Maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles

Abstract

The present invention relates to the technical field of traffic management and control, and particularly relates to a maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles. The steps are as follows: S1: Analyze the traffic flow volume and direction of the upstream intersection, and establish a traffic flow arrival rate model for the exit lanes of the upstream intersection; S2: Based on the geometric distribution model proposed by Robertson, derive the probability distribution function of vehicle travel time; S3: According to the vehicle arrival rate of the upstream intersection and the Robertson platoon dispersion model, establish the arrival rate models of motor vehicles and non-motor vehicles at the downstream intersection respectively. The maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention establishes the vehicle arrival rate model of the downstream intersection according to the vehicle arrival rate of the upstream intersection and the Robertson platoon dispersion model; comprehensively analyzes the different arrival situations of motor vehicles and non-motor vehicles under different saturations, and establishes an average vehicle delay model that comprehensively considers motor vehicles and non-motor vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management and control, and particularly to a maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles. Background Art

[0002] In recent years, with the strong advocacy of green travel, the number of non-motor vehicles has been increasing continuously and has become an important part of urban traffic travel. Non-motor vehicles have had a significant impact on the road traffic situation and have become a remarkable feature of the operation of urban roads in contemporary China. To improve the traffic efficiency of arterial roads, coordinated signal control at intersections is considered an effective measure, which can significantly improve the efficiency and safety of urban traffic operation. However, current traffic control research mainly focuses on motor vehicles, and the impact of non-motor vehicles on travel efficiency has been somewhat ignored to a certain extent. The existing maximum pressure traffic signal control method only considers motor vehicles and lacks consideration of non-motor vehicles, unable to maximize traffic services and also unable to meet the actual situation of the increasing number of non-motor vehicles. Therefore, the present invention proposes a maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles. This method relies on the maximum pressure distributed signal control algorithm and combines the queue length to feedback-optimize the green time of each phase, which can not only effectively prevent queue overflow but also maximize traffic travel services;

[0003] For this reason, we design a maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles to provide another technical solution to the above technical problems. Summary of the Invention

[0004] Based on this, it is necessary to provide a maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles to solve the technical problems raised in the above background art.

[0005] To solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles, the steps are as follows:

[0007] S1: Analyze the traffic flow rate and direction of the upstream intersection, and establish a traffic flow arrival rate model for the exit lanes of the upstream intersection;

[0008] S2: Based on the geometric distribution model proposed by Robertson, derive the probability distribution function of vehicle travel time;

[0009] S3: According to the vehicle arrival rate of the upstream intersection and the Robertson platoon dispersion model, establish the arrival rate models of motor vehicles and non-motor vehicles at the downstream intersection respectively;

[0010] S4: Establish a comprehensive delay model based on the comprehensive delays of motor vehicles and non-motor vehicles at downstream intersections under different arrival conditions;

[0011] S5: Based on the Robertson discrete model to predict the queue length generated downstream, establish a vehicle queue length model under different degrees of saturation;

[0012] S6: To prevent vehicle queue overflow, based on the vehicle queue length obtained in S5, establish a maximum traffic pressure control model for traffic signals that takes into account motor vehicles based on the queue ratio of sections;

[0013] S7: Based on the predicted queue length model of non-motor vehicles, establish a maximum traffic pressure control model for traffic signals that takes into account non-motor vehicles;

[0014] S8: According to the maximum traffic pressure control models established in S6 and S7, calculate the sum of the road pressures of the motor vehicle lane and the non-motor vehicle lane, and considering the conversion coefficient between non-motor vehicles and motor vehicles, obtain the comprehensive pressure of each phase of the road;

[0015] S9: Calculate the green light time of each phase by the ratio of the comprehensive pressure to the total pressure and the ratio to the cycle, so as to achieve the goal of comprehensively considering motor vehicles and non-motor vehicles in signal control;

[0016] S10: Use SUMO to build a simulation environment and conduct a comparative analysis with classical signal control methods.

[0017] As a preferred implementation manner of the maximum traffic pressure control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S1, the traffic flow arrival rate model of the exit lane of the upstream intersection is established, and the formula is as follows:

[0018]

[0019] In the formula, N i is the number of vehicles in the traffic flow direction of the upstream intersection i, pcu / h; η i is the turning ratio of the traffic flow direction of the upstream intersection i, η L +η S +η R = 1; s is the vehicle saturation flow rate, pcu / s; t g refers to the time when the traffic flow is released at the saturation flow rate during the green light period, t G is the green light release time;

[0020] The calculation formula for the motor vehicle saturation flow rate is:

[0021] The calculation formula for the non-motor vehicle saturation flow rate is:

[0022] Among them, N机 The number of motor vehicles passing through at time t, N 非 The number of non-motor vehicles passing through at time t, and w is the width of the non-motor vehicle lane;

[0023]

[0024] The exit lane of the upstream intersection is formed by the convergence of vehicles in three directions: right turn, straight, and left turn, G L Belongs to the set of green light times of the left turn phase, G S Belongs to the set of green light times of the straight phase.

[0025] As a preferred embodiment of the maximum traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S2, based on the geometric distribution model proposed by Robertson, the probability distribution function of vehicle travel time is derived, and the steps are as follows:

[0026] The relationship between the vehicle arrival rate at a certain section and the vehicle departure rate at the upstream section is as follows

[0027]

[0028] In the formula: τ is 0.8 times the average travel time of vehicles between the upstream and downstream sections, that is a is the fleet dispersion coefficient; q 2 (j) is the vehicle arrival rate at the downstream section within the jth time period; q 1 (j) is the vehicle departure rate at the upstream section within the jth time period;

[0029] Through derivation, the probability distribution function of vehicle travel time can be obtained; the expression is as follows:

[0030]

[0031] In the formula: g(T) is the probability distribution of vehicle travel time between the upstream and downstream sections; T is the vehicle travel time; d is the distance between the upstream and downstream sections; v is the average vehicle speed between the upstream and downstream sections; a is a parameter corrected according to the observed value, usually taking a = 0.35.

