Intelligent network connection bus operation optimization control method based on heterogeneous line scene

Abstract

The invention discloses an intelligent network connection bus operation optimization control method based on a heterogeneous line scene, and relates to an intelligent network connection bus operation optimization control method. The invention aims to solve the technical problems of non-uniformity of bus time intervals and congestion of stations and intersections of intelligent networked buses running on bus lanes due to coexistence of a manual driving bus route and an intelligent networked bus route at present. According to the intelligent network connection bus operation hierarchical optimization control model established by the invention, the optimal speed control scheme and the signal priority scheme are output under the condition of considering the uncertainty of manual driving bus operation, the uniformity of the time headway is improved, and the number of times of entering a node for queuing is reduced. The method provides a new thought for efficient operation of an intelligent network connection bus company, and is of great significance in promoting large-scale operation of the intelligent network connection bus from a specific simple road to a complex road.

Description

Technical Field

[0001] The invention relates to an intelligent network-connected public transportation operation optimization control method. Background Art

[0002] As an important part of the transportation system, many cities are committed to improving the service level and attractiveness of the public transportation system. The emergence of intelligent networked buses provides new opportunities for promoting the efficient operation of urban public transportation. It can provide a comprehensive, efficient, accurate and reliable information service system for urban public transportation and comprehensively improve the level of intelligence of urban public transportation. In recent years, various cities are carrying out pilot demonstrations of intelligent networked buses. In 2017, the first batch of intelligent networked buses were successfully piloted in Shenzhen, China; in 2020, the first intelligent networked bus line "315" was opened in Changsha, China; in 2023, Singapore's intelligent networked buses conducted the first closed road test in China. The application scenarios of intelligent networked buses are in the process of expanding from specific simple roads to complex roads of urban ground public transportation. Therefore, public transportation will be in a state of coexistence of traditional manually driven bus lines (without any networked functions) and intelligent networked bus lines for a long time. This has caused problems such as uneven headway of intelligent networked buses running on bus lanes and congestion at stations and intersections. Summary of the invention

[0003] The present invention aims to solve the technical problem that the coexistence of manual bus lines and intelligent networked bus lines has caused uneven headway of intelligent networked buses traveling on bus lanes and congestion at stations and intersections, and to provide an intelligent networked bus operation optimization control method based on heterogeneous line scenarios.

[0004] The intelligent networked public transportation operation optimization control method based on heterogeneous line scenarios of the present invention is carried out in the following steps:

[0005] Step 1: Select a hybrid bus lane that includes an intelligent connected bus route and a manually driven bus route to build a heterogeneous route scenario, establish an attribute set of the intelligent connected bus route in the heterogeneous route scenario to store route and node information, and establish an attribute set of the intelligent connected bus trip in the heterogeneous route scenario to store trip operation information;

[0006] The attribute set of the intelligent networked bus route includes the length of the non-queuing section, the length of the queuing section, the speed limit value of the section, the location of the node and the timing information of the signal light, and the node includes the station and the signal light; the queuing section is the section area before the stop line of the signal light, that is, the section area where the bus needs to queue when the signal light is in the red light phase, and the length of the queuing section is determined according to the historical maximum queue length; the non-queuing section is the section outside the queuing section;

[0007] The attribute set of intelligent networked bus trips includes the dispatching headway and initial departure schedule of the trips within the control period;

[0008] Step 2: Based on the V2X technology of intelligent networked buses, the location and speed of intelligent networked buses, the data of passengers getting on and off at stations, the status of traffic lights, and the location and speed of manually driven buses are obtained in real time;

[0009] Step 3: Focus on the operation status of intelligent connected buses and build a hierarchical optimization control model for intelligent connected buses:

[0010] 1. Description of the operation status of intelligent connected buses

[0011] The present invention mainly describes the operation status of intelligent networked buses from two aspects: whether the operation status of manually driven buses is considered;

[0012] 1.1 Not considering the operating status of manually driven buses

[0013] The running time T of the intelligent networked bus in the non-queuing section between nodes i k,j (1) Queuing section running time T i k,j (2) (This time requires that the car must be moving) According to its corresponding running speed V i k,j (1) and V i k,j (2) Decision;

[0014]

[0015] Among them, L k,j (1) is the length of the non-queuing section; L k,j (2) is the length of the queue section, which is determined according to the longest queue length in history; there are M stations in total, k represents the kth station, k≤M, j is the jth signal light between station k and station k+1, and i is the train number;

[0016] Waiting time of vehicles at traffic lights and the time to reach the stop line of the signal light The relationship between them is expressed as follows:

[0017]

[0018]

[0019] in, is the time when train number i leaves station k, It’s the moment when the green light comes on; C is the green light duration;k,j is the signal period; is the waiting time of train number i at the jth signal light between station k and station k+1; The time when train number i arrives at the jth signal stop line between station k and station k+1;

[0020] The time when smart connected bus i arrives at station k+1 Time to leave station k+1 The stay time T at station k+1 i k+1,dwell The relationship between the three is shown in formula (7), the stay time at the station T i k+1,dwell The number of passengers boarding at station k+1 and the number of passengers getting off The specific formula is as follows:

[0021]

[0022] Among them, β is the average boarding and alighting time of each passenger;

[0023] 1.2 Considering the operation status of manually driven buses

[0024] According to the overlapping characteristics of bus routes, when the intelligent connected bus runs on the non-queue section and the queuing section between two stations k and k+1, there are multiple manually driven buses, which affect the running speed of the intelligent connected bus and further affect the running time of the intelligent connected bus, which will reduce the running efficiency of the intelligent connected bus. Therefore, when describing the running status of the intelligent connected bus, it is necessary to measure the impact of the uncertainty of the running of manually driven buses:

[0025] 1.2.1 Description of the operating status of non-queuing sections

[0026] The operation time of the smart connected bus on each road section is determined by its operation speed, which is in turn affected by the traffic conditions on the road section. When the smart connected bus is traveling at a certain speed, there is a manually driven bus in front of it driving at a speed lower than the speed value, and the manually driven bus that further forces the smart connected bus to reduce its speed on the current road section is defined as an obstacle bus; therefore, the smart connected bus needs to determine whether there is an obstacle bus in front of it when it is running on the road section;

[0027] (1) Determine whether there is an accessible public transportation scene ahead

