Intelligent network connection vehicle special lane layout method considering dynamic bus priority

Abstract

The invention discloses an intelligent network connection vehicle special lane layout method considering dynamic bus priority. The method comprises a multi-source information acquisition module, a traffic information processing module, a vehicle control module, a traffic flow input module and a lane layout scheme evaluation module. The method comprises the specific steps that the multi-source information collection module collects road condition information, vehicle information and bus operation information; then the traffic information processing module screens and calculates the collected information; the vehicle control module controls the vehicle; the traffic flow input module is used for adjusting the permeability of the intelligent networked vehicles and the total traffic demand, and testing lane layout schemes under various intelligent networked vehicle permeability and different traffic demands; and the lane layout scheme evaluation module establishes evaluation indexes to evaluate test results of different lane layout schemes of the permeability of each intelligent network connection vehicle under different traffic demands, and compares and selects the optimal lane layout scheme under different traffic demands of each intelligent network connection vehicle.

Description

Technical Field

[0001] The present invention relates to a method for laying out a dedicated lane for intelligent networked vehicles taking dynamic public transport priority into consideration, and belongs to the field of intelligent networked traffic management and control. Background Art

[0002] With the development of intelligent connected vehicles and vehicle-road cooperative technology, the proportion of intelligent connected vehicles in traffic flow will gradually increase. For a long time in the future, the mixed traffic of intelligent connected vehicles and non-connected vehicles will become a normal situation. Through intelligent traffic management methods, road resources can be further optimized, road traffic efficiency and safety can be improved, and the advantages of intelligent connected vehicles can be fully utilized.

[0003] Intelligent connected vehicles can use various sensors on the vehicle side and V2X technology to obtain real-time road condition information, and integrate and process the data through the central processor to output the optimal driving decision under the current state. Compared with non-connected vehicles, intelligent connected vehicles have a shorter reaction time, can maintain a smaller headway with the vehicle in front, and have the advantage of centralized control, such as forming an intelligent connected fleet, coordinated control with traffic lights, etc., which can improve road traffic efficiency and safety. However, at a low penetration rate, the mixing of intelligent connected vehicles and non-connected vehicles will not only worsen the driving environment of non-connected vehicle drivers, but also be detrimental to the advantages of intelligent connected vehicles. Therefore, it is necessary to separate heterogeneous traffic flows from the perspective of lane management, give full play to the characteristics of intelligent connected vehicles that can be platooned, simplify the driving environment of non-connected vehicles in heterogeneous traffic flows, and improve traffic efficiency and safety.

[0004] However, when the penetration rate of intelligent connected vehicles is relatively low (less than 0.3), setting up dedicated lanes for them will not only waste road resources, but also compress the driving space of social vehicles, causing greater traffic delays. There are bus lanes on many main roads in urban roads. By utilizing the controllability of intelligent connected vehicles, it is possible to fully utilize the idle bus lane space while ensuring the priority of buses, and meet the needs of intelligent connected vehicles for dedicated lanes. When the penetration rate of intelligent connected vehicles is high, relying solely on borrowing bus lanes cannot meet the needs of intelligent connected vehicles for dedicated lanes. Therefore, in the face of future traffic road scenarios where the penetration rate of intelligent connected vehicles is gradually increasing, a set of highly adaptable intelligent connected dedicated lane layout methods is urgently needed to improve the utilization rate of road resources and give full play to the advantages of intelligent connected vehicles.

[0005] According to the different positions and numbers of the three lanes, namely, the social vehicle lane, the bus lane and the intelligent network lane, there will be a situation where at most one bus lane is set up and it can only be set up on the leftmost or rightmost side of the road. How to determine the optimal lane layout plan for different traffic demands under different intelligent network penetration rates, and how to ensure the priority of buses when intelligent networked vehicles use bus lanes are key issues that need to be studied in order to give full play to the advantages of intelligent networked technology and make full use of road resources.