[0032] As a preferred embodiment of the maximum traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S3, according to the vehicle arrival rate at the upstream intersection and the Robertson fleet dispersion model, the arrival rate models of motor vehicles and non-motor vehicles at the downstream intersection are established respectively, and the steps are as follows:

[0033] The vehicle arrival rate at the entrance lane of the downstream intersection can be calculated from the passing rate at the upstream intersection, and the formula is as follows:

[0034] V d(t + Δt) = AV 0 (t) + (1 - A)V d (t + Δt - 1)

[0035] Coefficient of dispersion:

[0036] t = 1, 2, 3...n

[0037] V d (t + Δt) is the vehicle arrival rate at the downstream intersection of (t + Δt); V 0 (t) is the vehicle passing rate at the upstream intersection; V d (t + Δt - 1) is the vehicle arrival rate at the downstream intersection at the moment of (t + Δt - 1); A is the coefficient of vehicle speed dispersion.

[0038] As a preferred implementation of the maximum pressure traffic signal control method for the coordination and optimization of motor vehicles and non-motor vehicles provided by the present invention, in S4, according to the comprehensive delay conditions of motor vehicles and non-motor vehicles at the downstream intersection under different arrival conditions, a comprehensive delay model is established, and the steps are as follows:

[0039] A. When the motor vehicle is in the under-saturated state and the non-motor vehicle is in the under-saturated state:

[0040] Motor vehicle delay (G 2 > G 1 + T), the formula is as follows:

[0041]

[0042] Non-motor vehicle delay (G 2 > G 1 + T):

[0043]

[0044] Comprehensive weighted delay:

[0045] D 1 = α 1 d 机 + β 1 d 非

[0046] In the formula, α 1 , β 1 are the weight coefficients of motor vehicle delay and non-motor vehicle delay, and the weight coefficients of motor vehicle delay and non-motor vehicle delay are obtained according to the motor vehicle flow and non-motor vehicle flow in each phase. The specific calculation formula is:

[0047]

[0048] In the formula, y 机ijis the motor vehicle flow ratio in the i-th phase and the j-th cycle, y 非ij is the non-motor vehicle flow ratio in the i-th phase and the j-th cycle, and hereinafter α n , β n are the weight coefficients of motor vehicles and non-motor vehicles under different arrival conditions respectively;

[0049] B. When the motor vehicles are under-saturated and the non-motor vehicles are near-saturated, the formula is as follows:

[0050] Motor vehicle delay (G 2 > G 1 + T):

[0051]

[0052] Non-motor vehicle delay (T < G 2 < G 1 + T):

[0053]

[0054] Comprehensive weighted delay:

[0055] D 2 = α 2 d 机 + β 2 d 非

[0056] C. When the motor vehicles are under-saturated and the non-motor vehicles are over-saturated, the formula is as follows:

[0057] Motor vehicle delay (G 2 > G 1 + T) 2 :

[0058]

[0059] Non-motor vehicle delay (G 2 < T):

[0060]

[0061] Comprehensive weighted delay:

[0062] D 3 = α 3 d 机 + β 3 d 非

[0063] D. When the motor vehicles are near-saturated and the non-motor vehicles are under-saturated, the formula is as follows:

[0064] Motor vehicle delay (T < G 2<G 1 +T):

[0065]

[0066] Non - motor vehicle delay (G 2 >G 1 +T):

[0067]

[0068] Comprehensive weighted delay:

[0069] D 4 =α 4 d 机 +β 4 d 非

[0070] E. When the motor vehicle is near saturation and the non - motor vehicle is near saturation, the formula is as follows: Motor vehicle delay (T < G 2 <G 1 +T):

[0071]

[0072] Non - motor vehicle delay (T < G 2 <G 1 +T):

[0073]

[0074] Comprehensive weighted delay:

[0075] D 5 =α 5 d 机 +β 5 d 非

[0076] F. When the motor vehicle is near saturation and the non - motor vehicle is oversaturated, the formula is as follows: Motor vehicle delay (T < G 2 <G 1 +T):

[0077]

[0078] Non - motor vehicle delay (G 2 <T):

[0079]

[0080] Comprehensive weighted delay:

[0081] D 6 =α 6 d 机 +β 6d 非

[0082] G. When the motor vehicles are oversaturated and the non-motor vehicles are undersaturated, the formula is as follows: Motor vehicle delay (G 2 <T):

[0083]

[0084] Non-motor vehicle delay (G 2 >G 1 +T):

[0085]

[0086] Composite weighted delay:

[0087] D 7 =α 7 d 机 +β 7 d 非

[0088] H. When the motor vehicles are oversaturated and the non-motor vehicles are nearly saturated, the formula is as follows: Motor vehicle delay (G 2 <T):

[0089]

[0090] Non-motor vehicle delay (T<G 2 <G 1 +T):

[0091]

[0092] Composite weighted delay:

[0093] D 8 =α 8 d 机 +β 8 d 非

[0094] J. When the motor vehicles are oversaturated and the non-motor vehicles are oversaturated, the formula is as follows: Motor vehicle delay (G 2 <T):

[0095]

[0096] Non-motor vehicle delay (G 2 <T):

[0097]

[0098] Composite weighted delay:

[0099] D 9 =α9 d 机 +β 9 d 非 。

[0100] As a preferred embodiment of the maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S5, based on the Robertson discrete model to predict the queue length generated downstream, a vehicle queue length model under different degrees of saturation is established, and the steps are as follows:

[0101] (1) The road section is unsaturated, H 0 is approximately equal to 0:

[0102]

[0103] (2) In the case that the road section is nearly saturated and there is no initial queue downstream: the traffic volume arriving downstream is greater than the downstream passing capacity. After the driving phase ends, then in the next cycle, the remaining equivalent traffic volume in the downstream queue is the total equivalent traffic volume reached within the downstream signal cycle minus the total equivalent traffic volume released during the downstream green light period difference:

[0104]

[0105] Then during the red light period of the next cycle of the road section, the average queue length of the lane is:

[0106]

[0107] (3) In the case that the road section is saturated and there is an initial queue downstream;

[0108] The traffic volume arriving downstream is greater than the downstream passing capacity. After the end of the kth period, then in the next cycle, the initial queue equivalent traffic volume is the remaining equivalent traffic volume in the downstream queue is the total equivalent traffic volume reached within the downstream signal cycle minus the total equivalent traffic volume released during the downstream green light period difference:

[0109]

[0110] As a preferred embodiment of the maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S6, to prevent vehicle queue overflow, based on the vehicle queue length obtained in S5, a maximum pressure traffic signal control model considering motor vehicles based on the road section queue ratio is established, and the steps are as follows:

[0111] The weight calculation based on the queue length for upstream and downstream intersections is as follows:

[0112]

[0113] Take the relevant weight of each turning traffic flow from section i to section m as the difference between the upstream queue and the average queue of the downstream intersection. The expression is as follows:

[0114]

[0115] Where H e,e+1 (t) is the queue length between the upstream intersection and the downstream intersection at time t; L e,e+1 is the length of section l; p e+1,e+2 (t) is the traffic flow ratio from intersection e + 1 to intersection e + 2 at time t; O m is the set of all sections at the starting point, i.e., the end point of section m.

