A distributed vehicle traffic management method under vehicle-mounted self-organizing network

Abstract

The present invention belongs to the technical field of vehicle traffic management, and specifically relates to a distributed vehicle traffic management method under a vehicle-mounted self-organizing network; the method comprises: calculating a vehicle score; sending beacon data to neighboring vehicles; the vehicle sends the beacon data to the vehicle with the highest vehicle score among neighboring vehicles with higher scores than the vehicle itself, and the vehicle with the highest score aggregates beacon information again and calculates a road weight until the vehicle score is higher than the vehicle scores of all neighboring vehicles; the vehicle calculates the weights of different roads according to the beacon data; the vehicle with the highest vehicle score identifies the congestion situation of the road according to the road weight and creates road knowledge according to the identification result beacon data; if the road is congested, the vehicle distributes the road knowledge to neighboring vehicles; the vehicle at the congestion point calculates an alternative route according to the road knowledge and travels according to the alternative route; the present invention realizes efficient vehicle traffic diversion, reduces network overhead, reduces vehicle congestion time, and is highly practical.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle traffic management, and in particular relates to a distributed vehicle traffic management method under a vehicle-mounted self-organizing network. Background Art

[0002] Traffic congestion is a daily occurrence in major cities around the world. As the economy and population of urban centers grow, the problem is becoming increasingly severe. The increasing demand for vehicle traffic may overwhelm the existing transportation infrastructure, especially during peak traffic hours. Due to financial and physical space constraints, the expansion of road infrastructure is usually limited. Therefore, it is more practical to use vehicle traffic management technology to improve the efficiency of traffic management systems.

[0003] In recent years, various approaches have been proposed to deal with problems related to vehicle traffic management. Typically, most systems connect a central server or roadside units to the internet to find en-route events and alternative routes for vehicles, but altruistic approaches are rarely applied to routing decisions.

[0004] Vehicular ad hoc networks (VANs) are a promising technology for efficient traffic management systems. Their highly dynamic topology is one of the characteristics that distinguishes them from other types of networks. The mobility patterns of mobile nodes (vehicles) in a VAN are constrained by road paths, traffic regulations, and traffic conditions. All external interactions occur via wireless communication links between vehicles or between vehicles and roadside units.

[0005] Most vehicular ad hoc networks use a beaconing approach to understand local conditions and identify en-route events (e.g., traffic jams). This approach uses a control channel to periodically exchange beacon messages. The content of the beacon messages is associated with GPS information, such as location coordinates, speed, direction, and other vehicle information. This allows each vehicle to understand information from neighboring vehicles within its transmission range and monitor whether a traffic jam or accident has occurred.

[0006] In summary, there is an urgent need for a distributed vehicle traffic management method under a vehicle-mounted self-organizing network, which can efficiently and quickly perform traffic planning when traffic jams or traffic accidents are discovered, and recommend alternative routes in real time so that traffic can return to normal as soon as possible. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention proposes a distributed vehicle traffic management method under a vehicle-mounted self-organizing network, which includes:

[0008] S1: Calculate the betweenness metric of vehicle nodes in the vehicular ad hoc network;

[0009] S2: Calculate the radio propagation model based on the two-line ground reflection propagation model;

[0010] S3: Calculate the vehicle score based on the betweenness metric of the vehicle node and the radio propagation model;

[0011] S4: Send beacon data to neighboring vehicles; wherein the beacon data includes a beacon message and a vehicle score;

[0012] S5: The vehicle sends beacon data to the vehicle with the highest vehicle score among its neighboring vehicles that have a higher score than the vehicle itself. The vehicle with the highest vehicle score among its neighboring vehicles receives the beacon data and aggregates it with its own beacon data to obtain aggregated beacon data until the vehicle score of the vehicle is higher than the vehicle scores of all neighboring vehicles. The vehicle calculates the weights of different roads based on the beacon data.

[0013] S6: The vehicle with the highest vehicle score identifies the congestion situation of the road according to the road weight; and obtains road knowledge based on the identification result and beacon data;

[0014] S7: If the recognition result indicates congestion, the vehicle distributes the road knowledge to neighboring vehicles; otherwise, it continues driving on the current route;

[0015] S8: Vehicles at congestion points calculate alternative routes based on road knowledge; vehicles at congestion points drive along the alternative routes.