[0028] like Figure 2As shown in the figure, assuming that the distance between the end point of a certain road section and the starting point is s3, at the departure time a0, the distance between the target bus 1 on the road section and the starting point is s1, and the running speed is u1; at the time a0, the distance between the bus 2 and the starting point is s2, and the running speed is u2, and bus 2 is running in front of the target bus 1. According to the positions and speeds of the two, there are two running states before the target bus 1 reaches the end point s3 of the road section:

[0029] Scenario I: When target bus 1 runs at speed u1 and bus 2 runs at speed u2 to the end point s3 of the road section, the distance between target bus 1 and bus 2 is always less than the safety distance s0 between the two vehicles. Bus 2 has no impact on the running state of target bus 1. There is no obstacle bus in front of target bus 1, satisfying the following formula (8):

[0030]

[0031] a5 is the time when the target bus 1 arrives at the destination, and a3 is the time when the bus 2 arrives at the destination;

[0032] Scenario II: At time a1, target bus 1 travels to a distance s0 from bus 2. Due to the speed limit of bus 2 in front, target bus 1 cannot maintain its original speed u1 and will be forced to slow down and follow bus 2 in front to the end of the section. Therefore, the time it takes for target bus 1 to reach the end of the section will increase by a4-a2. At this time, bus 2 is an obstacle bus for target bus 1:

[0033]

[0034] From the above formulas (8) and (9), it can be judged whether the manually driven bus on the road section will affect the operation status of the intelligent networked bus. If there is no barrier bus, the intelligent networked bus can maintain a constant speed to the end of the road section; if there is a barrier bus, the intelligent networked bus needs to adjust the speed at a certain position on the road section;

[0035] (2) Barrier-free bus scene ahead

[0036] If the smart connected bus is traveling on a non-queuing section and there is no barrier-free bus ahead, the speed is The time it takes to travel from the current position to the end of section j at a constant speed for:

[0037]

[0038] (3) Bus scene with obstacles ahead

[0039] The smart connected bus running on the non-queuing section is subject to the speed limit of the obstacle bus. In order to prevent the speed limit from increasing the running time, the smart connected bus needs to adjust its speed to surpass the obstacle bus in front. Therefore, the running process of the smart connected bus on the non-queuing section can be divided into the following three stages:

[0040] Phase 1: The obstacle bus is located in front of the intelligent connected bus i and the distance is greater than the minimum safe following distance d0 of the vehicle, that is, exist Under the condition of intelligent network bus i with speed The driving time of this stage is

[0041] Phase 2: Overtaking process; the intelligent networked bus i and the vehicle ahead are moving at a speed of Distance of the bus to the obstacle When Increase to overtaking speed After that, it starts to overtake; when the position of the intelligent networked bus i is ahead of the obstacle bus, it means that the overtaking process is completed. The driving time of this stage is

[0042] Phase 3: The smart connected bus overtakes the obstacle bus and continues to travel at a speed of The driving time of this stage is

[0043] The driving process of the above three stages must meet the following requirements:

[0044]

[0045] According to formula (11), the time to reach the destination is for:

[0046]

[0047] 1.2.2 Description of node operation status

[0048] According to the operation characteristics of bus routes, nodes are the traffic lights and the stops where passengers get on and off the bus.

[0049] (1) Description of the operating status at the signal light

[0050] When the smart connected bus i runs from the starting position of the queue section to the stop line of the signal light, it is necessary to consider whether there is a queue of manually driven buses; it is assumed that there are no other smart connected buses in the process of the smart connected bus i running from the starting position of the queue section to the stop line of the signal light;

[0051] Use the following formula (16) to determine whether the signal phase state of the smart connected bus i when it arrives at the starting position of the queue section is red or green; n k,j The remaining red light duration of the current phase when the smart connected bus i arrives at the starting position of the queue section; is the red light duration of signal light j; n k,j If it is equal to 0, it is a green light, otherwise it is a red light;

[0052]

[0053] When the red light is on, a queue of vehicles arriving at the queue section begins to form; when the green light is on, the queue begins to dissipate; the number of vehicles q in the queue of manually driven buses detected by the intelligent networked bus i in the queue section ahead k,j Therefore, the time for the manual bus queue to dissipate is txs k,j According to the traffic wave theory, it can be calculated by formula (17): Refers to the saturation flow rate;

[0054]

[0055] The travel time of the queue section is related to whether the vehicle joins the queue, the signal status and the speed of the vehicle in the queue section. According to different influencing factors, the travel time of the queue section mainly has the following three scenarios:

[0056] ①The traffic light is red

[0057] When the signal light j is red, It is necessary to determine whether the smart connected bus i joins the queue according to the dissipation time of the signal light queue, as shown in formula (18), where: is an integer variable between 0 and 1. When smart connected bus i joins the queue, otherwise

[0058]

[0059] L k,j (2) is the length of the queue section; is the speed of the smart connected bus i in the queue section before the jth signal light between station k and station k+1;

[0060] The operating time of the intelligent network-connected bus i in the queue section As shown in formula (19):

[0061]

[0062] The end time is when the vehicle passes the stop line of the signal light;

[0063] ② The signal light j is green and the queue is dissipating:

[0064] When signal j is green and the queue is dissipating, n k,j = 0 and txs k,j ≠0, and the queued vehicles are dissipating; the running time of the intelligent networked bus i in the queue section As shown in formulas (20) and (21):

[0065]

[0066] ③ The signal light j is green and the queue has completely dissipated:

[0067] When the signal light j is green and the queue has completely dissipated, n k,j = 0 and txs k,j = 0, the queued vehicles have completely disappeared; the running time of smart connected bus i in the queue section As shown in formula (22):

[0068]

[0069] For the above three situations, the decision on whether to start the bus signal priority strategy is made based on the time when the smart connected bus i reaches the stop line of the signal light j and the signal phase state, so that the smart connected bus can pass;

[0070]

[0071] r i k,j is the time when the smart connected bus i arrives at the jth signal stop line between station k and station k+1;

[0072] Formula (24) and formula (25) decide whether to start the bus signal priority strategy;

[0073]

[0074] in, When the green light comes on, The green light duration, is the red light duration of signal light j; Indicates whether to start the signal priority strategy of early red light off. If the signal priority is started, it is 1, otherwise it is 0; Indicates whether to start the signal priority strategy of delaying the green light. If the signal priority is started, it is 1, otherwise it is 0;

[0075] When equations (26) and (27) are satisfied at the same time, it means that the smart connected bus i can pass the stop line of the signal light during the green light phase, which is determined by the relationship between the time when the smart connected bus i reaches the stop line and the phase duration;