[0116] As a preferred embodiment of the maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S7, according to the predicted queue length model of non-motor vehicles, a maximum pressure traffic signal control model considering non-motor vehicles is established. The steps are as follows:

[0117] The predicted non-motor vehicle queue length is used to calculate the weight of non-motor vehicles in the maximum pressure control and release the non-motor vehicle lane. After the predicted non-motor vehicle waiting time, the predicted non-motor vehicle queue is calculated. The calculation formula is as follows:

[0118]

[0119] As a preferred embodiment of the maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S8, according to the maximum pressure traffic signal control model established in S6 and S7, calculate the sum of the road pressures of the motor vehicle lane and the non-motor vehicle lane, and consider the conversion coefficient between non-motor vehicles and motor vehicles to obtain the comprehensive pressure of each phase road. The steps are as follows;

[0120] The sum of the road pressures is calculated as follows:

[0121]

[0122] The comprehensive pressure is calculated as follows:

[0123]

[0124] δ m is the proportion of motor vehicles on the road, δ nis the proportion of non-motor vehicles; if during time period k, the current active phase σ of the green light time * the allocated green light time has reached or exceeded its maximum allowable green light time G max , then G e = G max ; if during time period k, the current active phase σ of the green light time * the allocated green light time is less than the minimum green light time G min , then G e = G min .

[0125] As a preferred embodiment of the maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention, in S9, by calculating the ratio of the comprehensive pressure to the total pressure and the ratio to the cycle, the green light time of each phase is calculated to achieve the goal of comprehensively considering motor vehicles and non-motor vehicles in signal control. The calculation formula is as follows:

[0126]

[0127] For each intersection e, decisions are made at the end of each phase in each time period, and all these moments are called control decision times k n , whenever the time reaches k n , the corresponding phase is σ * (k n ), and the pressure of each phase is calculated using the result of queue length estimation

[0128] It can be undoubtedly seen that through the above technical solutions of the present application, the technical problems to be solved by the present application can surely be solved.

[0129] Meanwhile, through the above technical solutions, the present invention has at least the following beneficial effects:

[0130] The maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles provided by the present invention establishes a vehicle arrival rate model for the downstream intersection according to the vehicle arrival rate of the upstream intersection and the Robertson vehicle queue dispersion model; comprehensively analyzes the different arrival situations of motor vehicles and non-motor vehicles under different saturations, and establishes a vehicle average delay model that comprehensively considers motor vehicles and non-motor vehicles; at the same time, discusses and analyzes the queue length of the downstream intersection based on the Robertson vehicle queue dispersion model;

[0131] Based on the vehicle queue length and the section queue ratio, the maximum pressure distributed signal control algorithm is used to feedback and optimize the green light time of each phase, which can effectively prevent queue overflow while maximizing traffic travel services. Considering the traffic volumes of both motor vehicles and non-motor vehicles, and taking the comprehensive service population as the evaluation index, a multi-objective optimization model of motor vehicle, non-motor vehicle queue length, delay, traffic travel service volume, and service population is established, taking into account the traffic volumes of both motor vehicles and non-motor vehicles. Description of the Drawings

[0132] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0133] Figure 1 Schematic diagram of the method flow of the present invention;

[0134] Figure 2 Traffic flow relationship diagram between intersections of the present invention;

[0135] Figure 3 Vehicle departure mode diagram of the present invention;

[0136] Figure 4 Vehicle arrival characteristic diagram under different saturations of the present invention;

[0137] Figure 5 Arrival situation diagram of motor vehicles and non-motor vehicles under different saturations of the present invention;

[0138] Figure 6 Queue length change rule diagram under different saturations of the present invention;

[0139] Figure 7 Simulation section diagram of the present invention;

[0140] Figure 8 Comparison and analysis diagram of motor vehicle queue lengths under different signal control methods of the present invention;

[0141] Figure 9 Comparison and analysis diagram of non-motor vehicle queue lengths under different signal control methods of the present invention;

[0142] Figure 10 Comparison and analysis diagram of motor vehicle service volumes under different signal control methods of the present invention;

[0143] Figure 11 Comparison and analysis diagram of non-motor vehicle service volumes under different signal control methods of the present invention;

[0144] Figure 12 Comparison analysis chart of the number of served people under different signal control methods of the present invention;

[0145] Figure 13 Delay comparison analysis chart under different signal control methods of the present invention. Specific implementation manners

[0146] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0147] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0148] It should be noted that, without conflict, the embodiments in the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0149] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0150] Refer to Figures 1-13 , a maximum pressure traffic signal control method for coordinated optimization of motor vehicles and non-motor vehicles, the steps are as follows:

[0151] Step 1: Analyze the traffic flow volume and direction of the upstream intersection, and establish a traffic flow arrival rate model for the exit lanes of the upstream intersection;

[0152] Step 2: Derive the probability distribution function of vehicle travel time by deriving the geometric distribution model proposed by Robertson;

[0153] Step 3: Establish arrival rate models for motor vehicles and non-motor vehicles at the downstream intersection according to the vehicle arrival rate at the upstream intersection and the Robertson platoon dispersion model respectively;

[0154] Step 4: Based on the different arrival situations of motor vehicles and non-motor vehicles at the downstream intersection, establish a comprehensive delay model for motor vehicles and non-motor vehicles under different arrival situations;

[0155] Step 5: Based on the Robertson dispersion model to predict the queue length generated downstream, establish a vehicle queue length model under different degrees of saturation;

[0156] Step 6: To prevent vehicle queue overflow, based on the vehicle queue length obtained in Step 5, establish a maximum pressure traffic signal control model that takes into account motor vehicles and is based on the section queue ratio;

[0157] Step 7: According to the predicted queue length model of non-motor vehicles, establish a maximum pressure traffic signal control model that takes into account non-motor vehicles;

[0158] Step 8: According to the maximum pressure traffic signal control models established in Step 6 and Step 7, calculate the sum of the road pressures on the motor vehicle lane and the non-motor vehicle lane, and consider the conversion coefficient between non-motor vehicles and motor vehicles to obtain the comprehensive pressure of each phase road;

[0159] Step 9: Calculate the green light time of each phase by the ratio of the comprehensive pressure to the total pressure and the ratio to the cycle, so as to achieve the goal of comprehensively considering motor vehicles and non-motor vehicles in signal control;

[0160] Step 10: Use SUMO to build a simulation environment and conduct a comparative analysis with the classical signal control method.