[0016] Preferably, the formula for calculating the betweenness metric of a vehicle node is:

[0017]

[0018] Among them, EBM (n) represents the betweenness measure of vehicle node n, A n represents the first-order adjacency matrix of the network centered on vehicle node n, Represents the second-order adjacency matrix of the network centered on vehicle node n.

[0019] Preferably, the formula for calculating the radio propagation model is:

[0020]

[0021] Among them, L TRI [dB] represents the two-line ground reflection propagation loss value, d represents the Euclidean distance between the two vehicles, λ represents the wavelength, Γ represents the reflection coefficient, Represents the phase difference of the interfering rays.

[0022] Preferably, the formula for calculating the vehicle score is:

[0023] V rank =αEBM+(1-α)LTRI [dB]

[0024] Among them, V rank represents the vehicle score, EBM represents the betweenness measure, α represents the weight factor, L TRI [dB] represents the two-line ground reflection propagation loss value.

[0025] Preferably, the process of calculating the weight of the road based on the beacon data includes:

[0026] The average total speed of the road is calculated based on the beacon data. The calculation formula is:

[0027]

[0028] in, represents the average total speed of the i-th road, v1 represents the first input speed of the i-th road, v2 represents the second input speed of the i-th road, c1 represents the first aggregation coefficient, and c2 represents the second aggregation coefficient.

[0029] The weight of the road is calculated based on the average total speed of the road. The calculation formula is:

[0030]

[0031] in, represents the weight of the i-th road, represents the maximum speed of the i-th road.

[0032] Preferably, the process of calculating the alternative route includes: the vehicle determines whether the vehicle is passing through a congestion point and whether there is a possibility of changing routes based on road knowledge. If the vehicle is passing through a congestion point and there is a possibility of changing routes, the vehicle is a congestion point vehicle; the neighboring vehicles of the congestion point vehicle calculate the entropy measure of the road on which it is located and obtain entropy measure information; the neighboring vehicles send the entropy measure information to the congestion point vehicle, and the congestion point vehicle selects the road with the lowest entropy measure as the alternative route based on the entropy measure information sent by the neighboring vehicles.

[0033] Furthermore, the formula for calculating the entropy measure of the road is:

[0034]

[0035] in, represents the entropy measure of the road, represents the weight of road segment i, Q represents the road segment weight set, and n represents the number of road segments.

[0036] The beneficial effects of the present invention are as follows: compared with most vehicle traffic management-related systems that require a central server and Internet connection to search for road events and alternative routes for vehicles, the distributed vehicle traffic management method designed by the present invention under the on-board self-organizing network adopts a self-centered betweenness measurement and a radio propagation model as a vehicle selection mechanism, which reduces the system's network overhead (the greater the traffic, the greater the network overhead); an altruistic entropy-based shortest path method for vehicle traffic management is proposed, in which vehicles share the decision of alternative routes with the maximum number of other vehicles participating in the congestion, providing a better decision-making process and a better overall network flow for all participating vehicles, thereby achieving efficient vehicle traffic diversion; compared with other management methods, the present invention has low cost, reduces vehicle driving time and traffic congestion time, is highly practical, and has good economic benefits in the field of intelligent distributed interconnected traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a distributed vehicle traffic management method in a vehicle-mounted self-organizing network according to the present invention;

[0038] Figure 2 Schematic diagram of the ego network topology with vehicle node n as the central node in the present invention;

[0039] Figure 3 This is a schematic diagram of a model for aggregating beacon data of vehicles in the present invention;

[0040] Figure 4 A schematic diagram of the process of calculating alternative routes for vehicles at congestion points in the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] The present invention proposes a distributed vehicle traffic management method under a vehicle-mounted self-organizing network, such as Figure 1 As shown, the method includes the following contents:

[0043] S1: Calculate the betweenness metric of vehicle nodes in the vehicular ad hoc network.

[0044] In the vehicle self-organizing network, the betweenness metric of the vehicle node is calculated based on the self-network; the self-network with vehicle node n as the central node is as follows Figure 2 As shown, define the link set

[0045]

[0046] Where v′ represents a single-hop node away from node n, d(n,v′) represents a single-hop link between nodes n and v′, V represents a set of nodes, and r represents the number of hops between nodes n and v′.