[0076]

[0077] in, The time when the red light is turned off early or the green light is delayed. Indicates whether to start the signal priority strategy of early red light off. If the signal priority is started, it is 1, otherwise it is 0; Indicates whether to start the signal priority strategy of delaying the green light. If the signal priority is started, it is 1, otherwise it is 0;

[0078] (2) Description of site operation status

[0079] The time it takes for smart connected bus i to arrive at station k+1 is Departure time is The station stay time is The waiting time at the station due to the limited number of berths is when When it is less than 30s, it can be ignored and equal to 0. As shown in equations (28)-(30), J is the number of traffic lights between the current position of smart connected bus i and station k+1; β is the average boarding and alighting time for each passenger, is the number of passengers boarding at station k+1, is the number of passengers getting off at station k+1;

[0080]

[0081]

[0082] 2. Construction of hierarchical optimization control model for intelligent connected public transportation

[0083] From the perspectives of global optimization and local optimization, the first-level optimization control model and the second-level optimization control model are constructed, and the solution algorithm of the optimization control model is designed to obtain the optimal vehicle speed control scheme and signal control scheme; firstly, the first-level optimization control model is established by considering passenger demand and traffic signal control interference factors without considering the influence of manually driven buses; however, due to the interference of the uncertainty of the operation of manually driven buses in actual operation, the intelligent networked buses cannot maintain the optimal speed planning scheme output by the first-level optimization control model, which will reduce the optimization efficiency; based on this, the second-level optimization control model is used to reasonably adjust the operating speed of the intelligent networked buses between each node and the priority duration of the signal lights;

[0084] 2.1 First-level optimization control model

[0085] The first-level optimization control model starts from the perspective of global optimization, does not consider the impact of manually driven buses, considers the constraints of economy and operation sequence, and takes improving the uniformity of headway of intelligent networked buses as the optimization goal, aiming to restore the regularity of the operation routes of intelligent networked buses:

[0086] 2.1.1 Objective Function

[0087] During the operation of the intelligent networked bus, the deviation of the headway from the dispatched headway is used as an indicator to measure the uniformity of the headway. The headway of two consecutive intelligent networked buses i and i+1 arriving at the same station k+1 is calculated. The optimization goal is to minimize the deviation from the dispatching headway h:

[0088]

[0089] is the time when smart connected bus i arrives at station k+1;

[0090] 2.1.2 Constraints

[0091] (1) Speed ​​restriction

[0092] Since the speed affects the energy consumption of the intelligent networked bus, it further affects the operating cost of the bus company. At the same time, passengers expect the bus to have a certain degree of smoothness. The present invention introduces the running speed under the greedy strategy as a criterion to ensure economy and smoothness. Here, the greedy strategy means that the intelligent networked bus running between two nodes can maintain the highest operating state of economy and smoothness, that is, its running speed is in the energy-saving driving speed range.

[0093]

[0094] V i k,j (1) is the travel speed of the intelligent network-connected bus i on the non-queuing section before the jth traffic light between station k and station k+1; V i k,j (2) is the travel speed of the intelligent connected bus i in the queue section before the jth signal light between station k and station k+1; ΔV is the speed control threshold, which is a known quantity and its specific value depends on the working conditions;

[0095] According to the "Urban Road Traffic Planning and Design Specifications", the operating speed of the queuing section should be less than or equal to the design speed of the section ρV designed ; Since the queue section is close to the traffic light, the speed V of the smart connected bus i in the queue section i k,j (2) Less than or equal to ρV designed ;

[0096] V i k,j (2)≤ρV designed (33)

[0097] Where V designed is the design speed of the road section, which is a known quantity, and ρ is 0.7;

[0098] In summary, the vehicle speed constraint should satisfy the following equations (34) and (35):

[0099] max{V min ,V eco -ΔV}≤V i k,j (1)≤min{V max ,V eco +ΔV} (34)

[0100] max{V min ,V eco -ΔV}≤V i k,j (2)≤min{V max ,V eco +ΔV,ρV designed} (35)

[0101] Among them, V min and V max are the minimum and maximum operating speeds of the road section, both of which are known quantities;

[0102] (2) Run order constraints

[0103] The goal of the optimization model of the present invention is to restore the regularity of the routes of the intelligent networked buses. Therefore, when implementing the control strategy, it is necessary to ensure that the operating order of the front and rear intelligent networked buses does not change.

[0104]

[0105] The time when smart connected bus i leaves station k+1;

[0106] 2.2 Second-level optimization control model

[0107] The second-level optimization control model starts from the perspective of local optimization, considers the output scheme of the first-level optimization control model, the impact of manually driven buses, and the constraints of speed-changing overtaking, and establishes an optimization model with dual objectives of minimizing the number of times the intelligent networked buses enter the node queue and minimizing the deviation between the running speed sequence between nodes and the speed planned by the first-level optimization control model, aiming to reduce the impact of uncertainty brought by manually driven buses;

[0108] 2.2.1 Objective Function

[0109] (1) Node queuing times

[0110] Since my country's bus lanes have repeated line characteristics, when the smart networked bus arrives at each node, it is necessary to consider the operating status of other lines of manually driven buses; at the bus station, due to the limited number of parking spaces, it is easy to cause the smart networked bus and the manually driven bus to queue up to enter the station; at the signal light, the smart networked bus is affected by the signal phase duration, causing the smart networked bus to enter the queue of manually driven buses that have not dissipated; based on this, the smart networked bus is likely to increase the number of stops, resulting in frequent acceleration and deceleration behaviors, which reduces the comfort of passengers and increases the operating energy consumption of the smart networked bus; the present invention takes reducing the number of times the smart networked bus enters the queues of each node as the optimization goal, and reduces the impact of manually driven buses;

[0111] ①Number of queues at traffic lights

[0112] The phenomenon that the smart connected bus arrives at the end of the queue before the queue of manually driven buses at the traffic light has not dissipated is defined as the smart connected bus joining the queue at the intersection, e.g. Figure 3 (a) is the number of queues of smart connected bus i at the jth signal light between station k and station k+1, which is 0 or 1;