[0161] Furthermore, it is possible to set up an analysis of the traffic flow rate and direction at the upstream intersection in Step 1 and establish a traffic flow arrival rate model for the exit lane of the upstream intersection: The above Step 1 includes:

[0162]

[0163] In the formula, N i is the number of vehicles in the traffic flow direction of the upstream intersection i, pcu / h; η i is the turning ratio of the traffic flow direction of the upstream intersection i (i ∈ [L, S, R represents left turn, straight, right turn]), η L + η S + η R = 1. s is the vehicle saturation flow rate, pcu / s; t g refers to the time when the traffic flow is released at the saturation flow rate during the green light period, t G is the green light release time. The calculation formula for the motor vehicle saturation flow rate is: The calculation formula for the non-motor vehicle saturation flow rate is: N 机 is the number of motor vehicles passing through in time t, N 非 is the number of non-motor vehicles passing through in time t, w is the width of the non-motor vehicle lane.

[0164]

[0165] The exit lane of the upstream intersection is formed by the convergence of vehicles in three directions: right turn, straight, and left turn. And in this invention, the classical four-phase is taken as an example, and the right-turn vehicles are not controlled by signals. G L belongs to the set of green light times for the left-turn phase, GS Belonging to the set of green light times for the straight-through phase. The traffic flow relationship diagram between intersections is as Figure 2 shown, and the vehicle departure patterns at different times are as Figure 3 shown.

[0166] Furthermore, it can be set that step 2 includes: The vehicle arrival rate at a certain section has the following relationship with the vehicle departure rate at the upstream section

[0167]

[0168] where: τ is 0.8 times the average travel time of vehicles between the upstream and downstream sections, that is a is the vehicle platoon dispersion coefficient; q 2 (j) is the vehicle arrival rate at the downstream section during the jth period; q 1 (j) is the vehicle departure rate at the upstream section during the jth period. Through derivation, the probability distribution function of the vehicle travel time can be obtained:

[0169]

[0170] where: g(T) is the probability distribution of the vehicle travel time between the upstream and downstream sections; T is the vehicle travel time; d is the distance between the upstream and downstream sections; v is the average vehicle speed between the upstream and downstream sections; a is a parameter corrected according to the observed values, usually taking a = 0.35.

[0171] Furthermore, it can be set that step 3 includes:

[0172] The vehicle arrival rate at the entrance lane of the downstream intersection can be calculated from the passing rate of the upstream intersection:

[0173] V d (t + Δt) = AV 0 (t) + (1 - A)V d (t + Δt - 1)

[0174] Dispersion coefficient:

[0175]

[0176] t = 1, 2, 3…n

[0177] V d (t + Δt) is the vehicle arrival rate at the downstream intersection at (t + Δt); V 0 (t) is the vehicle passing rate at the upstream intersection; V d (t + Δt - 1) is the vehicle arrival rate at the downstream intersection at (t + Δt - 1); A is the vehicle speed dispersion coefficient. The vehicle arrival characteristics under different saturations are as Figure 4 shown

[0178] Furthermore, it can be set that step 4 includes:

[0179] A. When the motor vehicle is under-saturated and the non-motor vehicle is under-saturated:

[0180] Motor vehicle delay (G 2 >G 1 +T):

[0181]

[0182] Non-motor vehicle delay (G 2 >G 1 +T):

[0183]

[0184] Comprehensive weighted delay:

[0185] D 1 =α 1 d 机 +β 1 d 非

[0186] In the formula, α 1 , β 1 are the weight coefficients of motor vehicle delay and non-motor vehicle delay. The weight coefficients of motor vehicle delay and non-motor vehicle delay are obtained according to the motor vehicle flow and non-motor vehicle flow in each phase. The specific calculation formula is:

[0187]

[0188] In the formula, y 机ij is the motor vehicle flow ratio in the i-th phase and the j-th cycle, y 非ij is the non-motor vehicle flow ratio in the i-th phase and the j-th cycle. Hereinafter, α n , β n are the weight coefficients of motor vehicles and non-motor vehicles in different arrival situations respectively.

[0189] B. When the motor vehicle is under-saturated and the non-motor vehicle is nearly saturated:

[0190] Motor vehicle delay (G 2 >G 1 +T):

[0191]

[0192] Non-motor vehicle delay (T < G 2 <G 1 +T):

[0193]

[0194] Comprehensive weighted delay:

[0195] D 2 = α 2 d 机 + β 2 d 非

[0196] C. When the motor vehicle flow is under-saturated and the non-motor vehicle flow is over-saturated:

[0197] Motor vehicle delay (G 2 > G 1 + T) 2 :

[0198]

[0199] Non-motor vehicle delay (G 2 < T):

[0200]

[0201] Comprehensive weighted delay:

[0202] D 3 = α 3 d 机 + β 3 d 非

[0203] D. When the motor vehicle flow is near-saturated and the non-motor vehicle flow is under-saturated: Motor vehicle delay (T < G 2 < G 1 + T):

[0204]

[0205] Non-motor vehicle delay (G 2 > G 1 + T):

[0206]

[0207] Comprehensive weighted delay:

[0208] D 4 = α 4 d 机 + β 4 d 非

[0209] E. When the motor vehicle flow is near-saturated and the non-motor vehicle flow is near-saturated: Motor vehicle delay (T < G 2 < G 1 + T):

[0210]

