Vehicle driving path control system and method based on intelligent traffic big data

Abstract

The invention relates to the technical field of intelligent traffic, in particular to a vehicle driving path control system and method based on intelligent traffic big data, and the method comprises the following steps: a, collecting the traffic data of the real-time position, the road traffic flow and the congestion condition of a vehicle through a real-time traffic data collection module; b, analyzing the historical traffic data through a big data analysis and prediction module, and predicting peak periods, accident risk areas and traffic bottleneck points; according to the invention, through dynamic acquisition and big data analysis of real-time traffic data, the peak period and the congestion area can be accurately predicted, so that the driving path of the vehicle is optimized, congestion is reduced, in addition, through intelligent regulation and control of a signal lamp and a freight vehicle shunting mechanism, the system avoids interference of freight vehicles to public transportation in the peak period, and the traffic safety is improved. The road traffic efficiency is improved, the system also provides real-time path adjustment suggestion and early warning service, and more convenient and safer travel experience is provided for drivers.

Description

Technical Field

[0001] The present invention relates to the field of smart transportation technology, and specifically to a vehicle driving path control system and method based on smart transportation big data. Background Art

[0002] With the acceleration of urbanization, the pressure on urban road traffic is increasing, especially during peak traffic hours and in the city center. Traffic congestion is very serious. The traditional traffic management system is slow to respond to complex traffic flow changes and emergencies, making it difficult to achieve accurate control of vehicle driving paths, resulting in the inability to effectively alleviate congestion. Therefore, smart transportation big data technology has gradually become the key to solving traffic congestion. Through real-time data collection and analysis, traffic conditions can be accurately predicted and efficiently managed.

[0003] The current traffic management system has two major problems when dealing with urban traffic congestion: first, there is a lack of dynamic analysis of real-time traffic data, making it difficult to optimize vehicle driving routes in a timely manner; second, freight vehicles have a greater impact on public transportation and private cars during peak traffic hours, especially in city centers or main roads, where freight vehicles often cause more serious congestion. The existing system cannot reasonably dispatch and divert freight vehicles, nor can it effectively alleviate traffic pressure during peak hours. A more intelligent path control system is urgently needed. Therefore, a vehicle driving path control system and method based on smart traffic big data are proposed to address the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a vehicle driving path control system and method based on smart traffic big data, so as to solve the two major problems existing in the current traffic management system when dealing with urban traffic congestion: first, there is a lack of dynamic analysis of real-time traffic data, which makes it difficult to optimize the vehicle's driving path in a timely manner; second, freight vehicles have a greater impact on public transportation and private cars during peak traffic periods, especially in city centers or main roads, where freight vehicles often cause more serious congestion. The existing system cannot reasonably dispatch and divert freight vehicles, nor can it effectively alleviate the traffic pressure during peak hours.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The vehicle driving path control system and method based on smart traffic big data include the following steps: Step a: collecting the real-time position of the vehicle, road traffic flow and traffic congestion data through a real-time traffic data collection module;

[0007] Step b: Analyze historical traffic data through the big data analysis and prediction module to predict peak hours, accident risk areas and traffic bottlenecks;

[0008] Step c: Dynamically optimize the vehicle's driving path through the path optimization module, avoid congested areas, and plan alternative routes;

[0009] Step d: adjusting the duration of traffic lights through the signal light control module to optimize vehicle traffic efficiency;

[0010] Step e: Use the freight vehicle restriction and diversion module to restrict and divert freight vehicles during peak hours to reduce their impact on public transportation and private cars;

[0011] Step f: Pushing real-time route adjustment information to the vehicle terminal through the Internet of Vehicles collaboration module;

[0012] Step g: Issue warning information and route adjustment suggestions to the driver through the early warning and information release module.

[0013] As a further optimization of the present invention, the prediction in step b includes scheduling planning based on the impact of weather conditions and special events on traffic flow.

[0014] As a further optimization of the present invention, in step c, the path optimization module performs multiple dynamic path calculations based on real-time traffic data, and recommends personalized optimal paths to different types of vehicles.