[0006] In order to solve the above problems, the present invention provides a method for laying out dedicated lanes for intelligent connected vehicles taking into account dynamic bus priority. By continuously adjusting traffic demand and intelligent connected vehicle penetration rate, traffic flow operation test is carried out to obtain the optimal lane layout plan under different intelligent connected vehicle penetration rates and different traffic demands.

[0007] In order to achieve the above object, the present invention adopts the following technical scheme:

[0008] A method for laying out lanes for intelligent networked vehicles considering dynamic bus priority includes a multi-source information collection module, a traffic information processing module, a vehicle control module, a traffic flow input module, and a lane layout scheme evaluation module, wherein: the multi-source information collection module is used to collect road condition information, vehicle information, and bus operation information; the traffic information processing module is used to filter and calculate the collected information; the vehicle control module is used to control the micro-driving behavior of the vehicle following, changing lanes, and using bus lanes; the traffic flow input module is used to perform traffic flow operation tests on lane layout schemes under different traffic demands at various intelligent networked vehicle penetration rates to obtain traffic flow operation parameters. The lane layout scheme evaluation module is used to compare the effects of different lane layouts under the same intelligent networked vehicle penetration rate, and select the optimal lane layout scheme for each intelligent networked vehicle penetration rate under different traffic demands.

[0009] The multi-source information collection module includes an intelligent vehicle terminal, an intelligent road test unit, an intelligent traffic detector and a vehicle-road collaborative data cloud control platform. The road condition information, vehicle information and bus operation information that need to be collected specifically include: the total number of lanes N of the road section L 、N number of social vehicle lanes SL and location, number of bus lanes N BL and location, intelligent network dedicated lane N CL and location, length of the road section L, maximum speed limit value V of the road section max and the minimum speed limit V min , the instantaneous speed V, instantaneous acceleration a and position X of all vehicles on the road section within one second, and the bus departure frequency N per unit time B , the number of passengers per bus n B And the number of people n on each bus C .

[0010] The information that the traffic information processing module needs to filter and calculate is: the average speed of traditional cars Average speed of connected vehicles Average speed of public transport vehicles The headway distance DL between each vehicle and the vehicle in front at each moment, the headway H between each vehicle and the vehicle in front at each moment, and the social vehicle traffic volume C during peak hours S , peak hour bus traffic volume C B , traffic demand Q in each time period t , the free flow speed of traditional cars V Hf , free flow speed of intelligent connected vehicles V Cf and the free flow speed of public transport vehicles V Bf , providing parameters for building road scenes.

[0011] The vehicle control module is used to control the micro-driving behaviors of the vehicle such as following, changing lanes, and using bus lanes, specifically: in any case, the traditional car adopts the IDM model as the following model and the MOBIL model as the lane changing model, and can only operate on the social lane; in any case, the bus adopts the IDM model as the following model and can only operate on the bus lane; when the front vehicle of the intelligent connected vehicle is a non-connected vehicle or there is no vehicle, the vehicle enters the adaptive cruise control (ACC) mode and adopts the ACC model for following; when the front vehicle of the intelligent connected vehicle is also an intelligent connected vehicle, the vehicle enters the collaborative adaptive cruise control (CACC) mode and adopts the CACC model for following; when the target lane for the intelligent connected vehicle to change lanes is the social lane, the MOBIL model is adopted as the lane changing model; when the target lane for the intelligent connected vehicle to change lanes is the bus lane, the game method between the intelligent connected vehicle and the bus is adopted to control the behavior of using the bus lane, so as to realize accurate control of the intelligent connected vehicle and ensure the priority of the bus.

[0012] Furthermore, the traffic flow input module is used to conduct traffic flow operation tests on lane layout schemes with various intelligent connected vehicle penetration rates under different traffic demands to obtain traffic flow operation parameters. The specific steps are as follows:

[0013] S1: Obtaining road condition information without setting a dedicated lane based on the multi-source information collection module and the traffic information processing module;

[0014] S2: Define three types of vehicles: traditional cars, intelligent connected vehicles and public buses. Based on the vehicle information and road condition information obtained by the multi-source information collection module and the traffic information processing module, set the initial speed of the three types of vehicles to the average speed of the target road section, the maximum speed to 70km / h, the minimum speed to 20km / h, and the total traffic volume of the road section to the average traffic volume of the target road section during the off-peak period of the day;

