Intersection main road entrance lane control strategy design method in heterogeneous traffic environment

Abstract

The invention discloses an intersection main road entrance lane control strategy design method in a heterogeneous traffic environment, relates to the field of electronic design automation, and comprises the following steps: collecting intersection road condition information, vehicle information and intersection signal control parameters in real time, and setting an entrance lane lane function according to the road condition information; the method comprises the following steps: setting car-following models of various types of vehicles by considering the vehicle type difference of manual driving and automatic driving in a heterogeneous traffic environment; setting a bus priority signal control mode based on the speed data of the bus arriving at the intersection and the intersection signal parameters; setting lane changing models of various types of vehicles and lane borrowing and leaving rules of bus lanes according to the driving states of the vehicles and in combination with vehicle lane changing motivation and safety conditions; a cost-benefit analysis method is adopted, the intersection traffic operation efficiency and the road resource utilization condition are combined, a trigger mechanism for complete sharing of the bus lanes is set, and a complete control strategy is formed. According to the method, the intersection operation efficiency can be remarkably improved, and the improvement range is not lower than 20%.

Description

Technical Field

[0001] This invention relates to the field of urban traffic management and control technology, and in particular to a method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments. Background Technology

[0002] Intersections are key nodes where urban traffic flows converge and transform. Traditional intersection control schemes, through measures such as lane function allocation, optimized signal timing, and standardized lane-changing behavior, have to some extent balanced the traffic demand of private vehicles with the priority rights of public transport, alleviating intersection congestion and reducing overall vehicle delays. However, with the development of autonomous driving technology, the traffic environment will gradually become a heterogeneous form where human-driven and autonomous vehicles coexist. The two differ significantly in terms of safe distance, acceleration and deceleration, and communication and coordination capabilities, making traditional control schemes insufficiently adaptable.

[0003] Although there are studies in the field exploring traffic control with the integration of autonomous vehicles, existing research cannot simultaneously address the needs of public transport priority, multi-traffic coordination, and efficient resource utilization under high demand, and is difficult to cope with the multi-objective balance requirements in heterogeneous scenarios.

[0004] Therefore, a method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments is provided to address the aforementioned issues. Summary of the Invention

[0005] To address the aforementioned challenges, this invention provides a method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments. This method adapts to heterogeneous traffic patterns where manual and autonomous vehicles coexist, balances the priority rights of public transportation with the needs of private vehicles, and improves the efficiency of intersection traffic operations and the utilization rate of road resources.

[0006] To achieve the above objectives, this invention provides a method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments, comprising the following steps: S1: Collects real-time traffic information, vehicle information, and intersection signal control parameters at the intersection, and sets the lane functions of the approach lane based on the traffic information; S2: Based on the safe distance, expected speed, and acceleration / deceleration characteristics of different types of vehicles, set up a car-following model for the corresponding vehicle type; S3: Based on the speed data of buses arriving at the intersection and the intersection signal parameters, set a bus priority signal control method that extends the green light and cuts off the red light; S4: Based on the vehicle's driving status, combined with the vehicle's lane-changing motivation and safety conditions, set lane-changing models for manually driven interactive vehicles, autonomous driving interactive vehicles, manually driven buses, and autonomous driving buses, as well as lane-borrowing and exiting rules for bus lanes. S5: Using a cost-benefit analysis approach, combined with the traffic efficiency at intersections and the utilization of road resources, a trigger mechanism for the full sharing of bus lanes is established to form a complete control strategy.

[0007] Preferably, the traffic information for the S1 intersection includes at least the number of lanes on the main road approach, the length of the approach, and the maximum and minimum speed limits on the upstream section of the approach. Vehicle information includes at least the location, speed, acceleration, bus departure frequency during peak hours, traffic volume of different types of vehicles during peak and off-peak hours, vehicle arrival time distribution, lane changing frequency, and trigger probability of all vehicles at the main road approach lane of the intersection at any time. The signal control parameters for an intersection should include at least the signal cycle duration and the green light duration for each phase.

[0008] Preferably, the entrance lane is configured as follows: the first lane is set as a bus-only lane, where buses travel; the remaining lanes are set as regular lanes, which allow regular vehicles and buses turning right to pass.