[0015] As a further optimized content of the present invention, wherein: the path dynamic calculation comprises the following steps:

[0016] Step 1: Represent the urban traffic network as a directed graph G = (V, E), where V is a set of nodes, representing road intersections or geographical locations, and E is a set of edges, representing roads. The initial weight of the edge is set based on the road section distance or historical traffic flow;

[0017] Step 2: Calculate each edge e using the following formula ij The cost of passage w ij , weights include distance, real-time traffic flow and congestion coefficient:

[0018] w ij =d ij +αT ij +βC ij

[0019] Where, d ij is the road distance, T ij is the real-time traffic flow, C ij is the congestion coefficient, α and β are adjustment coefficients;

[0020] Step 3: Determine the optimal path P between the starting point s and the end point t through the optimal path search algorithm Di jkstra algorithm, the goal is to minimize the total weight of the path:

[0021]

[0022] Where P is the set of paths from the starting point s to the end point t;

[0023] Step 4: Dynamically adjust the path based on real-time traffic data and update the weight of each edge in real time:

[0024]

[0025] In the formula, is the weight of the next time step, ΔT ij is the traffic flow change, ΔC ij is the change of congestion coefficient, γ and δ are adjustment coefficients;

[0026] Step 5: Through multiple dynamic path calculations, the optimal path is recalculated at each time step based on the updated weights until the preset stopping condition is met, and the optimal path with the minimum cost is output.

[0027] As further optimized content of the present invention, it includes: real-time traffic data collection module, big data analysis and prediction module, path optimization module, signal light control module, freight vehicle restriction and diversion module, vehicle networking collaboration module and early warning and information release module;

[0028] The real-time traffic data collection module is used to collect traffic data on the real-time position, speed, driving direction, road traffic flow and congestion of vehicles through vehicle-mounted GPS, road cameras and traffic sensors;

[0029] The big data analysis and prediction module is used to analyze historical traffic data, combine machine learning algorithms, predict traffic peak hours, accident-prone areas, and potential traffic bottlenecks, and dispatch according to the driving patterns of different types of vehicles;

[0030] The path optimization module is used to dynamically optimize the driving paths of freight vehicles and private cars based on the big data analysis results to avoid entering congested sections, including avoiding peak hours, optimizing traffic routes and diversion strategies;

[0031] The traffic light control module is used to dynamically adjust the duration of traffic lights according to real-time traffic flow, give priority to important vehicles, and reduce the intersection of freight vehicles with public transportation or private cars;

[0032] The freight vehicle restriction and diversion module is used to restrict and divert freight vehicles during peak hours to prevent them from entering the city center or main roads, thereby reducing road traffic load;

[0033] The Internet of Vehicles collaboration module is used to connect with the on-board terminal devices of freight vehicles and private cars, push dynamic path adjustment information in real time, and optimize the vehicle driving route;

[0034] The early warning and information release module is used to release warning information to the driver and recommend an alternative route when possible traffic congestion is detected.

[0035] As a further optimization of the present invention, the big data analysis and prediction module combines historical traffic data to perform traffic flow prediction to adjust the system's scheduling plan.

[0036] As further optimized content of the present invention, the path optimization module plans the travel path in an individualized manner based on the real-time road conditions, vehicle type and driver needs, and generates the optimal travel route in different time periods through dynamic calculation.

[0037] As a further optimization of the present invention, the traffic light control module automatically adjusts the duration distribution of the traffic light according to the type of vehicle, congestion conditions and traffic priority, so that emergency vehicles or important freight vehicles can pass through specific sections with priority.

[0038] As a further optimized content of the present invention, the Internet of Vehicles collaboration module communicates with the vehicle through a cloud system to update and push traffic information, route adjustment suggestions and potential risk warnings in real time.

[0039] As a further optimization of the present invention, the early warning and information release module can release route optimization suggestions before potential peak periods or emergencies based on traffic flow prediction results to reduce the stagnation time of vehicles on key sections.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. In the present invention, through the dynamic collection and big data analysis of real-time traffic data, peak hours and congested areas can be accurately predicted, so as to optimize the driving path of vehicles and reduce congestion. In addition, the system also avoids the interference of freight vehicles on public transportation during peak hours through intelligent control of signal lights and diversion mechanism of freight vehicles, thus improving the traffic efficiency of roads. The system also provides real-time route adjustment suggestions and early warning services, providing drivers with a more convenient and safe travel experience;

[0042] 2. In the present invention, the system automatically adjusts the duration of the signal light during peak hours through the signal light control module, giving priority to important vehicles and private cars, thereby optimizing the traffic flow. In particular, for emergency vehicles or important freight vehicles, the present invention can provide priority right of way and reduce their waiting time during peak traffic hours. This intelligent signal light control not only improves the traffic efficiency of specific vehicles, but also alleviates the overall traffic pressure to a certain extent, ensuring the smooth operation of urban traffic;