[0113] ②Number of queues at the site

[0114] Station queuing refers to the phenomenon that when smart connected buses and manual buses enter the station at the same time, due to the limitation of the number of parking spaces, the manual buses in front are serving at the station, while the smart connected buses in the back have to queue up in front of the station. Figure 3 (b) shows that when the queuing time of the intelligent networked bus to the station is greater than t1, the intelligent networked bus will enter the queuing waiting stage; A variable of 0 or 1, indicating whether the smart networked bus i is waiting in line at the station k+1, 1 represents queuing, and 0 represents not queuing; the present invention assumes that the smart networked bus i+1 located at the rear will not be affected by the smart networked bus i in front and will not queue when it is at the station;

[0115]

[0116] is the waiting time of smart connected bus i due to the limited number of berths at station k+1;

[0117] In summary, the optimization goal is to minimize the number of times that the intelligent networked bus enters the node queue:

[0118]

[0119] (2) Running speed deviation

[0120] The operation time of the smart connected bus on each road section is determined by its operation speed, which is in turn affected by the traffic conditions on the road section. When the smart connected bus is traveling at a certain speed, there is a manually driven bus in front of it driving at a speed lower than the speed value, and the manually driven bus that further forces the smart connected bus to reduce its speed on the current road section is defined as an obstacle bus. Therefore, the smart connected bus needs to determine whether there is an obstacle bus in front of it when it is running on the road section.

[0121] Affected by the interference of the obstacle bus, the intelligent networked bus produces a speed sequence by changing speed and overtaking between nodes. The optimization goal is to minimize the deviation between each running speed in the sequence and the running speed output by the first-level optimization control model. This improves the running smoothness while responding to the dynamic changes of the manually driven bus in real time.

[0122]

[0123] 2.2.2 Constraints

[0124] (1) Speed ​​restriction

[0125] On the basis of satisfying equations (34) and (35), the overtaking speed of the intelligent networked bus i is The speed must be greater than the speed of the obstructing bus. The operating speed of each non-queue section and the operating speed of each queuing section in the second-level optimization control model of intelligent connected bus i should satisfy the following equations (40)-(43):

[0126]

[0127]

[0128]

[0129] ΔV is the speed control threshold, which is a known quantity and its specific value depends on the operating conditions;

[0130] Queuing section: is the speed of the smart connected bus i in the queue section before the jth signal light between station k and station k+1;

[0131] Non-queue sections:

[0132] Phase 1: The obstacle bus is located in front of the intelligent connected bus i and the distance is greater than the minimum safe following distance d0 of the vehicle, that is, exist Under the condition of intelligent network bus i with speed The driving time of this stage is

[0133] Phase 2: Overtaking process; the intelligent networked bus i and the vehicle ahead are moving at a speed of Distance of the bus to the obstacle When Increase to overtaking speed After that, it starts to overtake; when the position of the intelligent networked bus i is ahead of the obstacle bus, it means that the overtaking process is completed. The driving time of this stage is

[0134] Phase 3: The smart connected bus overtakes the obstacle bus and continues to travel at a speed of The driving time of this stage is

[0135] (2) Overtaking restrictions

[0136] Since the queuing section of the urban road is close to the traffic light and overtaking is not possible, the second stage of overtaking by the intelligent networked bus i should be completed before the end of the non-queuing section:

[0137]

[0138] Among them, L k,j (1) is the length of the non-queuing section before the jth signal light between station k and station k+1. The meanings of the other physical quantities are the same as those in formulas (40)-(43);

[0139] (3) Signal control constraints

[0140] In order to avoid conflicts in bus priority applications, the number of bus priority applications in each signal cycle is limited to one at most. At the same time, it is necessary to ensure that the green light duration of the bus non-priority phase after the red light is turned off early or the green light is extended meets the safety crossing needs of pedestrians in all directions:

[0141]

[0142]

[0143] in, and They respectively refer to whether to start the signal priority strategy of early red light off and whether to start the signal priority strategy of delayed green light. If the signal priority is started, it is 1, otherwise it is 0;

[0144] μ k,j and are the vehicle flow, saturated flow, green-to-signal ratio and safe pedestrian crossing time of the bus non-priority phase of signal light j; Minimum green light duration to avoid oversaturation of non-priority phases for public transportation;

[0145] t0 is the vehicle startup loss time; is the length of the pedestrian crossing; V p is the walking speed of pedestrians, which is 1.5m / s; I k,j is the green light interval, set to 5s;

[0146] Step 4: In order to improve the accuracy and real-time performance of the operation control of the intelligent networked bus, the present invention solves the hierarchical optimization control model of the intelligent networked bus based on the idea of ​​rolling optimization; the genetic algorithm (GA) is used to solve the first-level single-objective optimization control model at each triggering moment of the first-level rolling optimization to obtain the planned speed of the intelligent networked bus;

[0147] At each triggering moment of the second-level rolling optimization, the non-dominated sorting genetic algorithm (NSGA-II) is used to solve the second-level optimization control model to obtain the optimal speed control scheme and signal priority adjustment scheme. The algorithm flow chart is shown in Figure 4 .

[0148] The headway is the time difference between two adjacent vehicles arriving at the same bus stop; the dispatch headway is the time difference between adjacent vehicles leaving the stop as determined in advance in the departure schedule and is a known quantity.

[0149] Bus tandem refers to the phenomenon that the arrival time difference of adjacent buses at a certain station is very small or they arrive at the station at the same time.

[0150] Advantages of the present invention:

[0151] The present invention focuses on the heterogeneous scenarios of traditional manually driven bus lines and intelligent networked bus lines on bus lanes, aiming to solve the problems of intelligent networked buses stringing together and queuing at stations and traffic lights. The hierarchical optimization control model for intelligent networked bus operation established by the present invention outputs the optimal speed control scheme and signal priority scheme under the condition of uncertainty in the operation of manually driven buses, improves the uniformity of headway time, and reduces the number of times of entering node queues. The present invention provides new ideas for the efficient operation of intelligent networked bus companies, and is of great significance to promoting the large-scale operation of intelligent networked buses from specific simple roads to complex roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0152] Figure 1 This is a schematic diagram of the intelligent network-connected bus route of the present invention, and the yellow vehicle is a non-bus;

[0153] Figure 2 Schematic diagram of two operation scenarios of target bus 1 on the road section;

[0154] Figure 3 Schematic diagram of node queuing phenomenon: (a) queuing at the traffic light; (b) queuing at the station; yellow vehicles are not buses;

[0155] Figure 4 This is a rolling optimization control flow chart in step 4 of the present invention;

[0156] Figure 5 This is a schematic diagram of the No. 41 bus route in Beijing for the first test;

[0157] Figure 6 The lighting parameters of each signal light in Test 1;

[0158] Figure 7 To test the speed parameters of each section of bus route 41 in Beijing;

[0159] Figure 8 This is the spatiotemporal trajectory diagram of the output of the no-control case in Experiment 1;

[0160] Fig. 9 This is the spatiotemporal trajectory diagram output for the optimization control case in Experiment 1;

[0161] Fig.10 This is the data diagram of the headway deviation value output for the uncontrolled case in Test 1;

[0162] Fig.11 This is the data graph of the headway deviation value output for the optimization control case in Experiment 1.