[0009] Preferably, in S2, in the vehicle following model, the manual driving category is set according to conventional driving characteristics and power performance, including manual driving interactive vehicles and manual driving buses; the autonomous driving category is set according to its communication and cooperative control characteristics, including autonomous driving interactive vehicles and autonomous driving buses. In all cases, both human-driven interactive vehicles and human-driven buses use the Intelligent Driver Model (IDM) as the car-following model. The calculation method for the IDM model is as follows: ; In the formula: Let n be the acceleration of the manually driven vehicle n at time t; This represents the maximum acceleration of a manually driven vehicle. Let be the speed of the manually driven vehicle i at time t; For vehicle i The speed difference between vehicle i and manually driven vehicle i at time t; The desired following distance; For vehicle i 1. The following distance between the manually driven vehicle i and the manually driven vehicle i at time t; The safe headway for manually driven vehicles; For the driver's free-flow speed; Minimum safe parking distance; To reduce speed for comfortable manual driving; δ is a coefficient, with a value range of [0,1]; δ is a constant coefficient. When the preceding vehicle in an autonomous interactive vehicle or autonomous bus is a non-internet-connected vehicle or there is no vehicle, Adaptive Cruise Control (ACC) is used as the car-following model. The calculation method for the ACC model is as follows: ; In the formula: Let be the distance between vehicle i and the vehicle in front at time t; T is the expected headway of the intelligent connected vehicle; l is the vehicle length. and This is a control coefficient, taking values ​​within the interval (0,1). When the lead vehicle for the autonomous interactive vehicle and the autonomous bus is an autonomous vehicle, the Cooperative Adaptive Cruise Control (CACC) mode is used as the car-following model. The calculation method of the CACC model is as follows: ; In the formula: Δt is the control step size of the intelligent connected vehicle system; For intelligent connected vehicles The speed of time; Let t be the error between the actual headway of the intelligent connected vehicle and the vehicle in front and the expected headway at time t; for The differential term with respect to time t; This is the vehicle spacing error control coefficient. This is the control coefficient for the differential term of the vehicle spacing error.

[0010] Preferably, in S3, the specific steps are as follows: S31: Obtain the minimum green light time for a non-priority phase based on the vehicle queue length and arrival rate of the non-priority phase. S32: Based on the minimum green time of the non-priority phase obtained in S31, calculate the maximum extended green time of the priority phase: ; In the formula: Non-priority phase k The initial green light time; The compressed green light time; The minimum green light limit for this phase; S33: When the main road is red, based on the signal timing scheme after the green light at the intersection is extended, detect the position and speed of vehicles in the bus lane, determine whether the bus arrives in the red light phase, calculate the red light duration that needs to be shortened, and if it does not exceed the difference between the maximum green light duration that can be extended and the already extended green light duration, cut off the red light.

[0011] Preferably, in S4, the manual driving class and the automatic driving class adopt the symmetric two-lane cellular automaton STCA lane-changing model and the overall braking model MOBIL based on minimizing lane-changing, respectively; STCA specifically refers to driverless interactive vehicles and driverless buses as follows: ;

[0012] In the formula: The distance between the target vehicle and the vehicle in front after the target vehicle changes lanes; This refers to the distance between the vehicle behind and the vehicle in front in the target lane after the target vehicle changes lanes. The acceleration of the vehicle following the target vehicle in the target lane after the target vehicle changes lanes; Minimum safe distance; To reduce speed for safety; For autonomous interactive vehicles and autonomous buses, the comprehensive lane-changing benefits are calculated using MOBIL: ; middle: To achieve comprehensive acceleration benefits from lane changing; , and These represent the accelerations of the target vehicle, the original following vehicle, and the following vehicle in the target lane before the lane change, respectively. and These represent the accelerations of the target vehicle and the original following vehicle after they change lanes, respectively. To accelerate the threshold; This is the courtesy coefficient.

