A rolling optimization method for intersection signals considering demand flow in a connected vehicle environment

Abstract

The present invention discloses a rolling optimization method for intersection signals that considers demand flow in a vehicle networking environment, including three key steps: a vehicle path shortest path and turning prediction model, an intersection queue length estimation model, and a dynamic signal timing scheme optimization model. The method of the present invention collects the OD information and vehicle operation status information of the upstream vehicles imported in the vehicle networking environment, infers the flow attributes of the vehicles at this intersection, and then accurately estimates the arrival status of the downstream vehicles. Finally, a mixed integer programming model is constructed to dynamically recommend the best signal timing scheme for the intersection signal lights, thereby improving the efficiency of urban road traffic operation and reducing the level of travel delays.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle networking technology and traffic signal optimization, and specifically relates to an intersection signal rolling optimization method that takes demand flow into consideration in a vehicle networking environment. Background Art

[0002] With the acceleration of urbanization in China and the rapid increase in the number of motor vehicles, the demand for urban travel has grown rapidly, while the backward infrastructure and low management level have led to an imbalance in road traffic supply and demand, and the pressure on urban traffic has increased day by day. Therefore, higher requirements have been put forward for the management and control capabilities of urban road traffic. The spatial and temporal resources of urban roads should be reasonably allocated, and the optimal control strategy should be sought under the existing conditions to alleviate urban traffic pressure and improve traffic efficiency and residents' travel experience.

[0003] Traditional urban road signal-controlled intersections often use historical road data as a basis to formulate fixed or time-divided signal timing plans for intersection lights. However, due to the time-varying and random characteristics of urban road traffic, historical data cannot accurately describe the distribution characteristics of current traffic flow. The resulting signal timing plan will cause a large loss of time for travelers and increase travel costs. Subsequently, with the increase in road detection equipment, traffic management departments in various regions gradually adopted geomagnetic coil detectors or video detection to obtain recent road traffic data, so as to analyze the road traffic operation status and adjust the signal timing plan. In recent years, with the development of wireless communication technology, vehicle networking technology has emerged. With the empowerment of this technology, the ID, speed, vehicle location and OD information of vehicles can be obtained in real time within a certain range of the intersection, which brings new opportunities for accurately predicting the road traffic status and arrival conditions.

[0004] In the existing technology, the traffic signal control technology at the road intersection uses various control hardware and software equipment, such as traffic signals, electronic computers, variable signs, etc. to organize, guide and control the operation of the traffic system, so as to achieve the balanced distribution of traffic flow in time and space, even traffic load, and reasonably command and control traffic, improve the effectiveness of road traffic resource supply, and relieve traffic pressure. The Internet of Vehicles technology is based on vehicle sensing network technology, radio frequency identification technology and wireless communication technology. By equipping vehicles with on-board units and deploying roadside units on the road, a communication network is established between infrastructure such as intersection signals and passing vehicles, and the dynamic information of all vehicles on urban roads is collected in a timely and effective manner, and then the road traffic management control strategy is adjusted in a timely manner to ensure the smooth passage of vehicles and improve traffic operation efficiency. However, the current signal control optimization technology in the Internet of Vehicles environment mostly considers the longitudinal driving state of the vehicle to predict the downstream vehicle arrival rate, but fails to consider the lateral distribution characteristics of the vehicle during road driving, resulting in excessive errors in the downstream steering ratio inference, affecting the timing scheme effect. Currently, the signal timing cycles at urban road intersections are mostly fixed in duration, and the phase sequence is fixed during the timing process. This makes the control scheme inflexible and difficult to cope with road traffic with time-varying characteristics, resulting in unnecessary delays. Summary of the invention

[0005] In view of the problems existing in the existing technology, the present invention provides a rolling optimization method for intersection signals taking into account the demand flow in a vehicle networking environment, so as to better form a signal timing plan to respond to the arriving traffic flow.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a rolling optimization method for intersection signals considering demand flow in a vehicle networking environment, comprising the following steps:

[0007] S1. In the vehicle networking environment, based on the information of the upstream vehicle starting and ending points of the road section, the vehicle flow direction is inferred and the downstream vehicle arrival data is predicted, and the number of vehicles queuing at the intersection is calculated; specifically, the following sub-steps are included:

[0008] S101. In the Internet of Vehicles environment, the departure and arrival points of the vehicle are obtained in real time, and the optimal path is calculated using the Dijkstra algorithm based on the shortest distance, as shown in the following formula:

[0009] d i =min{d j +τ ij}j∈Γ(i),

[0010] Where, d i is the shortest distance from node i to the destination; τ ij is the distance from node i to node j; Γ(i) is the set of adjacent nodes of node i;

[0011] S102: Obtain the next node j that the vehicle will pass through through the obtained optimal path next , and further obtain the flow status of the vehicle;

[0012] S103, based on the flow state of the vehicle, extract the vehicle ID of different flow directions, obtain the driving speed of the corresponding vehicle according to the vehicle ID, and calculate the estimated time for the vehicle to arrive at the downstream stop line, as follows:

[0013]

[0014] in, is the estimated time for vehicle k to arrive at the downstream stop line; is the current time of vehicle k; l k is the distance traveled by vehicle k to the stop line;

[0015] S104. Calculate the number of vehicles arriving at the downstream stop line at each time in the future according to the estimated time of arrival of each vehicle at the downstream stop line, as follows:

[0016]

[0017] In the formula, q d It is the cumulative estimated number of vehicles arriving downstream.

[0018] S2, with the goal of minimizing the number of vehicles queuing at the intersection, adding green light duration constraints, phase sequence constraints, and conflict avoidance constraints, constructing a signal optimization model to obtain the optimal green light duration of the current signal cycle;

[0019] S3: Collect the arrival data of a new round of downstream vehicles, calculate the number of vehicles queuing at the intersection, and use the signal optimization model to optimize the green light duration of the corresponding phase of the new round of arriving vehicles.

[0020] S4. Return to step S3 to implement the intersection signal rolling optimization strategy.

[0021] Furthermore, in the aforementioned step S1, the number of vehicles queuing at the intersection is calculated as follows:

[0022] N t =N t-1 +A t -D t

[0023] in, is the number of vehicles queuing in each turning lane at time t; is the estimated number of arriving vehicles; is the expected number of vehicles leaving when t is in the green light period,

[0024] in is the saturation flow rate of each turning lane.

[0025] Furthermore, the signal optimization model in the aforementioned step S2 is as follows:

[0026]

[0027] Among them, b j is the controlled variable, indicating the green light start time of each phase.

[0028] Furthermore, in the aforementioned step S2, the green light duration is constrained as follows:

[0029] h×n j,t +h'≤g j

[0030] Among them, g j is the control variable, which indicates the duration of the green light in each phase; h is the headway time, and h' is the time required from when the front of the vehicle is just at the stop line to when the rear of the vehicle passes through the intersection.

[0031] Furthermore, in the aforementioned step S2, based on the conventional intersection phase conflict matrix, the conflict avoidance constraint is established as follows:

[0032]

[0033] In the formula, Ω j1,j2, and Ω j1,j2 is a binary integer variable, where j1 and j2 represent two conflicting flow directions.

[0034] Furthermore, in the aforementioned step S2, the phase sequence constraint is as follows:

[0035]

[0036] Where b j1 , b j2 is the green light start time, g j2 is the green light duration, and M is a preset positive integer.

[0037] Compared with the prior art, the beneficial technical effects of the above technical scheme adopted in the present invention are as follows: by identifying the direction of vehicle flow, accurately predicting the number of vehicles arriving at the downstream intersection, and adaptively generating a signal timing scheme with variable phase sequence, diverse combinations, and adaptive duration to respond to the arriving traffic flow, the traffic operation pressure on congested sections or intersections is greatly alleviated, the travel time at intersections is reduced, the travel delays of urban residents are reduced, and the quality of urban transportation services is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1It is a schematic diagram of the rolling optimization strategy of the present invention. DETAILED DESCRIPTION

[0039] In order to better understand the technical content of the present invention, specific embodiments are given and described as follows in conjunction with the accompanying drawings.

[0040] Various aspects of the invention are described herein with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the invention are not limited to those described in the accompanying drawings. It should be understood that the invention is implemented by any of the various concepts and embodiments described above, as well as the concepts and embodiments described in detail below, because the concepts and embodiments disclosed in the invention are not limited to any implementation. In addition, some aspects disclosed in the invention may be used alone or in any appropriate combination with other aspects disclosed in the invention.