[0043] 3. In the present invention, the freight vehicle restriction and diversion module reasonably dispatches freight vehicles during peak hours and diverts them to the outer ring of the city or secondary roads, reducing their traffic impact on the core area of ​​the city. This mechanism significantly reduces the interference of freight vehicles on public transportation and private cars, especially on urban main roads and busy sections, avoiding the formation of traffic bottlenecks. At the same time, through the vehicle network collaboration module, the system can push route adjustment suggestions to the driver in real time, improving the convenience and safety of driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a vehicle driving path control system and method based on smart traffic big data of the present invention;

[0045] Figure 2 This is a flow chart of the dynamic calculation of paths of the present invention. DETAILED DESCRIPTION

[0046] See also Figure 1 , the present invention provides a technical solution:

[0047] The vehicle driving path control system and method based on smart traffic big data include the following steps: Step a: collecting the real-time position of the vehicle, road traffic flow and traffic congestion data through a real-time traffic data collection module;

[0048] Step b: Analyze historical traffic data through the big data analysis and prediction module to predict peak hours, accident risk areas and traffic bottlenecks;

[0049] Step c: Dynamically optimize the vehicle's driving path through the path optimization module, avoid congested areas, and plan alternative routes;

[0050] Step d: adjusting the duration of traffic lights through the signal light control module to optimize vehicle traffic efficiency;

[0051] Step e: Use the freight vehicle restriction and diversion module to restrict and divert freight vehicles during peak hours to reduce their impact on public transportation and private cars;

[0052] Step f: Pushing real-time route adjustment information to the vehicle terminal through the Internet of Vehicles collaboration module;

[0053] Step g: The early warning and information release module releases warning information and route adjustment suggestions to the driver. The system realizes intelligent control of the vehicle's driving route through comprehensive real-time data collection, big data analysis, route optimization, traffic light control, vehicle restriction and diversion, vehicle network collaboration and warning information release, effectively improving the traffic efficiency of urban traffic, reducing congestion, improving driving safety, and providing drivers with a more convenient and comfortable travel experience;

[0054] As a technical solution for further implementation of this solution, the prediction in step b includes scheduling planning based on the impact of weather conditions and special events on traffic flow. The system can more accurately predict traffic conditions, thereby providing vehicles with more reasonable driving route planning, ensuring that the traffic system can still maintain efficient operation under special circumstances such as severe weather or large-scale events;

[0055] As a technical solution for further implementing this solution, in step c, the path optimization module performs multiple dynamic path calculations based on real-time traffic data, and recommends personalized optimal paths to different types of vehicles. Through multiple dynamic calculations and personalized recommendations, the system can provide customized optimal paths for vehicles of different types and needs, which not only improves the flexibility and adaptability of the traffic system, but also enhances the user experience, so that each driver can obtain the best driving plan according to their own needs;

[0056] As a technical solution for further implementing this solution, the dynamic path calculation includes the following steps:

[0057] Step 1: Represent the urban traffic network as a directed graph G = (V, E), where V is a set of nodes, representing road intersections or geographical locations, and E is a set of edges, representing roads. The initial weight of the edge is set based on the road section distance or historical traffic flow;

[0058] Step 2: Calculate each edge e using the following formula ij The cost of passage w ij , weights include distance, real-time traffic flow and congestion coefficient:

[0059] w ij =d ij +αT ij +βC ij

[0060] Where, d ij is the road distance, T ij is the real-time traffic flow, C ij is the congestion coefficient, α and β are the adjustment coefficients;

[0061] Step 3: Determine the optimal path P between the starting point s and the end point t through the optimal path search algorithm Di jkstra algorithm, the goal is to minimize the total weight of the path:

[0062]

[0063] Where P is the set of paths from the starting point s to the end point t;

[0064] Step 4: Dynamically adjust the path based on real-time traffic data and update the weight of each edge in real time:

[0065]

[0066] In the formula, is the weight of the next time step, ΔT ij is the traffic flow change, ΔC ij is the change of congestion coefficient, γ and δ are adjustment coefficients;

[0067] Step 5: Through multiple dynamic path calculations, the optimal path is recalculated at each time step according to the updated weights until the preset stop condition is met, and the optimal path with the lowest cost is output. The dynamic path calculation method can respond to changes in traffic conditions in real time to ensure that the vehicle always travels along the optimal or suboptimal path. This method improves the flexibility and accuracy of path planning, reduces delays caused by traffic congestion, and improves the efficiency of the entire transportation system;

[0068] As a technical solution for further implementation of this plan, it includes real-time traffic data collection module, big data analysis and prediction module, route optimization module, traffic light control module, freight vehicle restriction and diversion module, vehicle networking collaboration module and early warning and information release module;

[0069] The real-time traffic data collection module is used to collect traffic data such as vehicle real-time location, speed, driving direction, road traffic flow and congestion conditions through vehicle-mounted GPS, road cameras and traffic sensors;

[0070] The big data analysis and prediction module is used to analyze historical traffic data and, combined with machine learning algorithms, predict peak traffic hours, accident-prone areas, and potential traffic bottlenecks, and dispatch vehicles based on their driving patterns.

