algorithm center detects traffic congestion early warning information, data are integrated and analyzed, and a traffic control strategy is formulated based on a traffic control model, wherein the traffic control model can be based on rules, an optimization algorithm, machine learning or deep learning models, and the selection of the model can be determined according to specific conditions without special limitation.

As described above, the traffic control cooperative policy includes an optimized route policy and a timing adjustment policy. The time sequence adjustment strategy refers to adjusting the time sequence of the signal lamp, namely adjusting the time sequence of the signal lamp according to the congestion position and the severity degree so as to optimize the traffic flow. The optimized route strategy refers to suggesting the driver to use alternative routes to avoid congested areas. In addition, the traffic control cooperative policy may further include: a speed limiting strategy for reducing speed limit to slow down traffic flow and reduce congestion risk; and a special event management strategy for formulating a special event processing strategy such as road construction, accident handling and the like. Once the policy is generated, the algorithm center will output the traffic control collaborative policy. These policies may be communicated to traffic signal control devices, electronically variable message signs, traffic management centers, and drivers.

Of course, the policy is not constant, and in order to improve the applicability of the solution described in the embodiments of the present application, after the algorithm center outputs the traffic control cooperative policy, the method may further include the following steps:

monitoring the effect of the traffic after the traffic is cooperatively controlled based on the traffic control cooperative strategy by each party to judge whether the traffic mobility is improved, whether the congestion situation is relieved and the like so as to judge whether the traffic control cooperative strategy is successful;

Or requesting each party to feed back the effect of the traffic after the traffic is cooperatively controlled based on the traffic control cooperative strategy, and improving the effect based on the feedback so as to improve the strategy generation efficiency in the subsequent application.

And step SS40, the traffic control cooperative strategy is sent to a management center.

The central control system is used as a core to cooperatively manage different signal lamps and traffic signal control equipment. It needs to have the following functions: communicating with each signal lamp and control equipment to implement time sequence adjustment and control; monitoring the states of the signal lamps and the equipment and carrying out real-time traffic; historical traffic data is saved for analysis and optimization.

In order to improve data reliability and security, in some optimized embodiments of the present application, before the algorithm center sends the traffic control coordination policy to the management center, the algorithm center further includes:

and carrying out coding processing and formatting processing on the traffic control collaborative strategy to ensure that the traffic control collaborative strategy can be correctly identified and executed by a management center. This may include converting policies into specific data formats or protocols to ensure that the traffic control collaborative policies are not tampered with and interrupted during transmission.

Therefore, after receiving the traffic control cooperative policy, the management center needs to analyze the traffic control cooperative policy and then send the traffic control cooperative policy to the signal lamp and the traffic signal control equipment for execution. For example, the traffic signal controller may adjust the timing of the signal lights, and the electronically variable message sign may display corresponding information.

In still another aspect, as shown in fig. 4, to solve the above technical problem, an embodiment of the present application further provides: a traffic control cooperative method based on an intelligent traffic system is applied to a control management center and comprises the following steps:

the SSS10 is used for receiving traffic control collaborative strategies and target real-time traffic data sent by the management center;

the SSS20 analyzes and verifies the traffic control cooperative strategy and the target real-time traffic data to obtain an effective traffic control cooperative strategy and effective real-time traffic data;

and step SSS30, deploying and executing based on the effective traffic control cooperative strategy and the effective real-time traffic data.

The deployment and execution of the effective traffic control cooperative policy and the effective real-time traffic data includes:

In still another aspect, as shown in fig. 5, to solve the above technical problem, an embodiment of the present application further provides: a traffic control collaboration system based on an intelligent transportation system, comprising:

It should be noted that, the data transmission module is configured to realize data transmission and cooperative work between different devices so as to ensure reliability and security of the system, and the following novel communication protocol and network structure may be adopted:

The following protocols may be used as the communication protocol:

5G communication protocol: the 5G technology provides high-speed and low-delay data transmission and is suitable for connecting large-scale Internet of things equipment. The intelligent traffic system has the characteristics of high reliability and safety, and is suitable for intelligent traffic systems.