[0211] Non - motor vehicle delay (T < G 2 < G 1 + T):

[0212]

[0213] Composite weighted delay:

[0214] D 5 = α 5 d 机 + β 5 d 非

[0215] F. When the motor vehicle is nearly saturated and the non - motor vehicle is oversaturated: Motor vehicle delay (T < G 2 < G 1 + T):

[0216]

[0217] Non - motor vehicle delay (G 2 < T):

[0218]

[0219] Composite weighted delay:

[0220] D 6 = α 6 d 机 + β 6 d 非

[0221] G. When the motor vehicle is oversaturated and the non - motor vehicle is undersaturated: Motor vehicle delay (G 2 < T):

[0222]

[0223] Non - motor vehicle delay (G 2 > G 1 + T):

[0224]

[0225] Composite weighted delay:

[0226] D 7 = α 7 d 机 + β 7 d 非

[0227] H. When the motor vehicle is oversaturated and the non - motor vehicle is nearly saturated: Motor vehicle delay (G 2 < T):

[0228]

[0229] Non - motor vehicle delay (T < G 2 < G 1 + T):

[0230]

[0231] Comprehensive weighted delay:

[0232] D 8 = α 8 d 机 + β 8 d 非

[0233] J. When the motor vehicle is oversaturated and the non - motor vehicle is oversaturated: Motor vehicle delay (G 2 < T):

[0234]

[0235] Non - motor vehicle delay (G 2 < T):

[0236]

[0237] Comprehensive weighted delay:

[0238] D 9 = α 9 d 机 + β 9 d 非

[0239] Analysis of various arrival delays is as Figure 5 shown.

[0240] Furthermore, it can be set that step 5 includes:

[0241] (1) When the road section is unsaturated, H 0 is approximately equal to 0:

[0242]

[0243] (2) In the case of near - saturation of the road section and no initial queue downstream: The traffic volume arriving downstream is greater than the downstream traffic capacity. After the end of the driving phase, in the next cycle, the remaining equivalent traffic volume in the downstream queue is the difference between the total equivalent traffic volume reached within the downstream signal cycle and the total equivalent traffic volume released during the downstream green light period :

[0244]

[0245] Then, during the red light period of the next cycle of the road section, the average queue length of the lane is:

[0246]

[0247] (3) In the case of road section saturation and with an initial queue downstream: The traffic volume arriving downstream is greater than the downstream passing capacity. After the end of the kth time period, then in the next cycle, the initial queue equivalent traffic volume is the remaining equivalent traffic volume in the downstream queue fleet which is the total equivalent traffic volume reached within the downstream signal cycle minus the total equivalent traffic volume released during the downstream green light period The difference is:

[0248]

[0249] The queue length situation is as Figure 6 shown.

[0250] Furthermore, it can be set that step 6 includes:

[0251] The weight calculation of the upstream and downstream intersections based on the queue length is

[0252]

[0253] In order to better formulate an active vehicle overflow prevention strategy and better describe the relationship between the queue length and the road section length, the present invention depicts the influence of the road section length while considering the weight distribution of the queue length MP, and can effectively prevent the occurrence of queue length overflow. The MP signal control strategy (this strategy is a non-periodic MP strategy) can be briefly described as taking the relevant weight of each turning traffic flow from road section i to road section m as the difference between the upstream queue and the average queue at the downstream intersection, that is

[0254]

[0255] H e,e+1 (t) is the queue length between the upstream intersection and the downstream intersection at time t; L e,e+1 is the length of road section l; p e+1,e+2 (t) is the traffic flow ratio from intersection e + 1 to intersection e + 2 at time t; O m is the set of all road sections at the starting point, that is, the end point of road section m. w e,e+1 (t) is the weight difference between the upstream and downstream road sections. If w e,e+1(t) is very high, that is, the first term on the right is much higher than the second term. Then the MP signal controller should allocate more green time to intersection e+1. However, if both terms on the right are high, it means that the pressure at the intersection and its downstream sections is already very high, and there is no need to allocate a large amount of green time to the current intersection.

[0256] While considering the MP weight allocation of queue length, the occurrence of queue length overflow can be effectively prevented. In addition, in order to balance the green ratio allocation with the MP weight of queue length and take into account the influence of queue dissipation and the arrival buffer of upstream vehicles. The concept of section pressure plays a crucial role in the MP strategy. The section pressure is defined as the ratio of the number of queued vehicles on the section to the maximum number of vehicles that the section can accommodate. In fact, if the number of vehicles that are still moving towards the end of the queue upstream of the section can be measured and predicted, then these vehicles can also be included in the calculation. The pressure of each phase:

[0257]

[0258] where: (e, e+1) ∈ σ indicates that phase σ serves the turning traffic flow from e to e+1, and V e,e+1 (t) is the saturation flow rate of the turning traffic flow from e to e+1.

[0259] Furthermore, step 7 can be set to include:

[0260] The predicted non-motor vehicle queue length is used to calculate the weight of non-motor vehicles in the maximum pressure control and release the non-motor vehicle lane. After predicting the non-motor vehicle waiting time, the predicted non-motor vehicle queue

[0261]

[0262] Since it is difficult to directly measure the queue length of non-motor vehicles at intersection e, the predicted value is used instead of the actual value of the queue length. The queue length of non-motor vehicles at time t+1 is equal to the queue length at time t plus the predicted non-motor vehicle entering flow and then subtract the non-motor vehicle leaving flow v (e,e+1) is the arrival rate of non-motor vehicles from intersection e to intersection e+1, Q ij is the traffic capacity, and v is the saturation flow rate of non-motor vehicles.