[0071] The route optimization module is used to dynamically optimize the driving routes of freight vehicles and private cars based on big data analysis results to avoid entering congested sections, including avoiding peak hours, optimizing routes and diversion strategies;

[0072] The traffic light control module is used to dynamically adjust the duration of traffic lights according to real-time traffic flow, give priority to important vehicles, and reduce the intersection of freight vehicles with public transportation or private cars;

[0073] The freight vehicle restriction and diversion module is used to restrict and divert freight vehicles during peak hours to prevent them from entering the city center or main roads, thereby reducing road traffic load;

[0074] The Internet of Vehicles collaboration module is used to connect with the on-board terminal devices of freight vehicles and private cars, push dynamic route adjustment information in real time, and optimize vehicle driving routes;

[0075] The early warning and information release module is used to issue warning information to drivers and recommend alternative routes when possible traffic congestion is detected. The system integrates multiple functional modules to form a comprehensive intelligent traffic management platform. This platform can manage and optimize urban traffic from multiple angles and levels, thereby improving the level of intelligent traffic management, reducing traffic congestion, improving road safety, and providing technical support for the sustainable development of urban traffic;

[0076] As a technical solution for further implementation of this plan, the big data analysis and prediction module combines historical traffic data to predict traffic flow in order to adjust the system's scheduling plan. The system can more accurately predict changes in traffic flow, thereby adjusting the traffic scheduling plan in advance, effectively responding to traffic management needs in various complex situations, and improving the adaptability and stability of the transportation system;

[0077] As a technical solution for further implementation of this plan, the route optimization module plans travel routes based on real-time road conditions, vehicle types and driver needs, and generates the optimal travel routes in different time periods through dynamic calculation. The system provides customized travel route planning services by considering real-time road conditions, vehicle characteristics and driver's personalized needs. This method not only improves travel efficiency and reduces travel time, but also improves the driver's travel experience, making the transportation system more humane and efficient.

[0078] As a technical solution for further implementation of this solution, the traffic light control module automatically adjusts the duration of traffic lights according to vehicle types, congestion conditions and traffic priority, so that emergency vehicles or important freight vehicles can have priority to pass through specific sections. By intelligently adjusting the duration of traffic lights, the system can provide priority to emergency vehicles and important freight vehicles, ensuring that these vehicles can pass through busy traffic areas quickly and safely. This priority management not only improves emergency response efficiency, but also helps optimize overall traffic flow and reduce traffic congestion.

[0079] As a technical solution for further implementation of this plan, the Internet of Vehicles Collaboration Module communicates with vehicles through the cloud system to update and push traffic information, route adjustment suggestions and potential risk warnings in real time. The Internet of Vehicles Collaboration Module improves the adaptability and response speed of vehicles to traffic conditions through real-time data exchange, thereby optimizing driving routes, reducing driving time, and improving driving safety. At the same time, it also provides drivers with more timely and accurate traffic information services;

[0080] As a technical solution for further implementation of this plan, the early warning and information release module can issue route optimization suggestions before potential peak periods or emergencies based on traffic flow forecast results to reduce the vehicle's stagnation time on key roads. The system can predict and prompt upcoming traffic jams or special events, allowing drivers to make route adjustments in advance, effectively avoiding traffic jams, improving road use efficiency, and reducing time and economic losses caused by waiting and delays.

[0081] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. The above is only a preferred implementation of the present invention. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the protection scope of the present invention.

Claims

1. A vehicle driving path control method based on smart traffic big data, characterized by: The following steps are involved: Step a: Collecting the real-time location of the vehicle, traffic flow and congestion data through the real-time traffic data collection module; Step b: Analyze historical traffic data through the big data analysis and prediction module to predict peak hours, accident risk areas and traffic bottlenecks; Step c: Dynamically optimize the vehicle's driving path through the path optimization module, avoid congested areas, and plan alternative routes; Step d: adjusting the duration of traffic lights through the signal light control module to optimize vehicle traffic efficiency; Step e: Use the freight vehicle restriction and diversion module to restrict and divert freight vehicles during peak hours to reduce their impact on public transportation and private cars; Step f: Pushing real-time route adjustment information to the vehicle terminal through the Internet of Vehicles collaboration module; Step g: Issue warning information and route adjustment suggestions to the driver through the early warning and information release module.