Vehicle-to-infrastructure (V2I) communication protocol: V2I communication protocols enable vehicles to communicate with traffic infrastructure, such as traffic lights and roadside equipment, to implement intelligent traffic control.

Vehicle-to-vehicle (V2V) communication protocol: the V2V communication protocol allows direct communication between vehicles, shares position, speed and traffic information, and is beneficial to cooperative work and improvement of traffic safety.

Internet of things (IoT) communication protocol: ioT communication protocols such as MQTT, coAP, etc. may be used to connect the various sensing devices, transmitting data to the central control system.

Secure communication protocol: secure communication protocols (e.g., TLS/SSL) are employed to ensure confidentiality and integrity of data to prevent tampering or theft of data.

The network structure may adopt the following structure:

edge computing architecture: edge computation is used to process real-time data, reduce data transmission delay, and support instant decision making. The edge node is located closer to the equipment, so that data is processed locally, and the load of the cloud server is reduced.

Distributed network: and the distributed network structure is adopted to distribute data storage and processing on a plurality of nodes so as to improve the reliability and redundancy of the system.

Blockchain techniques: blockchains can be used to securely record traffic data, ensure non-tamper-resistance of the data, and provide a decentralised trust mechanism.

Virtual Private Network (VPN): a secure virtual private network is established for communication between devices, ensuring encryption and isolation of data transmissions.

Fault tolerant mechanism: a fault tolerant mechanism is introduced to handle equipment faults or communication interruption, and the robustness of the system is ensured.

Security policy and access control: strict access control and security policies are established to prevent unauthorized access and attacks.

These new communication protocols and network structures will help to ensure the reliability and security of data transmission and co-operation between different devices, especially for intelligent traffic systems, where co-operation between devices is critical for traffic flow control and safety.

As some optional embodiments of the present application, the traffic control coordination system based on the intelligent traffic system further includes:

the sensing equipment is used for collecting real-time traffic data;

As described above, the sensing device may be a camera, radar, geomagnetic coil, GPS receiver, etc. sensing device to monitor vehicle position, speed, and flow. The signal controller may be a traffic signal controller to control the signal lights. The user client can be a driver client, a traffic management center client, an emergency service client or other related party clients.

Based on the same inventive concept as the previous embodiments, this embodiment provides a computer readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.

In some embodiments, the computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; but may be a variety of devices including one or any combination of the above memories. The computer may be a variety of computing devices including smart terminals and servers.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.

Claims

1. The traffic control cooperative method based on the intelligent traffic system is characterized by being applied to a management center and comprising the following steps of:

2. The intelligent transportation system-based traffic control cooperative method according to claim 1, wherein the optimized route policy includes an optimized route policy of a vehicle;

3. The traffic control cooperative method based on the intelligent traffic system is characterized by being applied to an algorithm center and comprising the following steps of:

receiving effective real-time traffic data sent by the management center;

and sending the traffic control collaborative strategy to a management center.

4. The traffic control cooperative method based on an intelligent traffic system according to claim 3, wherein the analyzing the effective real-time traffic data to obtain traffic congestion pre-warning information includes:

5. The intelligent traffic system-based traffic control cooperative method according to claim 4, wherein the preset microscopic traffic model is a Cellular Automaton model or a SUMO model; wherein each cell in the Cellular Automaton model represents a road, and vehicles in each cell are more than or equal to 0;

the preset time sequence analysis model is an LSTM deep learning model.

6. The traffic control cooperative method based on the intelligent traffic system according to claim 4, wherein the obtaining traffic congestion early-warning information based on the current traffic congestion information and the future traffic congestion information includes:

7. The traffic control cooperative method based on the intelligent traffic system is characterized by being applied to a control center and comprising the following steps of:

8. The intelligent transportation system-based traffic control coordination method according to claim 7, wherein the deploying and executing based on the effective traffic control coordination policy and the effective real-time traffic data comprises:

9. A traffic control cooperative system based on an intelligent traffic system, comprising:

10. The intelligent transportation system-based traffic control coordination system of claim 9, further comprising:

The sensing equipment is used for collecting real-time traffic data;