[0263] Since the maximum pressure control has the characteristic of maximizing the throughput at the network level, the present invention uses the maximum pressure algorithm to calculate the number of vehicles that should be served at the intersection during the green time of each time step. The weight is calculated as

[0264]

[0265] The weight is the queue length of non-motor vehicles minus the average queue length of the downstream road. The release of non-motor vehicles at intersection e is represented by v e(t) Furthermore, the pressure of non-motor vehicles corresponding to each phase is

[0266]

[0267] Further, it can be set that step 8 includes;

[0268] The sum of road pressures is:

[0269]

[0270] The present invention comprehensively considers the pressures of motor vehicles and non-motor vehicles. By analyzing the ratio of motor vehicles to non-motor vehicles and the conversion coefficient between non-motor vehicles and motor vehicles, through WANG et al

[12] The conversion coefficient α of non-motor vehicles on the road section in a motor-vehicle and non-motor-vehicle separated environment can be obtained as 0.22, and the comprehensive pressure can be obtained as:

[0271]

[0272] δ m is the proportion of motor vehicles on the road, and δ n is the proportion of non-motor vehicles. If the green-light time allocated to the current active phase σ * in the green-light time of time period k has reached or exceeded its maximum allowable green-light time G max , then G e = G max ; if the green-light time allocated to the current active phase σ * in the green-light time of time period k is less than the minimum green-light time G min , then G e = G min .

[0273] When the queuing ratio of the motor-vehicle queue length on the road section is greater than 0.85, to simulate the phenomenon of vehicle queue overflow and even traffic deadlock, motor vehicles are given priority, and the comprehensive road traffic pressure at this time is

[0274]

[0275] Further, it can be set that step 9 includes: calculating the green-light time of each phase through the ratio of the pressure of each phase to the total pressure and the proportion of the cycle, achieving the purpose of comprehensively considering motor vehicles and non-motor vehicles in signal control

[0276]

[0277] For each intersection e, decisions are made at the end of each phase in each time period, and all these moments are called control decision times k n, whenever the time reaches k n , the corresponding phase is σ * (k n ), calculate the pressure of each phase using the result predicted by the queue length Calculate the green light time of each phase based on the ratio of the pressure of each phase to the total pressure and the ratio of the cycle

[0278] Furthermore, it can be set that step 10 includes:

[0279] To verify the feasibility of the proposed method, select the actual road network as the research object and calibrate its parameters. Take 5 intersections (intersecting with Tuodong Road, Dongfeng East Road, Renmin East Road, Chuanjin Road and Huancheng North Road) on the Beijing Road trunk line in Kunming as the research object. The distance between adjacent intersections is less than 800m, which is 510m, 500m, 780m and 520m. The simulation section is as Figure 7 shown. The traffic flow of the main intersections of the trunk line is large, the turning traffic flow is small, the motor vehicle has six lanes in both directions, and the separation of motor vehicles and non-motor vehicles is implemented, which is more in line with the conditions of signal coordination optimization of motor vehicles and non-motor vehicles in the present invention

[0280] Build a test environment using the SUMO simulation platform to simulate and analyze the case. At the same time, use the simulated detectors in SUMO for detection, compare the changes of each parameter before and after, and build a test scenario according to the requirements of the test conditions as shown in the figure. Set the simulation time to 7200s, recalibrate the traffic flow in SUMO every 900s, and the traffic flow changes from unsaturated to nearly saturated, then to oversaturated, and finally gradually decreases to unsaturated. The maximum speed of motor vehicles on Beijing Road is set to 30km / h, the vehicle length is set to 5m, the maximum speed of non-motor vehicles is set to 20km / h, and the vehicle length is set to 1.5m

[0281] Table 1 Comparative analysis of control schemes

[0282]

[0283]

[0284] In the traditional webster fixed signal timing, the traffic flow is relatively stable in the unsaturated situation with small traffic flow, and the road traffic situation is basically locked in the oversaturated traffic situation; in the oversaturated situation with large traffic flow, the queue length of the induction signal control method is too long and it is difficult to reduce the queue length of vehicles in a short time. In the oversaturated situation with large traffic flow, the overall queue length of the two methods is too large, the queue overflow phenomenon occurs, the number of served vehicles and the comprehensive number of served people are small, and the road delay is relatively large

[0285] Compared with the nearly locked Webster fixed signal timing, the maximum pressure traffic signal control can better coordinate vehicles in oversaturated situations, rapidly reduce the number of queuing vehicles, and prevent vehicle queuing from overflowing. The queue lengths of motor vehicles and non-motor vehicles change significantly, and the optimization effect is remarkable. They are reduced by 74.47% and 28.16% respectively. The service quantities of motor vehicles and non-motor vehicles are increased by 19.97% and 13.57% respectively. The comprehensive service population is increased by 17.01%, and the delay is reduced by 51.01%. Compared with the inductive signal control, the queue lengths of motor vehicles and non-motor vehicles are reduced by 67.45% and 12.38% respectively. The service quantities of motor vehicles and non-motor vehicles are increased by 17.24% and 10.36% respectively. The comprehensive service population is increased by 14.08%, and the delay is reduced by 38.62%.

[0286] The maximum pressure traffic signal control method of the present invention that takes into account both motor vehicles and non-motor vehicles can not only effectively guarantee the road network order in oversaturated situations and prevent queuing overflow in oversaturated situations, but also achieve a greater traffic throughput. Compared with the maximum pressure traffic signal control method that only considers motor vehicles, the service efficiency is further improved. The queue lengths of motor vehicles and non-motor vehicles are reduced by 8.84% and 15.82% respectively. The service quantities of motor vehicles and non-motor vehicles are increased by 0.07% and 2.08% respectively. The comprehensive service population is increased by 1.32%, and the delay is reduced by 11.11%. The optimization effect is obvious compared with the Webster fixed signal timing and the inductive signal control, and the road conditions are good. It shows that the maximum pressure traffic signal control method of the present invention that takes into account both motor vehicles and non-motor vehicles can prevent queuing overflow and effectively control the increase of the queue length at the same time. It can more quickly and actively adjust the traffic flow of the section, achieve a greater traffic throughput and service population, reduce travel delays, and improve the road travel service efficiency.

