Urban intersection rail express system control method and system under heterogeneous traffic flow

Abstract

The invention relates to the technical field of intelligent traffic control, and particularly discloses an urban intersection rail express system control method and system under heterogeneous traffic flow, and the method comprises the steps: constructing an urban intersection conflict region scene model, and guaranteeing the priority passage of an intelligent rail express system based on a green light extension strategy; modeling a cooperative control problem of the intelligent rail express system and the intelligent network vehicle into a cooperative game model; distributing weights for the earnings based on an analytic hierarchy process, and generating a comprehensive earning function based on the weights; a control strategy is generated based on a comprehensive revenue function, so that the influence of ART on CAV can be considered on the premise of ensuring priority passage of ART, the potential traffic safety hazard of an intersection conflict area can be eliminated, and the safety revenue of each game participant can be ensured; under the condition that the overall passing efficiency of the intersection is not sacrificed, the passing efficiency of the game core participant ART is improved, and the time that the ART stays in the intersection is shortened.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent traffic control, in particular to a control method and system for an urban intersection rail express system under heterogeneous traffic flow. BACKGROUND

[0002] With the rapid development of intelligent transportation systems, heterogeneous traffic flow with mixed operation of intelligent rail express systems (ART) and intelligent network vehicles (CAV) has become a typical scenario at urban intersections. Overcoming the cooperative control technology of heterogeneous traffic flow is of great significance to traffic control. Existing traffic control methods have significant defects when facing the mixed scene of intelligent rail express systems (ART) and intelligent network vehicles (CAV). The non-cooperative game model does not consider the priority of ART, and it is difficult to balance the intelligent rail express system (ART) and the intelligent network vehicle (CAV); even fewer studies consider the influence between heterogeneous vehicles.

[0003] In contrast, the heterogeneous traffic flow optimization method based on game theory ensures the priority of the intelligent rail express system (ART) under the premise of considering the influence of the intelligent rail express system (ART) on the intelligent network vehicle (CAV), and pursues the maximum benefit of the whole system; when calculating the benefit, the influence of the physical parameters of the intelligent rail express system (ART) is considered, especially the influence of the length of the intelligent rail express system (ART) on the intersection traffic conditions.

[0004] Therefore, the heterogeneous traffic flow optimization method based on game theory is a potential new technology that can effectively make up for the shortcomings of traditional research methods, further resolve the contradictions between traffic participants in the intersection conflict area, and achieve safer, more efficient and more environmentally friendly traffic control, which is of great significance. SUMMARY

[0005] The purpose of the present application is to provide a control method and system for an urban intersection rail express system under heterogeneous traffic flow to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical solutions: The control method for an urban intersection rail express system under heterogeneous traffic flow comprises the following steps: Constructing a scenario model of the intersection conflict area, and ensuring the priority of the intelligent rail express system based on the green light extension strategy; Modeling the cooperative control problem of the intelligent rail express system and the intelligent network vehicle as a cooperative game model; Assigning weights to each benefit based on the analytic hierarchy process, and generating a comprehensive benefit function based on the weights; Generating a control strategy based on the comprehensive benefit function.

[0007] As a further scheme of the present application, on the basis of ensuring the priority of the intelligent rail express system, the average delay calculation formula of the intelligent network vehicle right turning in the conflict area is: ; Among them, , , T1 is the average delay of the intelligent network vehicle right turning in the conflict area; λ is the intelligent rail vehicle arrival rate; T pass is the intelligent rail vehicle passing time; T headway is the headway; L is the total length of the intelligent rail vehicle; V is the intelligent rail vehicle speed.

[0008] As a further scheme of the present application, the step of modeling the cooperative control problem of the intelligent rail express system and the intelligent network vehicle as a cooperative game model specifically includes: Under the scenario of the priority of the intelligent rail express system, the minimum decision distance and the maximum communication distance of the roadside unit are calculated; The detection range of each entrance lane of the intersection is determined based on the minimum decision distance and the maximum communication distance of the roadside unit; The safety benefit, efficiency benefit and comfort benefit of the intelligent rail express system and the intelligent network vehicle are calculated respectively.

[0009] As a further scheme of the present application, the step of calculating the minimum decision distance and the maximum communication distance of the roadside unit under the scenario of the priority of the intelligent rail express system specifically includes: The maximum communication distance is calculated based on the modified logarithmic distance path loss model; Under the scenario of the priority of the intelligent rail express system, the minimum decision distance is determined based on the double constraints of real-time response and safety braking.