[0163] The present invention is verified by the following tests:

[0164] Experiment 1: This experiment is an optimization control method for intelligent connected bus operation based on heterogeneous line scenarios, which is carried out in the following steps:

[0165] This experiment established a simulation case based on the example data of Beijing No. 41 intelligent network bus, and further compared and analyzed the results of the non-control optimization scheme and the optimization control scheme proposed by the present invention. Figure 5 As shown in the figure, the section of Beijing Bus No. 41 from the first stop Dongdan Road to Pingleyuan is located on the bus lane and is not affected by social vehicles, which meets the research object of the present invention. The total length of this section is 7067m, including 12 stations and 18 traffic lights. The energy-saving driving speed range of Bus No. 41 is [30,40] km / h. The present invention selects the energy-saving driving speed V eco =30km / h; the standard dispatch headway is set to 360s.

[0166] The passenger arrival rate and alighting rate at each stop of bus route 41 are shown in Table 1. The number of passengers getting on at each stop is calculated by multiplying the headway between the previous smart connected bus and the current bus at each stop by the passenger arrival rate at that stop; the number of passengers getting off at each stop is calculated by multiplying the number of passengers in the smart connected bus when it arrives at the stop by the passenger alighting rate at that stop; it is assumed here that the average boarding and alighting time per person is 2 seconds.

[0167] Table 1 Passenger arrival rate and alighting rate at each station

[0168]

[0169] Assuming that the first phase of each signal light is the phase for the intelligent networked bus to pass (i.e., the green light), the lighting parameters of each signal light are as follows: Figure 6 As shown in the figure, when the smart connected bus No. 1 leaves the station 1, the green lights of all the traffic lights are on. The length of the queue section is determined according to the number of phases of the traffic lights. The length of the queue section of the second phase (the traffic lights set up for pedestrians to cross the street, without lateral traffic flow) is set to 10m, and the length of the queue section of other phases (with lateral traffic flow) is set to 20m.

[0170] In order to verify that the optimization control model established by the present invention can respond in real time to the impact of uncertainty of manually driven buses in mixed traffic environments, the intelligent networked bus system updates the operating status of manually driven buses on the route every 100 seconds:

[0171] ① Assume that there is always a manually driven bus on each non-queuing section, and the speed is in the range of [0, 15] m / s (obtained from the historical data of bus route 41, such as Figure 7 As shown in FIG. 2 , an integer is randomly generated as the speed of the manually driven bus. Table 2 shows the position and speed parameters of some manually driven buses at the 100th second.

[0172] ② Randomly generate 18 integers in the interval [0,16] as the queue dissipation time of manually driven buses at each signal light when the green light is on in the signal cycle of the 100th second, as shown in Table 3;

[0173] ③ Randomly generate 12 integers as the arrival time of the manually driven buses at each station, for example: 113s, 273s, 357s, 467s, 575s, 610s represent the arrival time of the manually driven buses at station 2 in the 6 updates. Here, the arrival time of the two buses is less than 30s, which is considered as station queuing. The influence of vehicle dynamics is ignored.

[0174] Table 2 Parameters of some manually driven buses

[0175] Location (m) 48 267 343 840 1117 1305 1564 2199 2331 2524 Speed ​​(m / s) 9 15 9 6 4 5 6 11 9 12

[0176] Table 3 Dissipation time of queues for manually driven buses

[0177] traffic light sequence 1 2 3 4 5 6 7 8 9 Queue dissipation time (s) 11 9 6 14 12 12 8 8 6 traffic light sequence 10 11 12 13 14 15 16 17 18 Queue dissipation time (s) 5 14 4 5 2 4 10 9 8

[0178] The specific algorithm process is as follows:

[0179] The first-level rolling optimization based on event and time dual drive. The rolling optimization process is activated by the departure event of the intelligent networked bus, and the station sequence k in each control time domain is identified. fl ={1…k…M}, fl is determined by the number of stations. The state of manually driven buses on each section and queuing section at the trigger time (the intelligent connected bus leaves the station), the phase state of each signal light, the passenger flow data of each station (the operating state data of manually driven buses in 1.1 is not considered) are used as the input of the optimization control model to solve the single-objective optimization model and obtain the speed planning scheme for each non-queuing section and queuing section. When the intelligent connected bus arrives at the next station, the above process is reactivated and repeated multiple times until the terminal. The basic process of the first-level rolling optimization control of the intelligent connected bus embedded in GA can be expressed as:

[0180]

[0181] The second-level rolling optimization based on time-driven. Input the initial position s when the smart connected bus i leaves the station i,1 =p k (p k is the location of site k), initial time The first-level optimization control scheme outputs the planned vehicle speed, etc., and solves the dual-objective optimization model to determine the position s i,1 The speed control scheme to station k+1 and the priority scheme of each signal light. Traveling over the rolling step length t with the current control scheme DRHAfter that, the rolling optimization times are updated to b=b+1, the state information of the manually driven bus ahead is obtained again and the process is activated. After multiple cycles until reaching station k+1, the first-level rolling optimization control process is reactivated. The basic process of the second-level rolling optimization of the intelligent connected bus embedded in NSGA-II can be expressed as:

[0182]

[0183] The number of stations can determine that the first-level rolling optimization times of each intelligent network bus is 11, and the second-level rolling step length t is set DRH =30s. The minimum green light time of each signal light is set to 30% of the initial green light time, and the minimum safety distance is set to d0 = 5m. Other relevant parameters in the algorithm are set as follows: Crossing probability P C =0.7, mutation probability P M = 0.1, initial population size N0 = 30 and maximum number of iterations E = 200. The operation status of intelligent networked buses is simulated by Matlab and the solution algorithm is written.