[0015] S3: The vehicle control module is used to set the micro-driving models of following, lane changing and using the bus lane for three types of vehicles;

[0016] S4: According to the number and position of three different types of lanes, the arrangement and combination The lane layout scheme is as follows (assuming that each road section has only one bus lane and that the bus stop can only be set on the left or right side of the road for the convenience of stopping); the penetration rate of intelligent connected vehicles is adjusted in the interval [0,1]. In order to facilitate data recording, it is adjusted at an interval of 0.1. At the same time, the traffic demand under each penetration rate is adjusted from the peak traffic volume of the target road section to the design capacity specified in the "Urban Road Engineering Design Code". In order to facilitate data recording, it is adjusted at 100pcu / h, and different traffic demand times under each penetration rate are calculated separately. The traffic flow operation test is carried out based on various lane layout schemes to obtain the test results.

[0017] Furthermore, the lane layout scheme evaluation module evaluates the test results by establishing evaluation indicators, specifically:

[0018]

[0019] Among them, D represents the delay of vehicles on the entire road section, ρ represents the penetration rate of intelligent connected vehicles, and θ is a weight parameter to ensure the priority of public transportation, which can be taken between [0.5, 2]. The larger its value is, the higher the priority of public transportation is. By comparing the test index values ​​of different schemes with different traffic demands under the same intelligent connected vehicle penetration rate, the smaller the delay D value is, the better the traffic flow operation effect is, and the optimal lane layout plan for each traffic demand size under the penetration rate can be obtained.

[0020] Compared with the existing technology in this field, the beneficial effect brought about by the technical solution of the present invention is: the present invention proposes a method for laying out dedicated lanes for intelligent connected vehicles taking into account dynamic bus priority, while taking into account the needs of intelligent connected vehicles and buses for dedicated lanes, and starting from the angles of lane type, number of lanes, lane position and priority of buses, the optimal lane layout plan under different penetration rates of intelligent connected vehicles is obtained, which can ensure the priority of buses while improving road traffic efficiency and improve the utilization rate of road resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Flowchart for lane layout plan

[0022] Figure 2 Schematic diagram of a smart connected vehicle changing lanes when the vehicle behind it in the target lane is a traditional car

[0023] Figure 3 Schematic diagram of an intelligent connected vehicle following a traditional car

[0024] Figure 4 Schematic diagram of an intelligent connected vehicle following a public bus

[0025] Figure 5 Schematic diagram of an intelligent connected vehicle following an intelligent connected vehicle

[0026] Figure 6 Schematic diagram of a smart connected vehicle changing lanes when the vehicle behind it in the target lane is a bus DETAILED DESCRIPTION

[0027] The present invention is further described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0028] A method for laying out dedicated lanes for intelligent connected vehicles considering dynamic bus priority includes a multi-source information collection module, a traffic information processing module, a vehicle control module, a traffic flow input module, and a lane layout scheme evaluation module. Figure 1 As shown in the figure: the multi-source information acquisition module is used to collect road condition information, vehicle information and bus operation information; the traffic information processing module is used to filter and calculate the collected information; the vehicle control module is used to control the vehicle's micro-driving behavior of following, changing lanes and using bus lanes; the traffic flow input module is used to conduct traffic flow operation tests on lane layout plans under different traffic demands at various intelligent connected vehicle penetration rates and obtain test parameters. The lane layout plan evaluation module is used to compare the effects of different lane layouts under the same intelligent connected vehicle penetration rate and select the optimal lane layout plan under each intelligent connected vehicle penetration rate.