2. The vehicle driving path control method based on smart traffic big data according to claim 1 is characterized by: The prediction in step b includes scheduling planning based on the impact of weather conditions and special events on traffic flow.

3. The vehicle driving path control method based on intelligent traffic big data according to claim 1 is characterized in that: In step c, the path optimization module performs multiple dynamic path calculations based on real-time traffic data, and recommends personalized optimal paths to different types of vehicles.

4. The vehicle driving path control method based on intelligent traffic big data according to claim 1 is characterized in that: The path dynamic calculation comprises the following steps: Step 1: Represent the urban traffic network as a directed graph G = (V, E), where V is a set of nodes, representing road intersections or geographical locations, and E is a set of edges, representing roads. The initial weight of the edge is set based on the road section distance or historical traffic flow; Step 2: Calculate each edge e using the following formula ij The cost of passage w ij , weights include distance, real-time traffic flow and congestion coefficient: w ij =d i j+αT ij +βC ij Where, d ij is the road distance, T ij is the real-time traffic flow, C ij is the congestion coefficient, α and β are adjustment coefficients; Step 3: Determine the optimal path P between the starting point s and the end point t through the optimal path search algorithm Dijkstra algorithm, the goal is to minimize the total weight of the path: Where P is the set of paths from the starting point s to the end point t; Step 4: Dynamically adjust the path based on real-time traffic data and update the weight of each edge in real time: In the formula, is the weight of the next time step, ΔT ij is the traffic flow change, ΔC ij is the change of congestion coefficient, γ and δ are adjustment coefficients; Step 5: Through multiple dynamic path calculations, the optimal path is recalculated according to the updated weights at each time step until the preset stopping condition is met, and the optimal path with the minimum cost is output.

5. The vehicle driving path control system based on smart traffic big data according to any one of claims 1 to 4, characterized in that: It includes real-time traffic data collection module, big data analysis and prediction module, route optimization module, traffic light control module, freight vehicle restriction and diversion module, Internet of Vehicles collaboration module and early warning and information release module; The real-time traffic data collection module is used to collect traffic data on the real-time position, speed, driving direction, road traffic flow and congestion of vehicles through vehicle-mounted GPS, road cameras and traffic sensors; The big data analysis and prediction module is used to analyze historical traffic data, combine machine learning algorithms, predict traffic peak hours, accident-prone areas, and potential traffic bottlenecks, and dispatch according to the driving patterns of different types of vehicles; The path optimization module is used to dynamically optimize the driving paths of freight vehicles and private cars based on the big data analysis results to avoid entering congested sections, including avoiding peak hours, optimizing traffic routes and diversion strategies; The traffic light control module is used to dynamically adjust the duration of traffic lights according to real-time traffic flow, give priority to important vehicles, and reduce the intersection of freight vehicles with public transportation or private cars; The freight vehicle restriction and diversion module is used to restrict and divert freight vehicles during peak hours to prevent them from entering the city center or main roads, thereby reducing road traffic load; The Internet of Vehicles collaboration module is used to connect with the on-board terminal devices of freight vehicles and private cars, push dynamic path adjustment information in real time, and optimize the vehicle driving route; The early warning and information release module is used to release warning information to the driver and recommend an alternative route when possible traffic congestion is detected.

6. The vehicle driving path control system based on intelligent traffic big data according to claim 5 is characterized in that: The big data analysis and prediction module combines historical traffic data to perform traffic flow prediction to adjust the system's scheduling plan.

7. The vehicle driving path control system based on intelligent traffic big data according to claim 5 is characterized by: The path optimization module plans the travel path in a personalized manner based on real-time traffic conditions, vehicle types and driver needs, and generates the optimal travel route for different time periods through dynamic calculation.

8. The vehicle driving path control system based on intelligent traffic big data according to claim 5 is characterized by: The traffic light control module automatically adjusts the duration of the traffic light according to the type of vehicle, congestion conditions and traffic priority, so that emergency vehicles or important freight vehicles can pass through specific sections first.

9. The vehicle driving path control system based on intelligent traffic big data according to claim 5 is characterized in that: The Internet of Vehicles collaborative module communicates with the vehicle through the cloud system to update and push traffic information, route adjustment suggestions and potential risk warnings in real time.

10. The vehicle driving path control system based on intelligent traffic big data according to claim 5 is characterized in that: The early warning and information release module can release route optimization suggestions before potential peak periods or emergencies based on traffic flow prediction results to reduce the vehicle's stagnation time on key sections.

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