[0184] The following describes the specific hierarchical rolling optimization control process using the operation control results of bus 5 (i is 5) as an example. First, the first-level speed planning scheme is output under the premise of minimizing the uniformity of the headway time, as shown in Table 4, and the first sequence in the first-level optimization result, that is, the planned speed sequence between station 1 and station 2, is used as the input parameter of the second-level rolling optimization. Considering the two goals of the number of node queues and the deviation from the first-level planned vehicle speed, the second-level speed control scheme and signal optimization scheme are output, but only the rolling step t is actually executed. DRH The optimal control scheme within the time limit is t DRH After that, the second-level rolling optimization control process is activated again, and the specific process is shown in Table 5. The above process is repeated until the train arrives at station 2, and the second-level optimization results are shown in Table 6. The time to arrive at station 2 is 1856s.

[0185] Table 4 Bus 5 first level optimization results

[0186] Site 1 2 3 4 5 6 Arrival time(s) — 1847 1994 2130 2211 2246 Departure time(s) 1800 1848 1997 2133 2213 2247 Site S7 S8 S9 S10 S11 S12 Arrival time(s) 2360 2455 2495 2599 2676 2738 Departure time(s) 2365 2457 2496 2604 2677 —

[0187] Table 5bus 5 second level rolling optimization process

[0188]

[0189] Table 6 Bus 5 second level optimization results

[0190] Location (m) 0 88 96 177 325 Time(s) 1800 1809 1810 1830 1856

[0191] Table 7. Number of queues at stations

[0192] Site 1 2 3 4 5 6 7 8 9 10 11 12 total No control case 0 0 1 0 1 0 1 1 1 1 0 1 7 Optimization control case 0 0 0 2 0 0 0 0 0 0 0 0 2

[0193] Table 8 Number of queues at intersections

[0194]

[0195] Tables 7 and 8 show the number of queues at various stations and traffic lights for a total of 8 buses on Route 41 during the study period. Without control, the number of queues at stations and traffic lights was 7 and 79 times, respectively, which was reduced by 71% and 73% after hierarchical optimization control.

[0196] Figure 8 and Fig. 9 The spatiotemporal trajectory diagram of each intelligent networked bus during the study period is given. Without adjusting the operation status of the intelligent networked buses on the route, the operation regularity of each bus is poor (e.g. Figure 8 ), and the speed control and signal adjustment greatly improve the operation effect (such as Fig. 9 ).

[0197] Fig.10 and Fig.11 The deviation value of the headway between each intelligent networked bus and the preceding bus at each station during the study period is given, which is equal to the ratio of the actual headway to the standard dispatched headway. Compared with the uncontrolled simulation case ( Fig.10 ), the optimization control method proposed in the present invention ( Fig.11 ) reduces the headway deviation by 67%. Therefore, the optimization control method proposed by the present invention is effective in restoring line regularity and reducing node queues.

[0198] The present invention establishes a hierarchical optimization control model for intelligent networked public transportation operation through two strategies: speed control and signal priority. Based on the nonlinear characteristics of the model and the real-time requirements of the algorithm, an optimization control algorithm based on the idea of ​​rolling optimization is designed. Finally, two cases are simulated using matlab to compare and analyze the results of no control and optimization control. The headway deviation is reduced by 67%, and the number of queues at each station and intersection is reduced by 71% and 73% respectively. Therefore, the optimization control method proposed in the present invention is effective in restoring the regularity of the line and reducing node queues.

Claims

1. An intelligent networked bus operation optimization control method based on heterogeneous line scenarios, characterized by The operation optimization control method of intelligent connected public transportation based on heterogeneous line scenarios is carried out in the following steps: Step 1: Select a hybrid bus lane that includes an intelligent connected bus route and a manually driven bus route to build a heterogeneous route scenario, establish an attribute set of the intelligent connected bus route in the heterogeneous route scenario to store route and node information, and establish an attribute set of the intelligent connected bus trip in the heterogeneous route scenario to store trip operation information; Step 2: Based on the V2X technology of intelligent networked buses, the location and speed of intelligent networked buses, the data of passengers getting on and off at the stations, the status of traffic lights, and the location and speed of manually driven buses are obtained in real time; Step 3: Based on the operation status of intelligent connected buses, a hierarchical optimization control model for intelligent connected bus operation is constructed: Step 4: Solve the intelligent connected bus operation hierarchical optimization control model constructed in step 3 based on the rolling optimization method to obtain the planned speed, optimal speed control plan and signal priority adjustment plan of the intelligent connected bus.

2. The intelligent networked bus operation optimization control method based on heterogeneous line scenarios according to claim 1 is characterized in that The attribute set of the intelligent connected bus route described in step one includes the length of the non-queuing section, the length of the queuing section, the speed limit value of the section, the location of the node and the timing information of the signal light, and the nodes include the stations and the signal lights; the queuing section is the section area before the stop line of the signal light, that is, the section area where the buses need to queue when the signal light is in the red light phase, and the length of the queuing section is determined according to the historical maximum queue length; the non-queuing section is the section outside the queuing section.

3. The intelligent networked bus operation optimization control method based on heterogeneous line scenarios according to claim 1 is characterized in that The intelligent networked bus trip attribute set described in step 1 includes the dispatching headway and initial departure schedule of the trip within the control period.

4. The intelligent networked bus operation optimization control method based on heterogeneous line scenarios according to claim 1 is characterized in that In step 3, based on the operation status of intelligent connected buses, the specific process of constructing the hierarchical optimization control model of intelligent connected bus operation is as follows: 1) Establish the operation status data of intelligent networked public transportation based on the information established in step 1 and the data obtained in step 2: 2) Based on the data in 1 above, a hierarchical optimization control model for intelligent connected buses is constructed.

5. The intelligent networked bus operation optimization control method based on heterogeneous line scenarios according to claim 4 is characterized in that In step 4, the genetic algorithm is used to solve the first-level single-objective optimization control model at each triggering moment of the first-level rolling optimization to obtain the planned speed of the intelligent networked bus; At each triggering moment of the second-level rolling optimization, the non-dominated sorting genetic algorithm is used to solve the second-level optimization control model to obtain the optimal speed control scheme and signal priority adjustment scheme.

6. The intelligent networked public transportation operation optimization control method based on heterogeneous line scenarios according to claim 5 is characterized in that In step 4, when solving the first-level single-objective optimization control model using the genetic algorithm in the first-level rolling optimization, the data of 1.1 in step 3 on the operating status of the intelligent connected bus without considering the manually driven bus is substituted into it.