[0013] Preferably, in S4, rules for autonomous driving interactive vehicles to use bus lanes should be established: When the autonomous driving interactive vehicle encounters poor conditions in a regular lane, it determines whether the adjacent regular lane meets the conditions for lane changing. If it does, it changes to that lane; otherwise, it assesses the conditions of the bus lane and checks whether there are any following buses within the communication range. If there are subsequent buses, it will be further determined whether changing lanes will affect the expected speed of the buses. If it does not affect the expected speed of the buses, entry will be permitted. If there is no subsequent bus, you may enter if you meet the requirements for changing lanes in the bus lane. Establish rules for autonomous interactive vehicles leaving bus lanes: When the conditions of the bus lane are not good, the autonomous driving interactive vehicle will prioritize switching to the adjacent ordinary lane if the conditions for lane switching are met. If the autonomous driving interactive vehicle affects the driving of subsequent buses and the adjacent ordinary lane meets the safety conditions, a lane change will be forced. If the autonomous driving interactive vehicle affects the driving of subsequent buses and the adjacent ordinary lane does not meet the safety conditions, it will coordinate with other autonomous driving vehicles to change lanes, and will change lanes after the target lane meets the conditions.

[0014] Preferably, step S5 specifically includes the following steps: S51, set up core evaluation indicators for cost-benefit analysis, traffic operation efficiency indicators should at least cover the average vehicle delay on road segments and the capacity of intersections; road resource utilization indicators should at least cover the utilization rate of bus lanes and the occupancy rate of lane space-time resources. S52: Perform traffic flow operation tests and obtain values ​​for each indicator based on basic intersection information, traffic flow operation information, and vehicle characteristic information; S53: Set the threshold range for each evaluation indicator: When the average vehicle delay on the road segment is ≥a, the intersection capacity is ≤γ times the designed capacity, the bus lane utilization rate is ≤b, the lane space-time resource occupancy rate is ≤c, the greenhouse gas emissions per unit time on the road segment are ≥d, and the air pollutant emissions per unit time on the road segment are ≥e, the preliminary triggering conditions for the full sharing of bus lanes are met; the specific values ​​of the threshold parameters a, γ, b, c, d, and e are determined according to the actual characteristics of the study area; S54: Establish a comprehensive cost-benefit assessment model, assign weights to each evaluation indicator using the analytic hierarchy process (AHP), and calculate the comprehensive benefit value. ;

[0015] In the formula: q represents the total number of evaluation indicators; Let be the weight of the j-th indicator; is the standardized score of the j-th indicator; S55: Set trigger and termination thresholds for full sharing of bus lanes: when the comprehensive benefit value S ≥ Furthermore, all initial triggering conditions in S53 are met, and the state duration is ≥ At that time, the bus lane will be fully shared; When the comprehensive benefit value S < Or, if any of the initial triggering conditions in S53 is not met, and the duration of the state is ≥ When this happens, the full sharing of the bus lane will be terminated, and the bus lane will be restored to its dedicated use status; among which, the trigger threshold... Trigger duration Termination threshold Termination duration The specific value is determined based on the dynamic traffic conditions, public transport operation needs, and cost-benefit balance objectives of the actual application scenario.

[0016] Preferably, the proportion of autonomous buses is defined in S52. The proportion of autonomous buses is specifically expressed as the percentage of autonomous buses among all buses in the main road approach lane at an intersection. The value range is [0,1]; the proportion of autonomous driving interactive vehicles The value range is [0,1]; determine the adjustment interval. , build The study explored various combinations of autonomous vehicles to cover heterogeneous traffic scenarios with different levels of autonomous vehicle penetration. Under different combinations of autonomous vehicle proportions, traffic flow operation tests were conducted to collect data on average vehicle delays on road segments, intersection capacity, bus lane utilization, and lane space-time resource occupancy rates.

[0017] Therefore, the present invention adopts the above-mentioned design method for the control strategy of the main road approach lane at an intersection under heterogeneous traffic environment, which has the following beneficial effects: (1) By distinguishing the car-following and lane-changing characteristics of manually driven vehicles and autonomous vehicles, this invention solves the problem of insufficient adaptability of traditional control schemes to heterogeneous vehicle characteristics, and can be compatible with traffic scenarios with different levels of autonomous vehicle penetration.

[0018] (2) This invention not only safeguards the priority rights of public transport by setting up dedicated bus lanes and extending green lights and cutting off red lights, but also meets the traffic needs of social vehicles by using the dynamic lane borrowing and leaving rules of autonomous driving interactive vehicles and the shared triggering mechanism of dedicated bus lanes, thus achieving a balance between public transport priority and efficient resource utilization.

[0019] (3) The present invention constructs a comprehensive judgment model based on cost-benefit analysis, and uses dynamic thresholds of multiple dimensions such as delay, traffic capacity, and utilization rate to trigger a sharing mechanism, thereby avoiding the limitations of static control and improving the flexibility and scenario adaptability of the control strategy.