[0287] Taking the separate management of motor vehicles and non-motor vehicles as an example, the present invention analyzes the delays in different arrival situations of motor vehicles and non-motor vehicles, evenly considers the road traffic pressure under different proportions of motor vehicles and non-motor vehicles, and constructs a multi-objective optimization model for traffic signal timing that collaboratively optimizes motor vehicles and non-motor vehicles. Relying on the maximum pressure distributed signal control algorithm, the green light time of each phase is feedback-optimized in combination with the queue length. While effectively preventing queuing overflow, the maximization of traffic travel services is achieved. Four traffic performance indicators, namely motor vehicle delay, non-motor vehicle delay, vehicle queue length, and traffic travel service rate, are selected as the signal control optimization objective functions. Through case studies of five intersections on Beijing Road in Kunming, it shows that compared with the fixed-time control, inductive control, and the maximum pressure traffic signal control that only considers motor vehicles, the maximum pressure traffic signal control method that simultaneously considers motor vehicles and non-motor vehicles is more suitable for the current road traffic situation with the increasing number of non-motor vehicles. It can effectively improve the traffic throughput, improve the travel efficiency, reduce the vehicle queue length, and prevent queuing overflow. The simulation results are respectively as Figure 8 、Figure 9 , Figure 10 , Figure 11 , Figure 12 , Figure 13 As shown. The research results of the present invention provide a theoretical support for signal coordination of motor vehicles and non-motor vehicles with different saturations in a motor-vehicle and non-motor-vehicle separated environment, and can provide technical support for the research and development of signal control systems in a motor-vehicle and non-motor-vehicle separated environment, especially with obvious optimization in the case of large saturation. In addition, the present invention mainly considers the passive control of vehicle arrival, and the active control considering both vehicle arrival and speed guidance will be the focus of future research.

[0288] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles, characterized in that, the steps are as follows: S1: Analyze the traffic flow volume and direction of the upstream intersection, and establish a traffic flow arrival rate model for the exit lanes of the upstream intersection; S2: Based on the geometric distribution model proposed by Robertson, derive the probability distribution function of vehicle travel time; S3: According to the vehicle arrival rate of the upstream intersection and the Robertson platoon dispersion model, establish the arrival rate models of motor vehicles and non-motor vehicles at the downstream intersection respectively; S4: According to the comprehensive delay conditions of motor vehicles and non-motor vehicles at different arrival situations at the downstream intersection, establish a comprehensive delay model; S5: Based on the Robertson dispersion model to predict the queuing length generated downstream, establish a vehicle queuing length model under different degrees of saturation; S6: To prevent vehicle queuing from overflowing, according to the vehicle queuing length obtained in S5, establish a maximum pressure traffic signal control model based on the section queuing ratio that takes into account motor vehicles; S7: According to the predicted queuing length model of non-motor vehicle queues, establish a maximum pressure traffic signal control model that takes into account non-motor vehicles; S8: According to the maximum pressure traffic signal control models established in S6 and S7, calculate the sum of the road pressures of the motor vehicle lane and the non-motor vehicle lane, and consider the conversion coefficient between non-motor vehicles and motor vehicles to obtain the comprehensive pressure of each phase road; S9: Calculate the green light time of each phase through the ratio of the comprehensive pressure to the total pressure and the ratio of the cycle, so as to achieve the goal of signal control that comprehensively considers motor vehicles and non-motor vehicles; S10: Use SUMO to build a simulation environment and conduct a comparative analysis with the classic signal control method.

2. The maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S1, the traffic flow arrival rate model for the exit lanes of the upstream intersection is established, and the formula is as follows: Where N i is the number of vehicles in the traffic flow direction of upstream intersection i, pcu / h; η i is the turning ratio in the traffic flow direction of upstream intersection i, η L + η S + η R = 1; s is the vehicle saturation flow rate, pcu / s; t g refers to the time when the vehicle flow is released at the saturation flow rate during the green light period, t G refers to the green light release time; The calculation formula for the saturated flow rate of motor vehicles is as follows: The calculation formula for the saturation flow rate of non-motor vehicles is as follows: Among them, N 机 is the number of motor vehicles passing through at time t, and N 非 is the number of non-motor vehicles passing through at time t, and w is the width of the non-motor vehicle lane; The exit lane of the upstream intersection is formed by the convergence of vehicles in three directions: right turn, straight ahead, and left turn, G L belongs to the set of green light times for the left-turn phase, G S belongs to the set of green light times for the straight-ahead phase.

3. The maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S2, based on the geometric distribution model proposed by Robertson, the probability distribution function of vehicle travel time is derived, and the steps are as follows: The relationship between the vehicle arrival rate at a certain section and the vehicle departure rate at the upstream section is as follows where: τ is 0.8 times the average travel time of vehicles between the upstream and downstream sections, that is a is the platoon dispersion coefficient; q 2 (j) is the vehicle arrival rate at the downstream section during the j-th period; q 1 (j) is the vehicle departure rate at the upstream section during the j-th period; The probability distribution function of vehicle travel time; the expression is as follows: In the formula: g(T) is the probability distribution of vehicle travel time between the upstream and downstream sections; T is the vehicle travel time; d is the distance between the upstream and downstream sections; v is the average vehicle speed between the upstream and downstream sections; a is a parameter corrected according to the observed value, usually taking a = 0.

35.

4. The maximum pressure traffic signal control method for collaborative optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S3, according to the vehicle arrival rate of the upstream intersection and the Robertson platoon dispersion model, the arrival rate models of motor vehicles and non-motor vehicles at the downstream intersection are established respectively, and the steps are as follows: The vehicle arrival rate at the entrance lane of the downstream intersection can be calculated from the passing rate of the upstream intersection, and the formula is as follows: V d (t + Δt) = AV 0 (t) + (1 - A)V d (t + Δt - 1) Dispersion coefficient: V d (t + Δt) is the vehicle arrival rate at the downstream intersection at (t + Δt); V 0 (t) is the vehicle passing rate at the upstream intersection; V d (t + Δt - 1) is the vehicle arrival rate at the downstream intersection at the moment (t + Δt - 1); A is the vehicle speed dispersion coefficient.