[0029] The multi-source information collection module includes an intelligent vehicle terminal, an intelligent road test unit, an intelligent traffic detector and a vehicle-road collaborative data cloud control platform. The road condition information, vehicle information and bus operation information that need to be collected specifically include: the total number of lanes N of the road section L 、N number of social vehicle lanes SL and location, number of bus lanes N BL and location, intelligent network dedicated lane N CL and location, length of the road section L, maximum speed limit value V of the road section max and the minimum speed limit V min (determined by the grade and type of the test road), the instantaneous speed V, instantaneous acceleration a and position X of all vehicles on the road section within one second (measured by the roadside unit in the multi-source information acquisition module), and the bus departure frequency N per unit time B , the number of passengers per bus n B And the number of people n on each bus C.

[0030] The information that the traffic information processing module needs to filter and calculate is: the average speed of traditional cars Average speed of connected vehicles Average speed of public transport vehicles The headway distance DL between each vehicle and the vehicle in front at each moment, the headway H between each vehicle and the vehicle in front at each moment, and the social vehicle traffic volume C during peak hours S , peak hour bus traffic volume C B , traffic demand Q in each time period t , the free flow speed of traditional cars V Hf , free flow speed of intelligent connected vehicles V Cf and the free flow speed of public transport vehicles V Bf , providing parameters for building road scenes.

[0031] Furthermore, the vehicle control module is used to control the micro-driving behaviors of the vehicle such as following, changing lanes, and using the bus lane, specifically:

[0032] In any case, traditional cars and buses use the IDM model as the following model, and the model calculation method is:

[0033]

[0034]

[0035] Δv i (t) = [v i (t)-v i-1 (t)]

[0036] Among them, a i (t) is the acceleration of the manually driven vehicle n at time t; a max It is the maximum acceleration of a manually driven vehicle. It can take different values ​​according to different models. For family cars, it is generally 5m / s 2 ;v i (t) is the speed of the manually driven vehicle i at time t; Δv i (t) is the speed difference between vehicle i-1 and the manually driven vehicle i at time t; s * i (t) is the expected following distance; s i (t) is the following distance between vehicle i-1 and the manually driven vehicle i at time t; T i The safe headway time for manually driven vehicles can be set within the range of 2.5 to 3.3 seconds depending on the aggressiveness of vehicle control; v fis the free-flow speed of the driver, with a value of 0.9 times the maximum speed limit of the road; s0 is the minimum safe stopping distance, with a value of 2m: b is the comfortable deceleration of the manually driven vehicle, which can take values in the range of 1.2 - 2.5m / s 2 depending on the aggressiveness of vehicle control; s1 is a coefficient with a value range of [0, 1]; δ is a constant coefficient, which is taken as 4 here.

[0037] The MOBIL model is used as the lane-changing model when the target lane for traditional cars and connected and automated vehicles to change lanes is the general-purpose lane. It is stipulated that traditional cars can only run on the general-purpose lane, and buses can only run on the bus lane. The MOBIL model considers lane-changing safety and lane-changing benefits. The safety criterion is specifically as follows:

[0038]

[0039] In the formula: is the acceleration of the target vehicle after lane-changing, calculated according to the vehicle following model; is the acceleration of the vehicle following the target vehicle on the target lane after the target vehicle changes lanes, which can be calculated by the vehicle following model according to the relative position and speed relationship between the two vehicles; b safe is the given maximum safe deceleration, with a value of -3m / s 2 .

[0040] The incentive criterion is specifically as follows:

[0041]

[0042] In the formula:: is the acceleration of the target vehicle after lane-changing, calculated according to the vehicle following model; is the acceleration of the vehicle following the target vehicle on the target lane after the target vehicle changes lanes, which can be calculated by the vehicle following model according to the relative position and speed relationship between the two vehicles; u SV is the total lane-changing benefit of the target vehicle; a SV is the current acceleration of the target vehicle; a PFV is the current acceleration of the vehicle following on the target lane; is the acceleration of the vehicle following the target vehicle on the original lane after the target vehicle changes lanes, which can be obtained through the vehicle following model according to the position and speed relationship between the vehicle in front and the vehicle behind the target vehicle on the original lane; a FV is the current acceleration of the vehicle following on the original lane; p is the courtesy coefficient, 0 < p < 1, which is taken as 0.2 here; Δa th is the lane-changing benefit threshold, used to control the aggressiveness of vehicle lane-changing, which is taken as 0.5 here.