7. The intelligent networked public transportation operation optimization control method based on heterogeneous line scenarios according to claim 5 is characterized in that In step 4, when solving the second-level optimization control model using the non-dominated sorting genetic algorithm in the second-level rolling optimization, the data of the operating status of the intelligent connected bus under the manually driven bus in step 3 is substituted into it.

8. The intelligent networked bus operation optimization control method based on heterogeneous line scenarios according to claim 4 is characterized in that Step 3 1) The process of establishing the operation status data of the intelligent networked bus based on the information established in step 1 and the data obtained in step 2 is as follows: 1.

1. The establishment of the operating status data of the intelligent networked bus without considering the manual driving bus, the specific process is as follows: Step 31: Obtain the running time T of the intelligent networked bus in the non-queue section between each node i k,j (1) and the running time T of the queue section i k,j (2), the expression is: in, L k,j (1) is the length of the non-queuing section, L k,j (2) is the length of the queue section; k represents the kth station, j is the jth signal light between station k and station k+1, and i is the train number; Step 32: Based on the waiting time of vehicle i at the jth signal light between station k and station k+1 The running time T of the intelligent networked bus in the non-queuing section between nodes i k,j (1) The running time of the queue section T i k,j (2) Calculate the time when train number i arrives at the jth traffic light stop line between station k and station k+1 The expression is: (3) in, is the time when train number i leaves station k, It’s the moment when the green light comes on; C is the green light duration; k,j is the signal period; is the waiting time of train number i at the jth signal light between station k and station k+1; The time when train number i arrives at the jth signal stop line between station k and station k+1; Step 33: The time when train number i leaves station k The running time T of the intelligent networked bus in the non-queuing section between nodes i k,j (1) Queuing section running time T i k,j (2) The waiting time of train number i at the jth traffic light between station k and station k+1 Get the time when smart connected bus i arrives at station k+1 The expression is: Step 34: Number of boarding passengers based on station k+1 and the number of passengers getting off Get the stop time T of the site i k+1,dwell , the expression is: Among them, β is the average boarding and alighting time of each passenger; Step 35: Based on the time when the intelligent networked bus i arrives at the station k+1 and the dwell time T at the site i k+1,dwell , get the time when smart connected bus i leaves station k+1 The expression is: 1.2 Considering the establishment of the operating status data of the intelligent networked bus under manual driving, the specific process is as follows: 1.2.1 The establishment of non-queuing section operation status data, the specific process is as follows: (1) Barrier-free bus scene ahead If the smart connected bus is traveling on a non-queuing section and there is no barrier-free bus ahead, the speed is The time it takes to travel from the current position to the end of section j at a constant speed for: (2) Bus scene with obstacles ahead The driving process of smart connected buses on non-queuing sections can be divided into the following three stages: Phase 1: The obstacle bus is located in front of the intelligent connected bus i place, and Greater than the minimum safe following distance d0, that is exist Under the condition of intelligent network bus i with speed Driving at a constant speed, the driving time of stage 1 is Phase 2: Overtaking process; the intelligent networked bus i and the vehicle ahead are moving at a speed of Distance of the bus to the obstacle When Increase to overtaking speed After that, it starts to overtake; when the position of the intelligent networked bus i is ahead of the obstacle bus, it means that the overtaking process is completed, and the driving time of stage 2 is Phase 3: The smart connected bus overtakes the obstacle bus and continues to travel at a speed of Driving at a constant speed, the driving time of stage 3 is The driving process of the above three stages must meet the following requirements: According to equations (10) to (13), the time to reach the destination is for: 1.2.2 Establishment of node operation status data, the specific process is as follows: (1) Establishment of the operating status data at the traffic light. The specific process is as follows: Assume that there are no other smart connected buses when smart connected bus i runs from the starting position of the queue section to the stop line of the signal light; Formula (14) is used to determine whether the signal phase state of the intelligent network-connected bus i when it arrives at the starting position of the queue section is red or green; in, n k,j The remaining red light duration of the current phase when the smart connected bus i arrives at the starting position of the queue section; is the red light duration of signal light j; n k,j If it is equal to 0, it is a green light, otherwise it is a red light; When the red light turns on, a queue of vehicles arriving at the queuing section begins to form; when the green light turns on, the queue begins to dissipate; If the number of vehicles q in the queue of manually driven buses in the process of intelligent network-connected bus i running from the starting position of the queue section to the stop line of the signal light is k,j , then calculate the dissipation time txs of the queue of manually driven buses k,j , the expression is: in, Refers to the saturation flow rate; There are three main scenarios for the running time of the queue section: ①The traffic light is red When the signal light j is red, It is necessary to determine whether the smart connected bus i joins the queue according to the dissipation time of the signal light queue, as shown in formula (18), where: is an integer variable between 0 and 1. When smart connected bus i joins the queue, otherwise L k,j (2) is the length of the queue section; is the speed of the smart connected bus i in the queue section before the jth signal light between station k and station k+1; The operating time of the intelligent network-connected bus i in the queue section As shown in formula (17): The end time is when the vehicle passes the stop line of the signal light; ② The signal light j is green and the queue is dissipating: When signal j is green and the queue is dissipating, n k,j =0 and txs k,j ≠0, and the queued vehicles are dissipating; the running time of the intelligent networked bus i in the queue section As shown in formulas (18) and (19): ③ The signal light j is green and the queue has completely dissipated: When the signal light j is green and the queue has completely dissipated, n k,j =0 and txs k,j = 0, the queued vehicles have completely disappeared; the running time of smart connected bus i in the queue section As shown in formula (20): For the above three situations, the decision on whether to start the bus signal priority strategy is made based on the time when the smart connected bus i reaches the stop line of the signal light j and the signal phase state, so that the smart connected bus can pass; is the time when the smart connected bus i arrives at the jth signal stop line between station k and station k+1; Formula (22) and Formula (23) decide whether to start the bus signal priority strategy; in, When the green light comes on, The green light duration, is the red light duration of signal light j; Indicates whether to start the signal priority strategy of early red light off. If the signal priority is started, it is 1, otherwise it is 0; Indicates whether to start the signal priority strategy of delaying the green light. If the signal priority is started, it is 1, otherwise it is 0; When equations (24) and (25) are satisfied at the same time, it means that the smart connected bus i can pass the stop line of the signal light during the green light phase, which is determined by the relationship between the time when the smart connected bus i reaches the stop line and the phase duration; in, The time when the red light is turned off early or the green light is delayed. Indicates whether to start the signal priority strategy of early red light off. If the signal priority is started, it is 1, otherwise it is 0; Indicates whether to start the signal priority strategy of delaying the green light. If the signal priority is started, it is 1, otherwise it is 0; (2) Establishment of site operation status data. The specific process is as follows: The time it takes for smart connected bus i to arrive at station k+1 is Departure time is The station stay time is The waiting time at the station due to the limited number of berths is when When it is less than 30s, it can be ignored and equal to 0. As shown in equations (26)-(28), J is the number of traffic lights between the current position of smart connected bus i and station k+1; β is the average boarding and alighting time for each passenger, is the number of passengers boarding at station k+1, is the number of passengers getting off at station k+1; 9. The intelligent networked public transportation operation optimization control method based on heterogeneous line scenarios according to claim 8 is characterized in that Step 3 2) Based on the data in 1, a hierarchical optimization control model for intelligent networked public transportation is constructed. The specific process is as follows: The hierarchical optimization control model of intelligent networked public transportation includes the first-level optimization control model and the second-level optimization control model; 2.