[0047] The first-order node of node n is represented by an undirected graph Represents, where the node set corresponds to The set of edges corresponds to i,j represent different nodes in the network.

[0048] Undirected graph The node-to-node links in the network are represented by a symmetric adjacency matrix A(k×k), where k is the number of one-hop neighbor nodes. n Each element a in ij for:

[0049]

[0050] Similarly, middle element a ij ′ is expressed as:

[0051]

[0052] Then the betweenness metric EBM of a node n is The formula for calculating the betweenness metric of a vehicle node is:

[0053]

[0054] Among them, EBM (n) represents the betweenness measure of vehicle node n, A n represents the first-order adjacency matrix of the network centered on vehicle node n, represents the second-order adjacency matrix of the network centered on vehicle node n, and i and j represent any two different nodes in the network.

[0055] S2: Calculate the radio propagation model based on the two-line ground reflection propagation model.

[0056] Apply the two-line ground reflection propagation model and consider path loss The influence of interference rays from other vehicles and the reflection coefficient give the radio propagation model:

[0057]

[0058] Among them, L TRI[dB] represents the two-line ground reflection propagation loss value, d represents the Euclidean distance between the two vehicles, λ represents the wavelength, Γ represents the reflection coefficient, Represents the phase difference of the interfering rays.

[0059] Calculate the phase difference of interfering rays The formula is:

[0060]

[0061]

[0062]

[0063] Among them, d los and d ref Expressed as propagation distance and reflection distance respectively, h t and h r Represents the height of the transmitter and receiver antennas relative to the ground respectively.

[0064] The formula for calculating the reflection coefficient Γ is:

[0065]

[0066]

[0067]

[0068] Where ε represents the radio wave reflection from the ground, θ i represents the angle between the ground and the reflected light.

[0069] i=1,2, θ1 represents the angle of incidence, and θ2 represents the angle of refraction.

[0070] S3: Calculate the vehicle score based on the betweenness metric of the vehicle node and the radio propagation model.

[0071] The formula for calculating the vehicle score is:

[0072] V rank =αEBM+(1-α)L TRI [dB]

[0073] Among them, V rank represents the vehicle score, EBM represents the betweenness metric, α∈(0,1) represents the weight factor, which is proportional to the importance of the specific index in the network, and L TRI [dB] represents the two-line ground reflection propagation loss value, which refers to the loss caused by factors such as the transmission medium during the transmission of radio waves.

[0074] S4: Send beacon data to neighboring vehicles; wherein the beacon data includes a beacon message and a vehicle score.

[0075] Beacon messages are the most basic and important message type in the vehicle network, containing information such as the driving status and communication status of the vehicle node.

[0076] All vehicles in the vehicle network send their beacon data to their neighboring vehicles, which share beacon data by sending the beacon data to adjacent single-hop control channels.

[0077] S5: The vehicle sends beacon data to the vehicle with the highest vehicle score among its neighboring vehicles that have a higher score than its own vehicle. The vehicle with the highest vehicle score among its neighboring vehicles receives the beacon data and aggregates it with its own beacon data to obtain aggregated beacon data until the vehicle score of the vehicle is higher than the vehicle scores of all neighboring vehicles; the vehicle calculates the weights of different roads based on the beacon data.

[0078] like Figure 3 As shown in the figure, vehicle A has a vehicle score of 1.0. It forwards its beacon data to vehicle B, which has the highest score among its neighboring vehicles and a vehicle score of 1.93. Vehicle B receives the beacon data from vehicle A and aggregates its own beacon data with the beacon data from vehicle B to obtain aggregated beacon data. It then forwards this aggregated beacon data to its neighboring vehicle, vehicle C, which has the next highest vehicle score. This process repeats until the beacon data reaches vehicle D (with a vehicle score of 5.53). There are no other vehicles with a higher score within D's transmission range. Neighboring vehicles are all vehicles within the vehicle's signal transmission range.