[0043] When the vehicle in front of the intelligent connected vehicle is a non-connected vehicle or there is no vehicle, the vehicle enters the adaptive cruise control (ACC) mode and uses the ACC model for following. The calculation method is as follows:

[0044] a i (t) = k c [h i (t)-Tv i (t)-l-s0]+k v [v i-1 (t)-v i (t)]

[0045] Where: h i (t) is the distance between vehicle i and the front vehicle at time t; T is the expected headway between vehicles of the intelligent connected vehicle, which can be taken in the range of 1.3 to 2.5 seconds according to the aggressiveness of vehicle control; l is the length of the vehicle, which is generally 4 to 5.5 meters long for a family car, and is taken as 5 meters here: k c and k v is the control coefficient, which can take values ​​in the interval (0, 1), and here they are taken as 0.1 and 0.05 respectively.

[0046] When the vehicle ahead of the intelligent connected vehicle is also an intelligent connected vehicle, the vehicle enters the Cooperative Adaptive Cruise Control (CACC) mode and uses the CACC model for following. The calculation method is as follows:

[0047]

[0048] Where: h i (t) is the distance between vehicle i and the front vehicle at time t; T is the expected headway between vehicles of the intelligent networked vehicle, which can be taken in the range of 1.3 to 2.5 seconds according to the aggressiveness of vehicle control; l is the length of the vehicle, which is generally 4 to 5.5 meters long for a family car; Δt is the control step length of the intelligent networked vehicle system, which is 0.1 seconds; v i (t+Δt) is the speed of the intelligent connected vehicle at time t+Δt; e i (t) is the error between the actual headway distance between the intelligent connected vehicle and the preceding vehicle at time t and the expected headway distance; for e i (t) is the differential term with respect to time t; k p is the inter-vehicle distance error control coefficient, which is 0.25, k d is the control coefficient of the differential term of the inter-vehicle distance error, which is taken as 0.4.

[0049] When the target lane for the intelligent connected vehicle to change lanes is the bus lane, the game method between the intelligent connected vehicle and the bus is used to control the behavior of borrowing the bus lane, so as to achieve accurate control of the intelligent connected vehicle and ensure the priority of the bus. The specific control process is as follows:

[0050] In the game system, the strategy set of the target intelligent connected vehicle is set to C = {1: Y, 2: N}, C1 = Y means to execute the lane change operation, and C2 = N means not to execute the lane change operation. The strategy set of the bus is B = {1: A, 2: D, 3: K}, B1 = A means that the bus accelerates, S2 means that the bus takes deceleration and avoidance measures, and B3 = K means that the bus maintains the original state. Set P and Q as the profit matrices of the intelligent connected vehicle and the bus respectively. The profit matrix is ​​as follows:

[0051]

[0052] Among them, P 11 and Q 11 P is the benefit of the intelligent connected vehicle and the bus when the intelligent connected vehicle takes lane changing measures and the bus takes acceleration measures respectively; 12 and Q 12 P is the benefit of the intelligent connected vehicle and the bus when the intelligent connected vehicle takes lane changing measures and the bus takes deceleration measures respectively; 13 and Q 13 are the benefits of the intelligent connected vehicle and the bus when the lane-changing measures are taken for the intelligent connected vehicle and the bus status remains unchanged; P 21 and Q 21 are the benefits of the intelligent connected vehicle and the bus when the intelligent connected vehicle does not change lanes and the bus takes acceleration measures; P 22 and Q 22 are the benefits of the intelligent connected vehicle and the bus when the intelligent connected vehicle does not change lanes and the bus takes the deceleration measure; P 23 and Q 23 The benefits of the intelligent connected vehicle and the bus when the intelligent connected vehicle does not change lanes and the bus status remains unchanged.

[0053] Then, we find the mixed strategy Nash equilibrium. Assuming that the probability of the intelligent connected vehicle changing lanes is p1, the probability of not changing lanes is p2 = 1-p1. The probability of the bus accelerating and decelerating is q1 and q2 respectively, and the probability of maintaining the original state is q3 = 1-q1-q2.