[0041] refer to Figure 1 The present invention provides an intersection signal rolling optimization method considering demand flow in a vehicle networking environment, comprising the following steps:

[0042] S1. In the vehicle networking environment, based on the information of the upstream vehicle starting and ending points of the road section, the vehicle flow direction is inferred and the downstream vehicle arrival data is predicted to calculate the number of vehicles queuing at the intersection;

[0043] S2, with the goal of minimizing the number of vehicles queuing at the intersection, adding green light duration constraints, phase sequence constraints, and conflict avoidance constraints, constructing a signal optimization model to obtain the optimal green light duration of the current signal cycle;

[0044] S3: Collect the arrival data of a new round of downstream vehicles, calculate the number of vehicles queuing at the intersection, and use the signal optimization model to optimize the green light duration of the corresponding phase of the new round of arriving vehicles.

[0045] S4. Return to step S3 to implement the intersection signal rolling optimization strategy.

[0046] As a preferred embodiment of the present invention, inferring the vehicle flow direction and predicting the downstream vehicle arrival data in step S1 includes the following sub-steps:

[0047] S101. In the Internet of Vehicles environment, the departure and arrival points of the vehicle are obtained in real time, and the optimal path is calculated using the Dijkstra algorithm based on the shortest distance, as shown in the following formula:

[0048] d i =min{d j +τ ij}j∈Γ(i),

[0049] Where, d i is the shortest distance from node i to the destination; τ ijis the distance from node i to node j; Γ(i) is the set of adjacent nodes of node i;

[0050] S102: Obtain the next node j that the vehicle will pass through through the obtained optimal path next , and further obtain the flow status of the vehicle;

[0051] S103, based on the flow state of the vehicle (including left turn, straight, and right turn), extract the vehicle ID of different flow directions, obtain the driving speed of the corresponding vehicle according to the vehicle ID, and calculate the estimated time for the vehicle to arrive at the downstream stop line, as follows:

[0052]

[0053] in, is the estimated time for vehicle k to arrive at the downstream stop line; is the current time of vehicle k; l k is the distance traveled by vehicle k to the stop line;

[0054] S104. Calculate the number of vehicles arriving at the downstream stop line at each time in the future according to the estimated time of arrival of each vehicle at the downstream stop line, as follows:

[0055]

[0056] In the formula, q d It is the cumulative estimated number of vehicles arriving downstream.

[0057] As a preferred embodiment of the present invention, in step S1, the number of vehicles queuing at the intersection is calculated, specifically as follows:

[0058] N t =N t-1 +A t -D t

[0059] in, is the number of vehicles queuing in each turning lane at time t; is the estimated number of arriving vehicles; is the expected number of vehicles leaving. When t is in the green light period, in is the saturation flow rate of each turning lane; in this embodiment, when t is in the red light period, d j,t =0.

[0060] As a preferred embodiment of the present invention, the signal optimization model in step S2 is as follows:

[0061]

[0062] Among them, bj is the controlled variable, indicating the green light start time of each phase.

[0063] In step S2, the green light duration is constrained as follows:

[0064] h×n j,t +h'≤g j

[0065] Among them, g j is the control variable, indicating the duration of the green light in each phase; h is the headway time, which is 2s in this embodiment; h' is the time required from when the front of the vehicle is just at the stop line to when the rear of the vehicle passes through the intersection, which is 5s in the calculation.

[0066] According to the conventional intersection phase conflict matrix, the matrix is ​​as follows Table 1:

[0067] Table 1

[0068] Phase 1 2 3 4 5 6 7 8 1 √ √ △ √ √ △ △ △ 2 √ √ △ △ △ √ √ △ 3 △ △ √ √ △ √ √ △ 4 √ △ √ √ △ △ △ √ 5 √ △ △ △ √ √ △ √ 6 △ √ √ △ √ √ △ △ 7 △ √ √ △ △ △ √ √ 8 △ △ △ √ √ △ √ √

[0069] The above table is a phase conflict matrix table for a conventional intersection. √ indicates that there is no conflict point between the corresponding two phases in space, and △ indicates that there is a conflict between the corresponding two phases in space. According to the conflict matrix table, if the two conflicting traffic flows have the right of way at the same time, the traffic flows from different directions will conflict in time and space, thus causing safety hazards.

