Trunk line intelligent network connection bus speed and signal timing collaborative optimization method

Abstract

The invention discloses a trunk line intelligent network connection bus speed and signal timing collaborative optimization method, which comprises the following steps: acquiring basic information of bus lines, such as departure time interval, line length, station distribution and intersection signal phase; an intelligent networked bus speed control model is constructed, and dynamic speed guidance is implemented for different deviation types by calculating the deviation of the time headways of adjacent vehicles in real time; based on a vehicle speed control result, intersection signal lamp timing is optimized, and the negative influence of traffic signals on the driving speed of the public transport vehicle is reduced. By implementing accurate speed and signal lamp cooperative control at each bus station and intersection, the method can improve the overall operation efficiency of the trunk bus system to the maximum extent and reduce intersection delay while ensuring that the buses arrive on time, and compared with a traditional whole-line adjustment strategy, the method has the advantages that the overall operation efficiency of the trunk bus system is improved to the maximum extent, and the cost is reduced. According to the control strategy, the problem that the time headway deviation of a local road section is large can be solved more accurately.

Description

Technical Field

[0001] The present invention belongs to the field of traffic engineering and traffic information and control systems, and relates to the technical field of intelligent traffic systems. More specifically, it relates to a method for coordinated optimization of speed and signal timing of trunk intelligent networked buses. Background Art

[0002] With the rapid development of connected vehicles (CAVs) and autonomous driving technologies, public transportation systems, especially trunk bus systems, are facing unprecedented optimization opportunities. In traditional bus systems, the operation of buses is often limited by fixed traffic signal timing and non-intelligent scheduling mechanisms, resulting in frequent stops at traffic lights, road congestion, and parking operations at bus stops, affecting overall operating efficiency and punctuality. Especially in complex urban trunk environments, signal control at intersections and parking operations at bus stops have become key factors affecting the stability of bus headway and operational efficiency. In recent years, both academia and industry have been exploring ways to optimize the coordination mechanism between traffic signal timing and vehicle control by introducing intelligent networking and autonomous driving technologies, especially in improving the efficiency of public transportation systems and reducing traffic delays. Traditional traffic management methods mostly focus on static signal control and vehicle speed regulation, lacking real-time response to traffic flow and vehicle behavior. With the application of intelligent networking technology, scholars have begun to focus on how to achieve real-time adaptation of vehicles and traffic signals through dynamic collaborative control, thereby effectively improving the overall smoothness and energy-saving effects of the transportation system. For example, Guler et al. proposed a collaborative control method for traffic signals and vehicle formations based on an intelligent network environment, and studied the collaborative scheduling mechanism between vehicles and traffic lights. Similarly, Li et al. designed a simplified collaborative control algorithm for vehicle speed and traffic lights. With the gradual application of intelligent networked buses, more research has begun to focus on the collaborative optimization problem in bus systems. Yang et al. proposed an intersection control algorithm based on a mixed environment (including traditional vehicles, networked vehicles, and autonomous driving vehicles). Studies have shown that when the proportion of intelligent networked vehicles at intersections reaches about 50%, collaborative control can minimize traffic delays. Niroumand et al. adopted a model predictive control (MPC) method, combined with a linear vehicle following model, to achieve collaborative optimization of traffic signals and vehicle trajectories. Feng et al. proposed a collaborative control method with the goal of minimizing average delay and fuel consumption through a two-layer optimization model, and proposed an efficient solution algorithm. Based on the model of mixed integer linear programming, Yu et al. proposed a strategy for comprehensive optimization of signal timing, lane-changing behavior and vehicle arrival time, simulating a scenario with 100% autonomous vehicle penetration, showing the potential of this method in reducing traffic delays and optimizing bus flow. Guo et al. proposed a cooperative control algorithm under mixed traffic conditions that combines heuristic shooting and dynamic programming. On this basis, Jiang et al. designed an integrated control framework that combines traffic signal optimization with vehicle micro-control, aiming to reduce fuel consumption and improve the smoothness of traffic flow by optimizing signal timing and vehicle driving trajectory. Although these studies provide an important theoretical basis for the coordinated optimization of traffic signals and vehicle control, they still face many challenges in complex trunk bus systems. Especially in complex environments with multiple intersections and multiple stations, how to balance the timing of traffic lights with the running speed of buses through precise real-time control to avoid fluctuations in headway due to waiting at red lights and stopping at stations is still an urgent problem to be solved. Specifically, when a bus stops and waits at an intersection, the headway of subsequent vehicles may be drastically shortened due to the influence of the previous vehicle, or even a series of vehicles may occur, thereby affecting the parking time of subsequent buses and ultimately leading to inefficiency of the entire bus line. Therefore, the present invention proposes a coordinated optimization method of vehicle speed and signal timing based on intelligent networked buses, which aims to dynamically adjust the travel speed of the bus by calculating the parking operation time of the bus at the station and the traffic delay at the intersection, combined with the change of the headway, and optimize accordingly according to the signal timing of the intersection. Through this method, while ensuring a reasonable headway, the operating efficiency of the bus can be maximized, and the headway deviation can be avoided. Excessive deviations can be avoided, thereby effectively solving the problem of stringing caused by signal lights and parking operations, and ultimately improving the overall stability and operational efficiency of the trunk bus system. This method can not only ensure the on-time arrival of the bus, but also improve the resource utilization of the bus system, reduce traffic congestion, and lay a theoretical foundation and technical support for the widespread application of intelligent networked buses. Summary of the invention

[0003] Technical problem: Traditional intelligent networked bus systems usually focus on signal optimization and speed control at a single intersection. Although they can achieve certain results in local sections, they fail to comprehensively consider the coordination between multiple intersections and bus stops along the entire line, resulting in large fluctuations in the headway time of buses between different sections, affecting the punctuality and operation efficiency of the vehicles. In response to the above problems, the present invention aims to propose a method for coordinated optimization of the speed and signal timing of trunk intelligent networked buses, aiming to achieve continuous stability of the headway time and maximization of bus operation efficiency across the entire line by dynamically adjusting the signal timing and bus speed of each stop and intersection.