[0010] As a further scheme of the present application, the safety benefit of the intelligent rail express system is: ; Among them, a is the acceleration when the intelligent rail vehicle is running, a ref is the reference acceleration, V ref is the reference speed, is a normal number; The safety benefit of the intelligent network vehicle is: ; Among them, , p is the probability of the intelligent network vehicle and the intelligent rail vehicle in the same lane, S 变道 represents the safety benefit brought by the intelligent network vehicle avoiding the intelligent rail vehicle by changing lanes, N is the number of lanes, d is the distance between the intelligent network vehicle and the intersection, and D is the detection range.

[0011] As a further scheme of the present application, the efficiency benefit of the intelligent rail rapid transit system is: ; Wherein, , L cross is the length of the intersection, t t is the time required for the ART to accelerate through the intersection at the maximum safe acceleration, t ref is the benchmark time saved; The efficiency benefit of the intelligent network vehicle is: ; Wherein, k j is the block density, V f is the free flow speed, Q ref is the benchmark traffic volume, T is the average delay of conflict vehicles in the conflict area, T ref is the benchmark delay.

[0012] As a further scheme of the present application, the comfort benefit of the intelligent rail rapid transit system is: ; Wherein, a ref is the benchmark acceleration; The comfort benefit of the intelligent network vehicle is: ; Wherein, a vehicle is the acceleration of the intelligent network vehicle.

[0013] The present application also provides a rail rapid transit system control system for a city intersection under a heterogeneous traffic flow, for realizing a rail rapid transit system control method for a city intersection under a heterogeneous traffic flow, and the system comprises: A scene construction module, for constructing a scene model of a conflict area of a city intersection, and ensuring that the intelligent rail rapid transit system has priority in passing based on a green light extension strategy; A game module, for modeling the cooperative control problem of the intelligent rail rapid transit system and the intelligent network vehicle as a cooperative game model; A weight distribution module, for distributing weights to each benefit based on an analytic hierarchy process, and generating a comprehensive benefit function based on the weights; A strategy generation module, for generating a control strategy based on the comprehensive benefit function.

[0014] Compared with the prior art, the present application has the beneficial effects that: the present application can eliminate the traffic safety hazards in the conflict area of the intersection, and the safety benefits of the game participants are guaranteed; without sacrificing the overall passing efficiency of the intersection, the passing efficiency of the ART, the core participant of the game, is improved, and the time of the ART staying in the intersection is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only some embodiments of the present application.

[0016] Figure 1 The flow chart of the ART control strategy for the urban intersection under the heterogeneous traffic flow provided by the embodiment of the present application; Figure 2 The intelligent network vehicle delay distribution diagram of the right turn in the conflict area provided by the embodiment of the present application; Figure 3 The pollutant emission column chart provided by the embodiment of the present application; Figure 4 The CAV fuel consumption scatter diagram provided by the embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the technical problems to be solved by the present application, the technical solutions and beneficial effects more clearly, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not used to limit the present application.

[0018] Figure 1 The flow chart of the ART control strategy for the urban intersection under the heterogeneous traffic flow provided by the embodiment of the present application; The flow chart of the ART control strategy for the urban intersection under the heterogeneous traffic flow provided by the embodiment of the present application; The flow chart of the ART control strategy for the urban intersection under the heterogeneous traffic flow provided by the embodiment of the present application; The flow chart of the ART control strategy for the urban intersection under the heterogeneous traffic flow provided by the embodiment of the present application; The flow chart of the ART control strategy for the urban intersection under the heterogeneous traffic flow provided by the embodiment of the present application;

[0019] In the embodiment, the urban intersection conflict area scene model only considers motor vehicles, and does not consider other traffic elements such as non-motor vehicles and pedestrians.

[0020] The green light extension strategy is considered to realize the ART priority passing, and the signal cycle is not changed. If the remaining green light time can guarantee that the ART passes completely, no measures need to be taken; otherwise, the green light extension strategy is taken, and after the ART passes the intersection, the signal light switches to the next phase.

[0021] As a preferred embodiment of the present application, on the basis of ensuring the priority of the intelligent rail rapid transit system, the delay calculation of the right-turn vehicle is similar to the crossing delay at the unsignalized intersection, assuming that the ART arrival obeys the Poisson distribution, the average delay calculation formula of the intelligent network vehicle right-turning in the conflict area is: ; wherein, , , T1 is the average delay of the intelligent network vehicle right-turning in the conflict area, s; λ is the intelligent rail vehicle arrival rate; T pass is the intelligent rail vehicle passing time, s; T headway is the headway, s. L is the total length of the ART, m; V is the driving speed of the ART, m / s.