[0020] (4) By optimizing lane functions, vehicle driving rules and signal control logic, this invention effectively reduces the average delay of vehicles on road sections, improves the traffic capacity of intersections, and at the same time improves the utilization rate of time and space resources of bus lanes and reduces the waste of traffic resources.

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] Figure 1 This is a flowchart of the design method for the control strategy of the main road approach lane at an intersection in a heterogeneous traffic environment according to the present invention. Figure 2 This is a schematic diagram illustrating the functional setup of the inlet channel in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the use of a dedicated bus lane by an autonomous driving interactive vehicle in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the vehicle operation at the main road approach lane of an intersection, provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0025] The terms "comprising" or "including" as used in this invention mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements as well. Terms such as "inner," "outer," "upper," and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this invention, unless otherwise explicitly specified and limited, the term "attached" and similar terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Example like Figure 1 As shown, the design method for the control strategy of the main road approach lane at an intersection in a heterogeneous traffic environment includes the following steps: S1: Collects real-time traffic information, vehicle information, and intersection signal control parameters at the intersection, and sets the lane functions of the approach lane based on the traffic information; The traffic information for the S1 intersection should include at least the number of lanes on the main road approach, the length of the approach lane, and the maximum and minimum speed limits on the upstream road section of the approach lane; the main road approach lanes at the intersection must meet the standards for bus lanes in the "Setting of Bus Lanes" (GA / T 507-2004). Vehicle information includes at least the location, speed, acceleration, bus departure frequency during peak hours, traffic volume of different types of vehicles during peak and off-peak hours, vehicle arrival time distribution, lane changing frequency, and trigger probability of all vehicles at the main road approach lane of the intersection at any time. The signal control parameters for an intersection should include at least the signal cycle duration and the green light duration for each phase.

[0027] like Figure 2 As shown, the functional settings of the main intersection entrance lanes are as follows: the first lane is set as a bus-only lane, and buses travel in the bus-only lane; the remaining lanes are set as regular lanes, which allow regular vehicles and buses turning right to pass.

[0028] S2: Based on the safe distance, expected speed, and acceleration / deceleration characteristics of different types of vehicles, set up a car-following model for the corresponding vehicle type; In S2, the vehicle following model is divided into two categories: manual driving and autonomous driving. The manual driving category is set according to conventional driving characteristics and power performance. The manual driving category includes manual driving interactive vehicles and manual driving buses. The autonomous driving category is set according to its communication and cooperative control characteristics. The autonomous driving category includes autonomous driving interactive vehicles and autonomous driving buses. In all cases, both human-driven interactive vehicles and human-driven buses use the Intelligent Driver Model (IDM) as the car-following model. The calculation method for the IDM model is as follows: ; In the formula: Let n be the acceleration of the manually driven vehicle n at time t; This represents the maximum acceleration of a manually driven vehicle. Let be the speed of the manually driven vehicle i at time t; For vehicle i The speed difference between vehicle i and manually driven vehicle i at time t; The desired following distance; For vehicle i 1. The following distance between the manually driven vehicle i and the manually driven vehicle i at time t; The safe headway for manually driven vehicles; For the driver's free-flow speed; Minimum safe parking distance; To reduce speed for comfortable manual driving; δ is a coefficient, with a value range of [0,1]; δ is a constant coefficient. When the preceding vehicle in an autonomous interactive vehicle or autonomous bus is a non-internet-connected vehicle or there is no vehicle, Adaptive Cruise Control (ACC) is used as the car-following model. The calculation method for the ACC model is as follows: ; In the formula: Let be the distance between vehicle i and the vehicle in front at time t; T is the expected headway of the intelligent connected vehicle; l is the vehicle length. and This is a control coefficient, taking values ​​within the interval (0,1). When the lead vehicle for the autonomous interactive vehicle and the autonomous bus is an autonomous vehicle, the Cooperative Adaptive Cruise Control (CACC) mode is used as the car-following model. The calculation method of the CACC model is as follows: ; In the formula: Δt is the control step size of the intelligent connected vehicle system; For intelligent connected vehicles The speed of time; Let t be the error between the actual headway of the intelligent connected vehicle and the vehicle in front and the expected headway at time t; for The differential term with respect to time t; This is the vehicle spacing error control coefficient. This is the control coefficient for the differential term of the vehicle spacing error.