[0054] The profit function is constructed as follows:

[0055] Speed ​​Gains:

[0056]

[0057] Where: p v is the speed gain, v′ is the speed of the target vehicle at the completion of lane change, v0 is the speed of the target vehicle at the initial lane change, and v sl is the speed of the vehicle in front of the target lane, a0 is the acceleration of the target vehicle at the beginning of lane change, and t′ is the time required for the lane change process, which is within 3 to 10 seconds.

[0058] Safety benefits:

[0059]

[0060] Where: p s For safety benefit, d l is the current distance between the vehicle and the preceding vehicle in the same lane, d C-B is the current longitudinal distance between the target intelligent connected vehicle and the bus on the bus lane in the game system, v CL 、a CL and v BL 、a BL They represent the current speed and acceleration of the front vehicle in the original lane of the intelligent connected vehicle and the front vehicle in the bus lane, respectively. C 、a C and v B 、a B They are the current speed and acceleration of the intelligent connected vehicle and bus in the game system, which can be obtained according to the multi-source information acquisition module and the traffic information processing module.

[0061] Lane change benefits:

[0062]

[0063] Where: p lc is the lane change benefit, V max is the maximum speed limit of the road section, v0 is the speed of the target vehicle at the initial moment of lane change, and a0 is the acceleration of the target vehicle at the initial moment of lane change.

[0064] Public transport priority benefits:

[0063]

[0064] Where: p BA is the public transport priority benefit, n C is the bus's full load, n B is the number of passengers carried by the bus.

[0065] Therefore, in the entire game system, the profit matrices P and Q of the intelligent connected vehicles and buses are as follows:

[0066] in

[0067] Among them, p v is the speed gain of the vehicle; p s For the safety benefit of the vehicle; lc is the lane change benefit; P BA For public transport priority benefits.

[0068] According to the probability of each vehicle choosing each behavior, we can know the expected benefits E of the intelligent connected vehicle and the bus: C (p1, q1, q2), E B (p1, q1, q2) are:

[0069]

[0070] Among them, by maximizing E C and E B To obtain the optimal solution, that is, the Nash equilibrium of the mixed strategy. Assume that when the game system obtains the optimal strategy combination, Then the optimal mixed probability combination is At this point, the benefits of smart connected vehicles and public transport revenue All reach the optimum, namely:

[0071]

[0072] Seeking E C Partial derivative with respect to p1 E B The partial derivative with respect to q1 E B Partial derivative with respect to q2 make All are zero, find the balance point, the specific steps are as follows:

[0073] Expected benefit E of intelligent connected vehicles C Derivative of your own strategy p1:

[0074]

[0075] Setting the partial derivative to 0, we get:

[0076] q1(P 11 -P 21 )+q2(P 12 -P 22 )+q3(P 13 -P 23 )=0

[0077] The expected revenue of the bus is E B Derivative of your own strategies q1 and q2:

[0078]

[0079] Setting the partial derivative to 0, we get:

[0080]

[0081] Combining the above equations with partial derivatives set to 0, we get a system of equations:

[0082]

[0083] Substitute the value of the actual profit matrix into the above equation and solve p1, q1, and q2.

[0084] Check whether the solved strategy combination meets the conditions of mixed strategy Nash equilibrium, that is, under this strategy combination, the expected benefit of each player is maximized, and it is impossible to obtain higher benefits by unilaterally changing the strategy, that is, to obtain the optimal strategy combination.

[0085] Furthermore, the traffic flow input module is used to conduct traffic flow operation tests on lane layout schemes under various intelligent connected vehicle penetration rates to obtain traffic flow operation parameters. The specific steps are as follows:

[0086] S1: Obtaining road condition information without setting a dedicated lane based on the multi-source information collection module and the traffic information processing module;

[0087] S2: Define three types of vehicles: traditional cars, intelligent connected vehicles and public buses. Based on the vehicle information and road condition information obtained by the multi-source information collection module and the traffic information processing module, set the initial speed of the three types of vehicles to the average speed of the target section traffic, the maximum speed to 70km / h, the minimum speed to 20km / h, and the total traffic volume of the section to the average traffic volume of the target section during the off-peak period of the day.