[0022] As a preferred embodiment of the present application, the step of modeling the cooperative control problem of the intelligent rail rapid transit system and the intelligent network vehicle as a cooperative game model specifically comprises: calculating the minimum decision distance and the maximum communication distance of the road side unit in the scenario of the priority of the intelligent rail rapid transit system; determining the detection range of each entrance lane of the intersection based on the minimum decision distance and the maximum communication distance of the road side unit; respectively calculating the safety benefit, the efficiency benefit and the comfort benefit of the intelligent rail rapid transit system and the intelligent network vehicle.

[0023] As a preferred embodiment of the present application, the step of calculating the minimum decision distance and the maximum communication distance of the road side unit in the scenario of the priority of the intelligent rail rapid transit system specifically comprises: calculating the maximum communication distance based on the modified logarithmic distance path loss model; determining the minimum decision distance based on the double constraints of real-time response and safety braking in the scenario of the priority of the intelligent rail rapid transit system.

[0024] In the present embodiment, the control range of the RSU is calculated by considering multiple factors such as ensuring the priority of the ART, minimizing the impact on the overall traffic, adapting to the signal control logic, etc.

[0025] To determine the communication coverage range of the RSU, the present application uses the modified logarithmic distance path loss model, which comprehensively considers the free space attenuation, multipath effect and environmental interference, and the path loss is expressed as: ; wherein, PL(d) is the path loss at a distance d from the transmitter, dB; PL0 is the path loss at a reference distance d0, dB; n is the path loss exponent; d is the actual distance, m; d0 is the reference distance, m; Shadow fading margin, dB.

[0026] Combined with the log-distance path loss model, the maximum communication distance D max is expressed as: ; In the formula, D max is the maximum communication distance, m; P t is the transmission power, dBm; S is the receiver sensitivity, dBm; µ is the mean of the shadow fading margin ; z is the quantile of the standard normal distribution related to the required probability; is the standard deviation of the shadow fading margin .

[0027] For the ART priority scenario, the functional control range of the RSU needs to meet the dual constraints of real-time response and safety braking, and the minimum decision distance is expressed as: ; In the formula, D min is the minimum distance required for intelligent network vehicle decision, m; V vehicle is the CAV operating speed, m / s; t responde is the system response time, s; is the CAV deceleration, m / s 2 .

[0028] Considering the maximum communication distance, the minimum decision distance, and giving a certain safety allowance distance, the detection range of each approach of the intersection is finally determined to be 100 m.

[0029] As a preferred embodiment of the present application, the safety benefit of the intelligent rail rapid transit system is: ; Wherein, a is the acceleration of the intelligent rail vehicle when driving, a ref is the reference acceleration, V ref is the reference speed, is a normal number; The safety benefit of the intelligent network vehicle is: ; Wherein, , p is the probability of the intelligent network vehicle and the intelligent rail vehicle in the same lane, S 变道 represents the safety benefit brought by the intelligent network vehicle when avoiding the intelligent rail vehicle by changing lanes, and is usually taken as 1; N is the number of lanes, d is the distance of the intelligent network vehicle from the intersection, D is the detection range, and the probability value p depends on the initial same lane probability, lane changing feasibility, system response time, etc.

[0030] As a preferred embodiment of the present application, the probability calculation formula of the intelligent network vehicle and the intelligent rail vehicle in the same lane is: The efficiency benefit of the intelligent rail rapid transit system is: ; Wherein, , L cross is the length of the intersection, m; t t is the time required for the ART to accelerate through the intersection at the maximum safe acceleration, s; t ref is the reference saving time, s.

[0031] The efficiency benefit of the intelligent network vehicle is: ; Wherein, k j is the blockage density, vehicles / m; V f is the free flow speed, m / s; Q ref is the reference traffic volume, vehicles / s; T is the average delay of conflict vehicles in the conflict area, s; T ref is the reference delay, s.

[0032] As a preferred embodiment of the present application, the comfort benefit of the intelligent rail rapid transit system is: ; Wherein, a ref is the reference acceleration, m / s 2 ; The comfort benefit of the intelligent network vehicle is: ; Wherein, a vehicle is the acceleration of the intelligent network vehicle, m / s 2 .