[0029] S3: Based on the speed data of buses arriving at the intersection and the intersection signal parameters, set a bus priority signal control method that extends the green light and cuts off the red light; In S3, the specific steps are as follows: S31: Obtain the minimum green light time for a non-priority phase based on the vehicle queue length and arrival rate of the non-priority phase. S32: Based on the minimum green time of the non-priority phase obtained in S31, calculate the maximum extended green time of the priority phase: ;

[0030] In the formula: Non-priority phase k The initial green light time; The compressed green light time; The minimum green light limit for this phase; S33: When the main road is red, based on the signal timing scheme after the green light at the intersection is extended, detect the position and speed of vehicles in the bus lane, determine whether the bus arrives in the red light phase, calculate the red light duration that needs to be shortened, and if it does not exceed the difference between the maximum green light duration that can be extended and the already extended green light duration, cut off the red light.

[0031] S4: Based on the vehicle's driving status, combined with the vehicle's lane-changing motivation and safety conditions, set lane-changing models for manually driven interactive vehicles, autonomous driving interactive vehicles, manually driven buses, and autonomous driving buses, as well as lane-borrowing and exiting rules for bus lanes. In S4, the manual driving class and the automatic driving class respectively adopt the symmetric two-lane cellular automaton STCA lane-changing model and the overall braking model MOBIL based on minimizing lane-changing; STCA specifically refers to driverless interactive vehicles and driverless buses as follows: ;

[0032] In the formula: The distance between the target vehicle and the vehicle in front after the target vehicle changes lanes; This refers to the distance between the vehicle behind and the vehicle in front in the target lane after the target vehicle changes lanes. The acceleration of the vehicle following the target vehicle in the target lane after the target vehicle changes lanes; Minimum safe distance; To reduce speed for safety; For autonomous interactive vehicles and autonomous buses, the comprehensive lane-changing benefits are calculated using MOBIL: ; middle: To achieve comprehensive acceleration benefits from lane changing; , and These represent the accelerations of the target vehicle, the original following vehicle, and the following vehicle in the target lane before the lane change, respectively. and These represent the accelerations of the target vehicle and the original following vehicle after they change lanes, respectively. To accelerate the threshold; This is the courtesy coefficient.

[0033] In S4, rules for autonomous driving interactive vehicles to use bus lanes will be established: like Figure 3 As shown, when the conditions in the ordinary lane are poor, the autonomous driving interactive vehicle determines whether the adjacent ordinary lane meets the lane-changing conditions. If it does, it changes to that lane; if not, it assesses the conditions of the bus lane and detects whether there are any following buses within the communication range. If there are subsequent buses, it will be further determined whether changing lanes will affect the expected speed of the buses. If it does not affect the expected speed of the buses, entry will be permitted. If there is no subsequent bus, you can enter the bus lane if you meet the requirements for changing lanes.

[0034] Establish rules for autonomous interactive vehicles leaving bus lanes: When the conditions of the bus lane are not good, the autonomous driving interactive vehicle will prioritize switching to the adjacent ordinary lane if the conditions for lane switching are met. If the autonomous driving interactive vehicle affects the driving of subsequent buses and the adjacent ordinary lane meets the safety conditions, a lane change will be forced. If the autonomous driving interactive vehicle affects the driving of subsequent buses and the adjacent ordinary lane does not meet the safety conditions, it will coordinate with other autonomous driving vehicles to change lanes, and will change lanes after the target lane meets the conditions.

[0035] S5: Using a cost-benefit analysis approach, and considering the traffic efficiency at intersections and road resource utilization, establish a trigger mechanism for fully shared bus lanes to form a complete control strategy. Step S5 specifically includes the following steps: S51, set up core evaluation indicators for cost-benefit analysis, traffic operation efficiency indicators should at least cover the average vehicle delay on road segments and the capacity of intersections; road resource utilization indicators should at least cover the utilization rate of bus lanes and the occupancy rate of lane space-time resources. S52: Perform traffic flow operation tests and obtain values ​​for each indicator based on basic intersection information, traffic flow operation information, and vehicle characteristic information; S53: Set threshold ranges for each evaluation indicator: When the average vehicle delay per segment is ≥a, the intersection capacity is ≤γ times the designed capacity, the bus lane utilization rate is ≤b, the lane space-time resource occupancy rate is ≤c, the greenhouse gas emissions per unit time per segment are ≥d, and the air pollutant emissions per unit time per segment are ≥e, the preliminary triggering conditions for full sharing of bus lanes are met; the specific values ​​of the threshold parameters a, γ, b, c, d, and e are determined based on the actual characteristics of the study area, including but not limited to road segment geometric parameters, historical peak and off-peak traffic volume data, and local traffic management and environmental protection policy requirements; S54: Establish a comprehensive cost-benefit assessment model, assign weights to each evaluation indicator using the analytic hierarchy process (AHP), and calculate the comprehensive benefit value. ;