[0079] The process by which a vehicle calculates the weights of different roads based on beacon data includes:

[0080] The average total speed of the road is calculated based on the beacon data. The calculation formula is:

[0081]

[0082] in, represents the average total speed of the i-th road; v1 represents the first input speed of the i-th road, v2 represents the second input speed of the i-th road, the input speed is the ratio of the average driving distance of all vehicles in an area and a specific time period to the average travel time of all vehicles, and the specific time period is selected within the same time length, for example, 7:00 to 8:00 and 8:00 to 9:00 on the same day; c1 represents the first aggregation coefficient, c2 represents the second aggregation coefficient, the aggregation coefficient is the amount of data that helps to generate a new aggregation value, which indicates the amount of data aggregated in the previous aggregation process. The more aggregated data, the larger its value.

[0083] The weight of the road is calculated based on the average total speed of the road. The calculation formula is:

[0084]

[0085] in, represents the weight of the i-th road, The maximum speed of the i-th road is the maximum speed of the vehicle traveling on the i-th road.

[0086] S6: The vehicle with the highest vehicle score identifies the congestion situation of the road according to the weight of the road; and obtains road knowledge based on the identification result and beacon data.

[0087] The vehicle identifies the congestion situation of the road based on the beacon data and the road weight, and judges the service level and flow of the road based on the congestion situation. The judgment basis is shown in Table 1.

[0088] Table 1 Road service level and traffic flow judgment table

[0089]

[0090] When the service level of a road is judged to be AB, it means that the road is not congested. When the service level of a road is judged to be DEF, it means that the road is congested.

[0091] Based on the congestion identification results and beacon data, road knowledge is created, which includes congestion information and aggregated beacon data.

[0092] S7: If the recognition result is congestion, the vehicle distributes the road knowledge to neighboring vehicles; otherwise, it continues driving on the current route.

[0093] If a vehicle determines that the road is congested after identifying the congestion situation, the vehicle will distribute the road knowledge to neighboring vehicles; if the vehicle determines that the road is not congested after identifying the congestion situation, the vehicle will continue its current driving state, that is, continue its current driving route.

[0094] S8: Vehicles at congestion points calculate alternative routes based on road knowledge; vehicles at congestion points drive along the alternative routes.

[0095] like Figure 4 As shown in FIG, the process of calculating an alternative route based on road knowledge for a vehicle at a congestion point includes: the vehicle that receives the road knowledge determines whether the vehicle is passing through a congestion point and whether a route change is possible based on the road knowledge; if the vehicle is passing through a congestion point and a route change is possible, the vehicle is a congestion point vehicle; the neighboring vehicles of the congestion point vehicle calculate the entropy measure of the road on which they are located to obtain entropy measure information; otherwise, the vehicle continues to travel on the current route.

[0096] Before calculating the alternative route, the vehicle needs to wait for a short period of time. The waiting time is proportional to the distance between the congestion point and the current location of the vehicle. During the waiting time, the alternative route information sent by other vehicles is collected.

[0097] The re-planning path adopts the shortest path based on entropy, and defines R = {r1,…,r n} is the set of road segments calculated by the vehicle, and defines is the popularity set of each road segment; the formula for calculating the entropy measure of the road is:

[0098]

[0099]

[0100]

[0101]

[0102] in, represents the entropy measure of the road, represents the weight of road segment i, Q represents the road segment weight set, and n represents the number of road segments on a route; len (i) Indicates the length of the road, which is the distance the vehicle travels from its current location to the destination. (i) Indicates the number of lanes available for the vehicle.

[0103] Neighboring vehicles send entropy measurement information to vehicles at congestion points. Vehicles at congestion points select the road with the lowest entropy measure as an alternative route based on the entropy measurement information sent by neighboring vehicles.

[0104] Congestion point vehicles travel along alternative routes. A congestion point vehicle is the vehicle closest to the congestion point. When the current congestion point vehicle no longer becomes a congestion point vehicle (due to a route change or no longer being closest to the congestion point), the new congestion point vehicle executes step S9 to obtain an alternative route. The new congestion point vehicle travels along the obtained alternative route, and the above process is repeated until traffic is smooth.