[0033] The influence of the length of the ART itself on the intersection traffic conditions is considered when calculating the benefit, effectively making up for the shortcomings of traditional research methods.

[0034] The comprehensive benefit is composed of the safety benefit, the efficiency benefit and the comfort benefit, and the comprehensive benefit function is defined as: ; ; According to the analytic hierarchy process, the judgment matrix is set combined with expert opinions, and the weight is obtained by using the characteristic vector method: ; The consistency test of the judgment matrix is passed, and the system comprehensive benefit function is defined as: ; The safety performance of the SUMO built-in original model and the optimized model in the conflict area of the intersection was compared and analyzed. The results showed that the original model frequently appeared vehicle sudden braking behavior in the conflict area, and there was a non-physical crossing phenomenon between vehicles, indicating that its conflict resolution mechanism was insufficient. In contrast, the optimized model effectively avoided sudden braking and vehicle collision problems by restructuring vehicle interaction logic and decision-making mechanism. Qualitative analysis showed that the optimized model could more reasonably coordinate vehicle trajectories, reduce risk conflicts, and thus improve intersection operation safety.

[0035] By setting multiple groups of flow rate ratio parameters through the control variable method, the simulation data of the SUMO built-in original model and the optimized model constructed in this study were compared. As shown in Table 1, compared with the original model, the optimized model proposed in this paper showed better results under different flow combinations, and the average delay of ART was reduced.

[0036] Table 1 ART average delay table

[0037] As shown in Table 2, the optimized model showed better traffic efficiency when the flow rate ratio was low and high. It is worth noting that the overall delay of the optimized model remained stable under different flow rate conditions, without significant fluctuations, showing stronger robustness.

[0038] Table 2 System average delay table

[0039] Through the control variable experiment, the flow rate of westbound straight and right-turn vehicles was gradually increased, the delay distribution of right-turn vehicles under different flow rate combinations was collected, and the data was recorded. As shown in Figure 2 , the delay distribution of right-turn vehicles in the conflict area of the intersection showed a significant bimodal feature. The main peak was concentrated in the time interval of (5, 7] seconds, and there was a secondary peak structure, and with the increase of flow, the amplitude of the secondary peak showed a significant increasing trend.

[0040] The optimized model proposed in this study showed significant improvement in pollutant emissions compared with the SUMO built-in original model. As shown in Figure 3 , the optimized model showed a downward trend in key pollutant emission indicators, with a 6.8% reduction in carbon dioxide (CO2) emissions, a 9.3% reduction in hydrocarbon (HC) emissions, a 55.4% reduction in nitrogen oxide (NO x ) emissions, and a 38.5% reduction in particulate matter (PM x ) emissions.

[0041] In addition, the experimental results show that the average fuel consumption of the original model is 1409 grams, while the average fuel consumption of the optimized model is reduced to 1381 grams, with a reduction of 1.99%, achieving higher fuel economy. As Figure 4 The fuel consumption time sequence characteristics of each CAV in the optimized model are visually characterized by scatter plots, which present the instantaneous energy consumption distribution of each CAV unit and reflect the dynamic evolution process of fuel economy under the collaborative control strategy.

[0042] The ART control strategy under heterogeneous traffic flow provided by the embodiment of the present application can eliminate the traffic safety hazards in the conflict area of the intersection, guarantee the safety benefits of each game participant (i.e., ART and CAV), improve the passing efficiency of the game core participant ART without sacrificing the overall passing efficiency of the intersection, reduce the time of ART staying in the intersection, explore the delay distribution of non-priority participants in the conflict area, compare the delay change trend under different flow rates, and reduce the average fuel consumption and air pollutant emissions.

[0043] The present application also provides a rail rapid transit system control system for urban intersections under heterogeneous traffic flow, which is used to implement the rail rapid transit system control method for urban intersections under heterogeneous traffic flow. The system comprises: A scene construction module is used to construct a conflict area scene model of the urban intersection, and ensure the priority passing of the intelligent rail rapid transit system based on the green light extension strategy; A game module is used to model the cooperative control problem of the intelligent rail rapid transit system and the intelligent network vehicle as a cooperative game model; A weight allocation module is used to allocate weights to each item of income based on the analytic hierarchy process, and generate a comprehensive income function based on the weights; A strategy generation module is used to generate a control strategy based on the comprehensive income function.