[0036] In the formula: q represents the total number of evaluation indicators; Let be the weight of the j-th indicator; is the standardized score of the j-th indicator; S55: Set trigger and termination thresholds for full sharing of bus lanes: when the comprehensive benefit value S ≥ Furthermore, all initial triggering conditions in S53 are met, and the state duration is ≥ At that time, the bus lane will be fully shared; When the comprehensive benefit value S < Or, if any of the initial triggering conditions in S53 is not met, and the duration of the state is ≥ When this happens, the full sharing of the bus lane will be terminated, and the bus lane will be restored to its dedicated use status; among which, the trigger threshold... Trigger duration Termination threshold Termination duration The specific values ​​are determined based on the dynamic traffic conditions, public transport operation needs, and cost-benefit balance objectives of the actual application scenario. References include, but are not limited to, micro-traffic simulation verification results, environmental benefit assessment data, and financial feasibility analysis to ensure that the parameter values ​​match the actual traffic system operation characteristics and management objectives.

[0037] Traffic conditions at the main road approach lanes of the intersection as follows Figure 4 As shown, the proportion of autonomous buses is defined in S52. The proportion of autonomous buses is specifically expressed as the percentage of autonomous buses among all buses in the main road approach lane at an intersection. The value range is [0,1]; the proportion of autonomous driving interactive vehicles The value range is [0,1]; determine the adjustment interval. , build The study explored various combinations of autonomous vehicles to cover heterogeneous traffic scenarios with different levels of autonomous vehicle penetration. Under different combinations of autonomous vehicle proportions, traffic flow operation tests were conducted to collect data on average vehicle delays on road segments, intersection capacity, bus lane utilization, and lane space-time resource occupancy rates.

[0038] Therefore, this invention adopts the aforementioned design method for the main road approach lane control strategy at intersections in heterogeneous traffic environments. By collecting intersection information in real time and setting lane functions, it implements bus priority signal control, formulates differentiated lane-changing rules and bus lane sharing mechanisms, and finally sets shared trigger conditions for bus lanes based on cost-benefit analysis, forming a complete control strategy. This invention solves the problem of insufficient adaptability of traditional control schemes to heterogeneous vehicle characteristics; achieves a balance between bus priority and efficient resource utilization; improves the flexibility and scenario adaptability of the control strategy; effectively reduces the average vehicle delay on road segments, improves intersection capacity, increases intersection operating efficiency by at least 20%, and simultaneously improves the spatial and temporal resource utilization rate of bus lanes.

[0039] This solution is not limited to the above-mentioned best implementation method. Anyone can derive other forms of intersection main road approach control strategy design methods under heterogeneous traffic environments based on the inspiration of this solution. All equivalent changes and modifications made within the scope of the patent application of this solution shall be covered by this solution.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments, characterized in that, Includes the following steps: S1: Collects real-time traffic information, vehicle information, and intersection signal control parameters at the intersection, and sets the lane functions of the approach lane based on the traffic information; S2: Based on the safe distance, expected speed, and acceleration / deceleration characteristics of different types of vehicles, set up a car-following model for the corresponding vehicle type; S3: Based on the speed data of buses arriving at the intersection and the intersection signal parameters, set a bus priority signal control method that extends the green light and cuts off the red light; S4: Based on the vehicle's driving status, combined with the vehicle's lane-changing motivation and safety conditions, set lane-changing models for manually driven interactive vehicles, autonomous driving interactive vehicles, manually driven buses, and autonomous driving buses, as well as lane-borrowing and exiting rules for bus lanes. S5: Using a cost-benefit analysis approach, combined with the traffic efficiency at intersections and the utilization of road resources, a trigger mechanism for the full sharing of bus lanes is established to form a complete control strategy.

2. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 1, characterized in that: Traffic information for the S1 intersection should include at least the number of lanes on the main road approach, the length of the approach lane, and the maximum and minimum speed limits on the upstream section of the approach lane. Vehicle information includes at least the location, speed, acceleration, bus departure frequency during peak hours, traffic volume of different types of vehicles during peak and off-peak hours, vehicle arrival time distribution, lane changing frequency, and trigger probability of all vehicles at the main road approach lane of the intersection at any time. The signal control parameters for an intersection should include at least the signal cycle duration and the green light duration for each phase.

3. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 2, characterized in that: The functions of the entrance lanes are set as follows: the first lane is designated as a bus-only lane, where buses travel; the remaining lanes are designated as regular lanes, allowing regular vehicles and buses turning right to pass.

4. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 3, characterized in that: In S2, the vehicle following model is divided into two categories: manual driving and autonomous driving. The manual driving category is set according to conventional driving characteristics and power performance. The manual driving category includes manual driving interactive vehicles and manual driving buses. The autonomous driving category is set according to its communication and cooperative control characteristics. The autonomous driving category includes autonomous driving interactive vehicles and autonomous driving buses. In all cases, both human-driven interactive vehicles and human-driven buses use the Intelligent Driver Model (IDM) as the car-following model. The calculation method for the IDM model is as follows: ； In the formula: Let n be the acceleration of the manually driven vehicle n at time t; This represents the maximum acceleration of a manually driven vehicle. Let be the speed of the manually driven vehicle i at time t; For vehicle i The speed difference between vehicle i and manually driven vehicle i at time t; The desired following distance; For vehicle i 1. The following distance between the manually driven vehicle i and the manually driven vehicle i at time t; The safe headway for manually driven vehicles; For the driver's free-flow speed; Minimum safe parking distance; To reduce speed for comfortable manual driving; δ is a coefficient, with a value range of [0,1]; δ is a constant coefficient. When the preceding vehicle in an autonomous interactive vehicle or autonomous bus is a non-internet-connected vehicle or there is no vehicle, Adaptive Cruise Control (ACC) is used as the car-following model. The calculation method for the ACC model is as follows: ； In the formula: Let be the distance between vehicle i and the vehicle in front at time t; T is the expected headway of the intelligent connected vehicle; l is the vehicle length. and This is a control coefficient, taking values ​​within the interval (0,1). When the lead vehicle for the autonomous interactive vehicle and the autonomous bus is an autonomous vehicle, the Cooperative Adaptive Cruise Control (CACC) mode is used as the car-following model. The calculation method of the CACC model is as follows: ； In the formula: Δt is the control step size of the intelligent connected vehicle system; For intelligent connected vehicles The speed of time; Let t be the error between the actual headway of the intelligent connected vehicle and the vehicle in front and the expected headway at time t; for The differential term with respect to time t; This is the vehicle spacing error control coefficient. This is the control coefficient for the differential term of the vehicle spacing error.

5. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 4, characterized in that: In S3, the specific steps are as follows: S31: Obtain the minimum green light time for a non-priority phase based on the vehicle queue length and arrival rate of the non-priority phase. S32: Based on the minimum green time of the non-priority phase obtained in S31, calculate the maximum extended green time of the priority phase: ； In the formula: Non-priority phase k The initial green light time; The compressed green light time; The minimum green light limit for this phase; S33: When the main road is red, based on the signal timing scheme after the green light at the intersection is extended, detect the position and speed of vehicles in the bus lane, determine whether the bus arrives in the red light phase, calculate the red light duration that needs to be shortened, and if it does not exceed the difference between the maximum green light duration that can be extended and the already extended green light duration, cut off the red light.

6. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 5, characterized in that: In S4, the manual driving class and the automatic driving class respectively adopt the symmetric two-lane cellular automaton STCA lane-changing model and the overall braking model MOBIL based on minimizing lane-changing; STCA specifically refers to driverless interactive vehicles and driverless buses as follows: ； In the formula: The distance between the target vehicle and the vehicle in front after the target vehicle changes lanes; This refers to the distance between the vehicle behind and the vehicle in front in the target lane after the target vehicle changes lanes. The acceleration of the vehicle following the target vehicle in the target lane after the target vehicle changes lanes; Minimum safe distance; To reduce speed for safety; For autonomous interactive vehicles and autonomous buses, the comprehensive lane-changing benefits are calculated using MOBIL: ； In the formula: To achieve comprehensive acceleration benefits from lane changing; , and These represent the accelerations of the target vehicle, the original following vehicle, and the following vehicle in the target lane before the lane change, respectively. and These represent the accelerations of the target vehicle and the original following vehicle after they change lanes, respectively. To accelerate the threshold; This is the courtesy coefficient.

7. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 6, characterized in that: In S4, rules for autonomous driving interactive vehicles to use bus lanes will be established: When the autonomous driving interactive vehicle encounters poor conditions in a regular lane, it determines whether the adjacent regular lane meets the conditions for lane changing. If it does, it changes to that lane; otherwise, it assesses the conditions of the bus lane and checks whether there are any following buses within the communication range. If there are subsequent buses, it will be further determined whether changing lanes will affect the expected speed of the buses. If it does not affect the expected speed of the buses, entry will be permitted. If there is no subsequent bus, you may enter if you meet the requirements for changing lanes in the bus lane. Establish rules for autonomous interactive vehicles leaving bus lanes: When the conditions of the bus lane are not good, the autonomous driving interactive vehicle will prioritize switching to the adjacent ordinary lane if the conditions for lane switching are met. If the autonomous driving interactive vehicle affects the driving of subsequent buses and the adjacent ordinary lane meets the safety conditions, a lane change will be forced. If the autonomous driving interactive vehicle affects the driving of subsequent buses and the adjacent ordinary lane does not meet the safety conditions, it will coordinate with other autonomous driving vehicles to change lanes, and will change lanes after the target lane meets the conditions.

8. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 7, characterized in that: Step S5 specifically includes the following steps: S51, set up core evaluation indicators for cost-benefit analysis, traffic operation efficiency indicators should at least cover the average vehicle delay on road segments and the capacity of intersections; road resource utilization indicators should at least cover the utilization rate of bus lanes and the occupancy rate of lane space-time resources. S52: Perform traffic flow operation tests and obtain values ​​for each indicator based on basic intersection information, traffic flow operation information, and vehicle characteristic information; S53: Set the threshold range for each evaluation indicator: When the average vehicle delay on the road segment is ≥a, the intersection capacity is ≤γ times the designed capacity, the bus lane utilization rate is ≤b, the lane space-time resource occupancy rate is ≤c, the greenhouse gas emissions per unit time on the road segment are ≥d, and the air pollutant emissions per unit time on the road segment are ≥e, the preliminary triggering conditions for the full sharing of bus lanes are met; the specific values ​​of the threshold parameters a, γ, b, c, d, and e are determined according to the actual characteristics of the study area; S54: Establish a comprehensive cost-benefit assessment model, assign weights to each evaluation indicator using the analytic hierarchy process (AHP), and calculate the comprehensive benefit value. ； In the formula: q represents the total number of evaluation indicators; Let be the weight of the j-th indicator; is the standardized score of the j-th indicator; S55: Set trigger and termination thresholds for full sharing of bus lanes: when the comprehensive benefit value S ≥ Furthermore, all initial triggering conditions in S53 are met, and the state duration is ≥ At that time, the bus lane will be fully shared; When the comprehensive benefit value S < Or, if any of the initial triggering conditions in S53 is not met, and the duration of the state is ≥ When this happens, the full sharing of the bus lane will be terminated, and the bus lane will be restored to its dedicated use status; among which, the trigger threshold... Trigger duration Termination threshold Termination duration The specific value is determined based on the dynamic traffic conditions, public transport operation needs, and cost-benefit balance objectives of the actual application scenario.

9. The method for designing control strategies for main road approach lanes at intersections in heterogeneous traffic environments as described in claim 8, characterized in that: S52 defines the proportion of autonomous buses. The proportion of autonomous buses is specifically expressed as the percentage of autonomous buses among all buses in the main road approach lane at an intersection. The value range is [0,1]; Proportion of autonomous driving interactive vehicles The value range is [0,1]; determine the adjustment interval. , build The study explored various combinations of autonomous vehicles to cover heterogeneous traffic scenarios with different levels of autonomous vehicle penetration. Under different combinations of autonomous vehicle proportions, traffic flow operation tests were conducted to collect data on average vehicle delays on road segments, intersection capacity, bus lane utilization, and lane space-time resource occupancy rates.

Images