[0088] S3: The vehicle control module is used to set the micro-driving models of following, lane changing and using the bus lane for three types of vehicles;

[0089] S4: According to the number and position of three different types of lanes, the arrangement and combination The lane layout scheme is as follows (assuming that there is only one bus lane on each road section and the bus stop is intelligently set on the left or right side of the road for the convenience of stopping the bus); the penetration rate of intelligent connected vehicles is adjusted in the interval [0,1]. In order to facilitate data recording, it is adjusted at an interval of 0.1. At the same time, the traffic demand under each penetration rate is adjusted from the peak traffic volume of the target road section to the design capacity specified in the "Urban Road Engineering Design Code". In order to facilitate data recording, it is adjusted at 100pcu / h, and different traffic demand times under each penetration rate are calculated separately. The traffic flow operation test is carried out based on various lane layout schemes to obtain the test results.

[0090] Furthermore, the lane layout scheme evaluation module is used to establish evaluation indicators to evaluate the test results, specifically:

[0091]

[0092] Among them, D represents the delay of vehicles on the entire road section, ρ represents the penetration rate of intelligent connected vehicles, and θ is a weight parameter to ensure the priority of public transportation, which can be taken between [0.5, 2]. The larger its value is, the higher the priority of public transportation is. By comparing the test index values ​​of different schemes with different traffic demands under the same intelligent connected vehicle penetration rate, the smaller the delay D value is, the better the traffic flow operation effect is, and the optimal lane layout plan for each traffic demand size under the penetration rate can be obtained.

[0093] The above description of the specific implementation methods is intended to describe and illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical ideas proposed in the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for laying out dedicated lanes for intelligent networked vehicles considering dynamic public transport priority, characterized in that: It includes multi-source information collection module, traffic information processing module, vehicle control module, traffic flow input module and lane layout scheme evaluation module, among which: The multi-source information acquisition module is used to collect road condition information, vehicle information and public transportation operation information; The traffic information processing module is used to screen and calculate the collected information; The vehicle control module is used to control the micro-driving behaviors of the vehicle such as following, changing lanes, and using bus lanes; The traffic flow input module is used to test lane layout schemes for various intelligent connected vehicle penetration rates under different traffic demands and obtain traffic flow operation parameters; The lane layout scheme evaluation module is used to compare the implementation effects of different lane layout methods under different intelligent connected vehicle penetration rates and different traffic demands, and to select the optimal lane layout scheme under each intelligent connected vehicle penetration rate.

2. According to claim 1, a method for laying out dedicated lanes for intelligent networked vehicles taking into account dynamic public transport priority is characterized in that: The multi-source information acquisition module includes an intelligent vehicle-mounted terminal, an intelligent road test unit, an intelligent traffic detector and a vehicle-road collaborative data cloud control platform.

3. According to claim 1, a method for laying out dedicated lanes for intelligent networked vehicles taking into account dynamic public transport priority is characterized in that: The road condition information that needs to be collected by the multi-source information collection module is specifically: the total number of lanes N of the road section L 、N number of social vehicle lanes SL and location, number of bus lanes N BL and location, intelligent network dedicated lane N CL and location, length of the road section L, maximum speed limit value V of the road section max and the minimum speed limit V min .

4. According to claim 1, a method for laying out dedicated lanes for intelligent networked vehicles taking into account dynamic public transport priority is characterized in that: The vehicle information that needs to be collected by the multi-source information collection module is specifically: the instantaneous speed V, instantaneous acceleration a and position X of all vehicles on the road section within one second.

5. According to claim 1, a method for laying out dedicated lanes for intelligent networked vehicles taking into account dynamic public transport priority is characterized in that: The bus operation information that the multi-source information collection module needs to collect is specifically: the bus departure frequency N per unit time B , the number of passengers per bus n B and the number of people n that each bus is fully loaded with C .