5. A maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S4, according to the comprehensive delay conditions of motor vehicles and non-motor vehicles at downstream intersections under different arrival conditions, a comprehensive delay model is established, and the steps are as follows: A. When the motor vehicle is under-saturated and the non-motor vehicle is under-saturated: Motor vehicle delay G 2 >G 1 +T, the formula is as follows: Non-motor vehicle delay G 2 >G 1 +T: Comprehensive weighted delay: D 1 = α 1 d 机 + β 1 d 非 Where α 1 , β 1 are the delay weight coefficients of motor vehicles and non-motor vehicles. The delay weight coefficients of motor vehicles and non-motor vehicles are obtained according to the motor vehicle flow and non-motor vehicle flow in each phase. The specific calculation formula is as follows: where y 机ij is the motor vehicle flow ratio in the i-th phase and the j-th cycle, y 非ij is the non-motor vehicle flow ratio in the i-th phase and the j-th cycle. Hereinafter, α n , β n are the weight coefficients of motor vehicles and non-motor vehicles under different arrival conditions respectively; B. When the motor vehicle flow is undersaturated and the non-motor vehicle flow is nearly saturated, the formula is as follows: the delay of motor vehicles G 2 >G 1 +T: Non-motor vehicle delay \(T < G\) 2 \(< G\) 1 +\(T\): Comprehensive weighted delay: D 2 = α 2 d 机 + β 2 d 非 C. When the motor vehicle flow is under-saturated and the non-motor vehicle flow is over-saturated, the formula is as follows: Motor vehicle delay G 2 >G 1 +T: Non-motor vehicle delay G 2 <T: Comprehensive weighted delay: D 3 = α 3 d 机 + β 3 d 非 D. When the motor vehicle flow is nearly saturated and the non-motor vehicle flow is under-saturated, the formula is as follows: The motor vehicle delay T < G 2 <G 1 + T: Non-motor vehicle delay G 2 >G 1 +T: Comprehensive weighted delay: D 4 = α 4 d 机 + β 4 d 非 E. When the motor vehicle flow is nearly saturated and the non-motor vehicle flow is nearly saturated, the formula is as follows: The motor vehicle delay T < G 2 <G 1 + T: Non-motor vehicle delay \(T \lt G\) 2 \(\lt G\) 1 +\(T\): Comprehensive weighted delay: D 5 = α 5 d 机 + β 5 d 非 F. When the motor vehicle flow is nearly saturated and the non-motor vehicle flow is oversaturated, the formula is as follows: The delay of motor vehicles \(T < G\) 2 <G 1 +T: Non-motor vehicle delay G 2 <T: Comprehensive weighted delay: D 6 = α 6 d 机 + β 6 d 非 G. When the motor vehicle flow is oversaturated and the non-motor vehicle flow is undersaturated, the formula is as follows: Motor vehicle delay G 2 <T: Non-motor vehicle delay G 2 >G 1 +T: Comprehensive weighted delay: D 7 = α 7 d 机 + β 7 d 非 H. When the motor vehicle flow is oversaturated and the non-motor vehicle flow is nearly saturated, the formula is as follows: Motor vehicle delay G 2 <T: Non-motor vehicle delay \(T < G\) 2 <G 1 +T: Comprehensive weighted delay: D 8 = α 8 d 机 + β 8 d 非 J. When the motor vehicle is over-saturated and the non-motor vehicle is over-saturated, the formula is as follows: Motor vehicle delay G 2 <T: Non-motor vehicle delay G 2 <T: Comprehensive weighted delay: D 9 = α 9 d 机 + β 9 d 非 。 6. A maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S5, based on the Robertson discrete model to predict the queue length generated downstream, a vehicle queue length model under different saturation degrees is established, and the steps are as follows: (1) The section is unsaturated, H 0 Approximately equal to 0: (2) When the section is nearly saturated and there is no initial queue downstream: The traffic volume arriving downstream is greater than the downstream capacity. After the driving phase ends, in the next cycle, the remaining equivalent traffic volume in the downstream queue is the total equivalent traffic volume reached within the downstream signal cycle minus the total equivalent traffic volume released during the downstream green light period difference: Then during the red light period of the next cycle of the road section, the average queue length H of the lane is: (3) In the case of road section saturation, there is an initial queue downstream; If the traffic volume arriving downstream is greater than the downstream capacity, after the end of the k-th period, then in the next cycle, the initial queuing equivalent traffic volume is The equivalent traffic volume remaining in the downstream queuing vehicle fleet Is the total equivalent traffic volume reached within the downstream signal cycle And the total equivalent traffic volume released during the downstream green light period The difference:

7. A maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S6, to prevent vehicle queue overflow, according to the vehicle queue length obtained in S5, a maximum pressure traffic signal control model considering motor vehicles based on the queue ratio of the road section is established, and the steps are as follows: The weight calculation based on the queue length of upstream and downstream intersections is as follows: Regarding the relevant weight of each turning traffic flow from road section i to road section m as the difference between the upstream queue and the average queue of the downstream intersection, the expression is as follows: Among them, H e,e+1 (t) is the queue length between the upstream intersection and the downstream intersection during the t time period; L e,e+1 is the length of section l; p e+1,e+2 (t) is the traffic flow ratio from intersection e + 1 to intersection e + 2 during the t time period; O m is the set of all sections at the starting point, that is, the end point of section m.

8. A maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S7, according to the predicted queue length model of non-motor vehicles, a maximum pressure traffic signal control model considering non-motor vehicles is established, and the steps are as follows: The predicted queue length of non-motor vehicles is used to calculate the weight of non-motor vehicles in the maximum pressure control and release the non-motor vehicle lane. After the predicted waiting time of non-motor vehicles, the predicted non-motor vehicle queue, and the calculation formula is as follows:

9. A maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S8, according to the maximum pressure traffic signal control models established in S6 and S7, calculate the sum of the road pressures of the motor vehicle lane and the non-motor vehicle lane, and considering the conversion coefficient between non-motor vehicles and motor vehicles, obtain the comprehensive pressure of each phase road, and the steps are as follows; The sum of road pressures, the calculation formula is as follows: The comprehensive pressure, the calculation formula is as follows: δ m is the proportion of motor vehicles on the road, and δ n is the proportion of non-motor vehicles; when the green light time of the active phase σ * allocated in time period k has reached or exceeded its maximum allowable green light time G max , then G e = G max ; when the green light time of the current active phase σ * allocated in time period k is less than the minimum green light time G min , then G e = G min .

10. A maximum pressure traffic signal control method for the coordinated optimization of motor vehicles and non-motor vehicles according to claim 1, characterized in that, in S9, by calculating the ratio of the comprehensive pressure to the total pressure and the ratio of the cycle, calculate the green light time of each phase to achieve the goal of signal control considering both motor vehicles and non-motor vehicles comprehensively, and the calculation formula is as follows: For each intersection e, a decision is made at the end of each phase in each time period, and all these moments are called control decision times k n , whenever the time reaches k n , the corresponding phase is σ * (k n ), and the pressure of each phase is calculated using the result of queue length estimation

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