[0044] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A control method for urban rail rapid transit systems at heterogeneous traffic flows, characterized in that, The method includes: Construct a scenario model of conflict zones at urban intersections, and ensure priority passage for the intelligent rail transit system based on the green light extension strategy; The collaborative control problem of intelligent rail transit systems and intelligent network vehicles is modeled as a cooperative game theory model. Weights are assigned to various revenue streams based on the analytic hierarchy process, and a comprehensive revenue function is generated based on these weights. Control strategies are generated based on comprehensive revenue functions.

2. The control method for urban intersection rail rapid transit system under heterogeneous traffic flow according to claim 1, characterized in that, Based on ensuring priority passage for the intelligent rail transit system, the formula for calculating the average delay of intelligent network vehicles turning right in the conflict zone is as follows: ； in, , T1 represents the average delay of intelligent network vehicles turning right in the conflict zone; λ represents the arrival rate of intelligent rail vehicles; T pass T represents the transit time of the intelligent rail vehicle. headway L is the headway; L is the total length of the intelligent rail vehicle; V is the speed of the intelligent rail vehicle.

3. The control method for urban intersection rail rapid transit system under heterogeneous traffic flow according to claim 1, characterized in that, The steps for modeling the collaborative control problem of intelligent rail transit systems and intelligent network vehicles as a cooperative game model specifically include: In scenarios where priority is given to intelligent rail transit systems, calculate the minimum decision distance and maximum communication distance of roadside units; The detection range of each approach lane at the intersection is determined based on the minimum decision distance and maximum communication distance of the roadside unit; Calculate the safety benefits, efficiency benefits, and comfort benefits of intelligent rail transit systems and intelligent network vehicles respectively.

4. The control method for urban intersection rail rapid transit system under heterogeneous traffic flow according to claim 3, characterized in that, In the scenario where priority passage is given to the intelligent rail transit system, the steps for calculating the minimum decision distance and maximum communication distance of the roadside unit specifically include: The maximum communication distance is calculated based on the modified logarithmic distance path loss model; In scenarios where priority passage is prioritized in intelligent rail transit systems, the minimum decision distance is determined based on the dual constraints of real-time response and safe braking.

5. The control method for urban intersection rail rapid transit system under heterogeneous traffic flow according to claim 3, characterized in that, The safety benefits of the intelligent rail transit system are: ; Where a is the acceleration of the intelligent rail vehicle during operation, a ref As the reference acceleration, V ref As the reference speed, It is a positive number; The security benefits of intelligent networked vehicles are: ; in, Let p be the probability that the intelligent network vehicle and the intelligent rail vehicle are in the same lane, and S be the probability that they are in the same lane. 变道 This represents the safety benefits of intelligent network vehicles changing lanes to avoid intelligent rail vehicles, where N is the number of lanes, d is the distance between the intelligent network vehicle and the intersection, and D is the detection range.

6. The control method for urban intersection rail rapid transit system under heterogeneous traffic flow according to claim 5, characterized in that, The efficiency benefits of the intelligent rail transit system are: ; in, L cross t is the length of the intersection. t t is the time required for the ART to accelerate through the intersection at maximum safe acceleration. ref Save time for benchmarks; The efficiency gains of intelligent networked vehicles are: ; Where, k j For the blocking density, V f Q is the free flow velocity. ref The baseline traffic volume is T, where T is the average delay of vehicles involved in conflict in the conflict zone. ref The baseline delay.

7. The control method for urban intersection rail rapid transit system under heterogeneous traffic flow according to claim 6, characterized in that, The comfort benefits of the intelligent rail transit system are: ; Among them, a ref As the reference acceleration; The comfort benefits of intelligent network vehicles are: ; Among them, a vehicle Accelerating the development of intelligent connected vehicles.

8. A control system for a rail transit system at an urban intersection under heterogeneous traffic flow, used to implement the control method for a rail transit system at an urban intersection under heterogeneous traffic flow as described in any one of claims 1-7, characterized in that, The system includes: The scenario building module is used to build scenario models of conflict zones at urban intersections, and to ensure priority passage for the intelligent rail transit system based on the green light extension strategy; The game theory module is used to model the collaborative control problem of intelligent rail transit systems and intelligent network vehicles as a cooperative game model; The weight allocation module is used to assign weights to various returns based on the analytic hierarchy process and generate a comprehensive return function based on the weights. The strategy generation module is used to generate control strategies based on the comprehensive return function.

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