[0070] The conflict avoidance constraint is established as follows:

[0071]

[0072] In the formula, Ω j1,j2, and Ω j1,j2 is a binary integer variable, where j1 and j2 represent two conflicting flow directions, and can be 0 or 1, where j1 and j2 represent two conflicting flow directions.

[0073] When a flow of traffic from a certain direction has the right of way at an intersection, its corresponding green light phase should be after all conflicting green light phases have ended, so a phase order constraint is established as follows:

[0074]

[0075] Where b j1 , b j2 is the green light start time, g j2is the duration of the green light, and M is a relatively large positive integer. Although the present invention has been described above with a preferred embodiment, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. A method for rolling optimization of intersection signals considering demand flow in a vehicle networking environment, characterized in that: The steps include: S1. In the vehicle networking environment, based on the information of the upstream vehicle starting and ending points of the road section, the vehicle flow direction is inferred and the downstream vehicle arrival data is predicted to calculate the number of vehicles queuing at the intersection; It includes the following sub-steps: S101. In the Internet of Vehicles environment, the departure and arrival points of the vehicle are obtained in real time, and the optimal path is calculated using the Dijkstra algorithm based on the shortest distance, as shown in the following formula: d i =min{d j +τ ij }j∈Γ(i), Where, d i is the shortest distance from node i to the destination; τ ij is the distance from node i to node j; Γ(i) is the set of adjacent nodes of node i; S102: Obtain the next node j that the vehicle will pass through through the obtained optimal path next , and further obtain the flow status of the vehicle; S103, based on the flow state of the vehicle, extract the vehicle ID of different flow directions, obtain the driving speed of the corresponding vehicle according to the vehicle ID, and calculate the estimated time for the vehicle to arrive at the downstream stop line, as follows: in, is the estimated time for vehicle k to arrive at the downstream stop line; is the current time of vehicle k; l k is the distance traveled by vehicle k to the stop line; S104. Calculate the number of vehicles arriving at the downstream stop line at each time in the future according to the estimated time of arrival of each vehicle at the downstream stop line, as follows: In the formula, q d To estimate the number of vehicles arriving downstream; S2, with the goal of minimizing the number of vehicles queuing at the intersection, adding green light duration constraints, phase sequence constraints, and conflict avoidance constraints, constructing a signal optimization model to obtain the optimal green light duration of the current signal cycle; S3: Collect the arrival data of a new round of downstream vehicles, calculate the number of vehicles queuing at the intersection, and use the signal optimization model to optimize the green light duration of the corresponding phase of the new round of arriving vehicles. S4. Return to step S3 to implement the intersection signal rolling optimization strategy.

2. According to claim 1, a method for rolling optimization of intersection signals taking into account demand flow in a vehicle networking environment is characterized in that: In step S1, the number of vehicles queuing at the intersection is calculated as follows: N t =N t-1 +A t -D t in, is the number of vehicles queuing in each turning lane at time t; is the estimated number of arriving vehicles; is the expected number of vehicles leaving when t is in the green light period, in is the saturation flow rate of each turning lane.

3. The method for rolling optimization of intersection signals considering demand flow in a vehicle networking environment according to claim 2 is characterized in that: The signal optimization model in step S2 is as follows: Among them, b j is the controlled variable, indicating the green light start time of each phase.

4. The method for rolling optimization of intersection signals considering demand flow in a vehicle networking environment according to claim 3 is characterized in that: In step S2, the green light duration is constrained as follows: h×n j,t +h'≤g j Among them, g j is the control variable, which indicates the duration of the green light in each phase; h is the headway time, and h' is the time required from when the front of the vehicle is just at the stop line to when the rear of the vehicle passes through the intersection.

5. The method for rolling optimization of intersection signals considering demand flow in a vehicle networking environment according to claim 4 is characterized in that: In step S2, based on the conventional intersection phase conflict matrix, the conflict avoidance constraint is established as follows: In the formula, Ω j1,j2 , and Ω j1,j2 is a binary integer variable, where j1 and j2 represent two conflicting flow directions.

6. The method for rolling optimization of intersection signals considering demand flow in a vehicle networking environment according to claim 5 is characterized in that: In step S2, the phase order constraint is as follows: Where b j1 , b j2 is the green light start time, g j2 is the green light duration, and M is a preset positive integer.

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