1. Construct the first-level optimization control model without considering the impact of manually driven buses. The specific process is as follows: 2.1.

1. Construct the objective function. The specific process is as follows: During the operation of the intelligent networked bus, the deviation of the headway from the dispatched headway is used as an indicator to measure the uniformity of the headway. The headway of two consecutive intelligent networked buses i and i+1 arriving at the same station k+1 is calculated. The optimization goal is to minimize the deviation from the dispatching headway h: is the time when the intelligent connected bus i arrives at station k+1; 2.1.2 The constraints are as follows: (1) Vehicle speed constraints are as follows: is the travel speed of the intelligent connected bus i on the non-queuing section before the jth signal light between station k and station k+1; is the travel speed of the intelligent connected bus i in the queuing section before the jth signal light between station k and station k+1; ΔV is the speed control threshold; It is the energy-saving driving speed range; In i k,j (2)≤ρV designed (31) Where V designed is the design speed of the road section, which is a known quantity, ρ is 0.7; V i k,j (2) is the speed of the smart connected bus i in the queue section; In summary, the vehicle speed constraint should satisfy the following equations (32) and (33): max{V min ,V eco -ΔV}≤V i k,j (1)≤min{V max ,V eco +ΔV} (32) max{V min ,V eco -ΔV}≤V i k,j (2)≤min{V max ,V eco +ΔV,ρV designed } (33) Among them, V min and V max are the minimum and maximum operating speeds of the road section, both of which are known quantities; (2) The running order constraints are as follows: The time when smart connected bus i leaves station k+1; The time when the intelligent network-connected bus i+1 leaves the station k+1; 2.2 Construct the second-level optimization control model. The specific process is as follows: 2.2.1 Construct the objective function. The specific process is as follows: (1) Objective function of node queue times: ①Number of queues at traffic lights The phenomenon that the smart connected bus arrives at the end of the queue before the queue of manually driven buses at the signal light has not dissipated is defined as the smart connected bus joining the queue at the intersection; is the number of queues of smart connected bus i at the jth signal light between station k and station k+1, which is 0 or 1; ②Number of queues at the site Queuing at a station refers to the phenomenon that when an intelligent network-connected bus and a manually-driven bus enter a station at the same time, due to the limitation of the number of parking spaces, the intelligent network-connected bus at the rear must queue up in front of the station while the manually-driven bus in front is serving at the station. It is defined that when the queuing time of an intelligent network-connected bus at a station is greater than t1, the intelligent network-connected bus will enter the queuing waiting stage; A variable of 0 or 1, indicating whether the smart networked bus i is waiting in line at the station k+1, 1 represents queuing, and 0 represents not queuing; the present invention assumes that the smart networked bus i+1 located at the rear will not be affected by the smart networked bus i in front and will not queue when it is at the station; is the waiting time of smart connected bus i due to the limited number of berths at station k+1; In summary, the optimization goal is to minimize the number of times that the intelligent networked bus enters the node queue: (2) Construct the objective function of the running speed deviation: 2.2.2 The constraints are as follows: (1) Vehicle speed constraints are as follows: On the basis of satisfying equations (34) and (35), the overtaking speed of the intelligent networked bus i is The speed must be greater than the speed of the obstructing bus. The operating speed of each non-queue section and the operating speed of each queuing section in the second-level optimization control model of intelligent connected bus i should satisfy the following equations (38)-(41): ΔV is the speed control threshold, which is a known quantity and its specific value depends on the operating conditions; Queuing section: is the speed of the smart connected bus i in the queue section before the jth signal light between station k and station k+1; Non-queue sections: Phase 1: The obstacle bus is located in front of the intelligent connected bus i and the distance is greater than the minimum safe following distance d0 of the vehicle, that is, exist Under the condition of intelligent network bus i with speed The driving time of this stage is Phase 2: Overtaking process; Intelligent networked bus i and the front at speed Distance of the bus to the obstacle When Increase to overtaking speed After that, it starts to overtake; when the position of the intelligent networked bus i is ahead of the obstacle bus, it means that the overtaking process is completed. The driving time of this stage is Phase 3: The smart connected bus overtakes the obstacle bus and continues to travel at a speed of The driving time of this stage is (2) Overtaking restrictions are as follows: Since the queuing section of the urban road is close to the traffic light and overtaking is not possible, the second stage of overtaking by the intelligent networked bus i should be completed before the end of the non-queuing section: Among them, L k,j (1) is the length of the non-queuing section before the jth signal light between station k and station k+1. The meanings of the other physical quantities are the same as those in formulas (40)-(43); (3) The signal control constraints are as follows: In order to avoid conflicts in bus priority applications, the number of bus priority applications in each signal cycle is limited to one at most. At the same time, it is necessary to ensure that the green light duration of the bus non-priority phase after the red light is turned off early or the green light is extended meets the safety crossing needs of pedestrians in all directions: in, and They respectively refer to whether to start the signal priority strategy of early red light off and whether to start the signal priority strategy of delayed green light. If the signal priority is started, it is 1, otherwise it is 0; μ k,j and are the vehicle flow, saturated flow, green-to-signal ratio and safe pedestrian crossing time of the bus non-priority phase of signal light j; Minimum green light duration to avoid oversaturation of non-priority bus phases; t0 is the vehicle startup loss time; is the length of the pedestrian crossing; V p is the walking speed of pedestrians, which is 1.5m / s; I k,j The green light interval is set to 5s.

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