[0105] Compared with most vehicle traffic management-related systems that require a central server and Internet connection to search for road events and alternative routes for vehicles, the distributed vehicle traffic management method designed by the present invention under the on-board self-organizing network adopts a self-centered betweenness measurement and a radio propagation model as a vehicle selection mechanism, reducing the system's network overhead (the greater the traffic, the greater the network overhead); an altruistic entropy-based shortest path method for vehicle traffic management is proposed, in which vehicles share the decision of alternative routes with the maximum number of other vehicles participating in the congestion, providing a better decision-making process and a better overall network flow for all participating vehicles, thereby achieving efficient vehicle traffic diversion; compared with other management methods, the present invention has low cost, reduces vehicle travel time and traffic congestion time, and is highly practical. It has good economic benefits in the field of intelligent distributed interconnected traffic management.

[0106] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation plans of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed vehicle traffic management method in a vehicle-mounted self-organizing network, characterized in that: include: S1: Calculate the betweenness metric of vehicle nodes in the vehicle ad hoc network; The formula for calculating the betweenness metric of a vehicle node is: Among them, EBM (n) represents the betweenness measure of vehicle node n, A n represents the first-order adjacency matrix of the network centered on vehicle node n, represents the second-order adjacency matrix of the network centered on vehicle node n; i and j represent any two different nodes in the network; S2: Calculate the radio propagation model based on the two-line ground reflection propagation model; S3: Calculate the vehicle score based on the betweenness metric of the vehicle node and the radio propagation model; the formula for calculating the vehicle score is: V rank =αEBM+(1-α)L TRI [dB] Among them, V rank represents the vehicle score, EBM represents the betweenness measure, α represents the weight factor, L TRI [dB] represents the two-line ground reflection propagation loss value; S4: sending beacon data to neighboring vehicles; wherein the beacon data includes a beacon message and a vehicle score; S5: The vehicle sends beacon data to the vehicle with the highest vehicle score among neighboring vehicles with higher scores than the vehicle itself. The vehicle with the highest vehicle score among the neighboring vehicles receives the beacon data and aggregates it with its own beacon data to obtain aggregated beacon data until the vehicle score of the vehicle is higher than the vehicle scores of all neighboring vehicles. The vehicle calculates weights of different roads based on the beacon data. The process of calculating the weights of roads based on the beacon data includes: The average total speed of the road is calculated based on the beacon data. The calculation formula is: in, represents the average total speed of the ith road, v1 represents the first input speed of the ith road, v2 represents the second input speed of the ith road, c1 represents the first aggregation coefficient, and c2 represents the second aggregation coefficient; The weight of the road is calculated based on the average total speed of the road, and the calculation formula is: in, represents the weight of the i-th road, represents the maximum speed of the i-th road; S6: The vehicle with the highest vehicle score identifies the congestion situation of the road according to the weight of the road; and obtains road knowledge based on the identification result and beacon data; S7: If the recognition result is congestion, the vehicle distributes the road knowledge to neighboring vehicles, otherwise, it continues to drive on the current route; S8: The vehicle at the congestion point calculates an alternative route based on road knowledge; the vehicle at the congestion point drives according to the alternative route; the process of calculating the alternative route includes: the vehicle determines whether the vehicle is passing through the congestion point and whether there is a possibility of changing routes based on road knowledge, and if the vehicle is passing through the congestion point and there is a possibility of changing routes, then the vehicle is a congestion point vehicle; the neighboring vehicle of the congestion point vehicle calculates the entropy measure of the road where it is located to obtain entropy measure information; the neighboring vehicle sends the entropy measure information to the congestion point vehicle, and the congestion point vehicle selects the road with the lowest entropy measure as the alternative route based on the entropy measure information sent by the neighboring vehicle; The formula for calculating the entropy measure of a road is: in, represents the entropy measure of the road, represents the weight of road segment i, Q represents the road segment weight set, and n represents the number of road segments.

2. A distributed vehicle traffic management method in a vehicle-mounted self-organizing network according to claim 1, characterized in that: The formula for calculating the radio propagation model is: Among them, L TRI [dB] represents the two-line ground reflection propagation loss value, d represents the Euclidean distance between the two vehicles, λ represents the wavelength, Γ represents the reflection coefficient, Represents the phase difference of the interfering rays.

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