6. According to claim 1, a method for laying out dedicated lanes for intelligent networked vehicles taking into account dynamic public transport priority is characterized in that: The information that the traffic information processing module needs to filter and calculate is: the average speed of traditional cars Average speed of connected vehicles Average speed of public transport vehicles The headway distance DL between each vehicle and the vehicle in front at each moment, the headway H between each vehicle and the vehicle in front at each moment, and the social vehicle traffic volume C during peak hours S , peak hour bus traffic volume C B , traffic demand Q in each time period t , the free flow speed of traditional cars V Hf , free flow speed of intelligent connected vehicles V Cf and the free flow speed of public transport vehicles V Bf .

7. According to claim 1, a method for laying out dedicated lanes for intelligent networked vehicles taking into account dynamic public transport priority is characterized in that: The vehicle control module is used to control the vehicle's micro-driving behaviors of following, changing lanes and using bus lanes, specifically: in any case, the car adopts the intelligent driver model (IDM) model as the following model and the MOBIL model as the lane changing model, and can only operate on the social lane; in any case, the bus adopts the IDM model as the following model and can only operate on the bus lane; when the vehicle in front of the intelligent connected vehicle is a non-connected vehicle or there is no vehicle, the vehicle enters the adaptive cruise control (ACC) mode and uses the ACC model for following; when the vehicle in front of the intelligent connected vehicle is also an intelligent connected vehicle, the vehicle enters the collaborative adaptive cruise control (CACC) mode and uses the CACC model for following; when the target lane for the intelligent connected vehicle to change lanes is a social lane, the MOBIL model is used as the lane changing model; when the target lane for the intelligent connected vehicle to change lanes is a bus lane, the game method between the intelligent connected vehicle and the bus is used to control the behavior of using the bus lane.

8. According to claim 1, a method for laying out dedicated lanes for intelligent networked vehicles taking into account dynamic public transport priority is characterized in that: The traffic flow input module is used to test the lane layout scheme under different intelligent connected vehicle penetration rates and different traffic demands to obtain traffic flow operation parameters. The specific steps are as follows: S1: Obtaining road condition information without setting a dedicated lane based on the multi-source information collection module and the traffic information processing module; S2: Define three types of vehicles: traditional cars, intelligent connected vehicles and public buses. Based on the vehicle information and road condition information obtained by the multi-source information collection module and the traffic information processing module, set the initial speed of the three types of vehicles to the average speed of the target section traffic flow, the maximum speed to 70km / h, the minimum speed to 20km / h, and the initial traffic demand of the section to the average traffic volume of the target section during the off-peak period of the day; S3: The vehicle control module is used to set the micro-driving models of following, lane changing and using the bus lane for three types of vehicles; S4: According to the number and position of three different types of lanes, the arrangement and combination A lane layout scheme is proposed. It is assumed that each road section has only one bus lane and for convenience, the bus stop can only be set up on the leftmost or rightmost side of the road. The penetration rate of intelligent connected vehicles is adjusted in the interval of [0,1]. In order to facilitate data recording, it is adjusted at an interval of 0.

1. At the same time, the traffic demand under each penetration rate is adjusted from the peak traffic volume of the target road section to the design capacity specified in the "Urban Road Engineering Design Code", and different traffic demand times under each penetration rate are calculated separately. The traffic flow operation test is carried out based on various lane layout schemes to obtain the test results.

9. The method for laying out dedicated lanes for intelligent networked vehicles considering dynamic public transport priority according to claim 1, characterized in that: The lane layout scheme evaluation module evaluates the test results by establishing evaluation indicators, specifically: Among them, D represents the delay of vehicles on the entire road section, ρ represents the penetration rate of intelligent connected vehicles, and θ is a weight parameter to ensure the priority of public transportation, which takes values ​​between [0.5, 2]. The larger its value is, the higher the priority of public transportation is. By comparing the test index values ​​of different schemes with different traffic demands under the same intelligent connected vehicle penetration rate, the smaller the delay D value is, the better the traffic flow operation effect is, and the optimal lane layout plan for each traffic demand size under the penetration rate can be obtained.

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