[0004] Technical solution: To solve the above technical problems, the present invention provides a trunk bus dynamic speed optimization method based on headway balance, comprising the following steps:

[0005] Step 1: Collect information about bus routes, including departure time interval, route length, represented by L, number of bus stops along the route, represented by M, distance between adjacent stops, and distance between the i-th stop and the i+1-th stop, represented by L i,i+1 Indicates that the intelligent networked buses operating in the same direction on the entire line are represented by S, and vehicle j+1 is always behind vehicle j, and there will be no overtaking. The signal intersections on the bus line are represented by p, p = 1, 2, ..., P, P represents the total number of signal intersections on the line; collect signal phase information at signal intersections; Step 2: Construct a speed control model for intelligent networked buses, including calculating the headway time between adjacent vehicles, and dividing the model into three types for speed guidance based on the headway time deviation between the current vehicle and the vehicles in front and behind; Step 3: Based on the road speed guidance, optimize the intersection signal timing to reduce the impact of signal intersections on the stability of bus headway time; determine whether to increase the green light travel time based on the comprehensive intersection delay index, and calculate based on the reduction in vehicle delays in the bus direction caused by the extension of the green light and the early closure of the red light and the increase in vehicle delays in other directions, including the calculation of the delay reduction, the delay increase, and the construction of signal light adjustment principles.

[0006] In step 2, the speed control model of the intelligent connected bus is constructed, which includes the following steps: Step 21, calculation of the headway between adjacent vehicles; the time consumed by the intelligent network-connected bus j from the i-th station to the i+1-th station is the travel time and the cumulative delay time of all intersections between the two stations, expressed as It is represented by formula (1); Where: L i,i+1 Represents the distance between bus stop i and bus stop i+1, in meters; represents the running speed of bus j between bus stop i and bus stop i+1, in meters. This speed is the actual running speed and does not include the stopping time of the vehicle at the intersection; d j,p represents the delay time of smart connected bus j at intersection p, in seconds; I i,i+1 represents the number of intersections between bus stop i and bus stop i+1, in units, and satisfies I i,i+1 ≤P; When there are multiple signalized intersections between two bus stops, the time it takes for the vehicle to arrive at the next intersection is calculated based on the departure time of the smart connected bus at the current intersection, as shown in formula (2): Where: represents the time when the smart connected bus j arrives at the intersection p, and a represents arrival; represents the time when the smart connected bus j leaves the intersection p-1, and d represents the departure; L p-1,p It represents the distance between intersection p-1 and intersection p, taking the distance between the stop lines of the two intersections as the benchmark, in meters; The time it takes for smart connected bus j to arrive at intersection p Calculated by formula (3): Where: It indicates the remaining red light time of the signal light of this phase when the smart connected bus j arrives at the intersection p, in seconds; The time difference between the time when the smart connected bus j leaves the intersection and the time when it arrives at the intersection is the delay of the vehicle at intersection p. When the bus arrives at the intersection and the light is green, it can pass directly and the delay is 0; if the bus arrives at the intersection within the red light time, the delay can be regarded as the remaining time of the red light, and the delay is d. j,p Calculated by formula (4): The time when smart connected bus j leaves bus stop i is the time when vehicle arrives at i The parking time of the vehicle at the station The sum of is shown in formula (5): Where: represents the time when smart connected bus j leaves bus stop i; represents the time when smart connected bus j arrives at bus stop i; Describe the parking time of bus j at bus stop i; The time when smart connected bus j arrives at bus stop i Calculated by formula (6): The arrival time of the bus departure station can be directly based on the departure time of the first station in the timetable; When the departure time of smart connected buses j-1 and j at bus stop i is known, the headway difference between the two vehicles at i can be calculated using formula (7): Where: is the headway difference between smart connected buses j-1 and j at bus stop i, in seconds; represents the time when the smart connected bus j-1 leaves the bus stop i; Step 22, implement speed guidance for the bus; the headway deviation is the difference between the headway and the departure interval between the vehicle and the preceding or following vehicle, calculated by formula (8): Where: It represents the headway deviation between smart connected buses j and j+1 at station i, in seconds; Hj-1,j It represents the departure interval between smart connected buses j and j+1, in seconds; Based on the headway deviation between the current vehicle and the vehicles ahead and behind, the speed control of the intelligent connected bus can be divided into three control strategies: The deceleration strategy is that at bus stop i, the headway deviation between smart connected buses j and j+1 is greater than 0, and greater than the headway deviation between buses j-1 and j, that is, and At this time, the vehicle's running speed in the next section should be reduced to reduce the headway between vehicle j and the following vehicle. The speed can be determined by formula (9); Where: represents the running speed of bus j between bus stop i-1 and bus stop i, in meters; It represents the headway deviation between smart connected buses j-1 and j at station i, in seconds; Acceleration strategy: At bus stop i, the headway deviation between smart connected buses j-1 and j is greater than 0, and greater than the headway deviation between vehicles j and j+1. and At this time, the vehicle's running speed in the next section should be increased to reduce the headway between vehicle j and the preceding vehicle. The speed value can be calculated by formula (10): The maximum speed operation strategy, if the headway deviations between vehicle j and the vehicles ahead and behind are both 0, the operating speed of the intelligent networked bus in the section can be increased to the maximum speed to improve the operation efficiency of the bus, that is: Where: V max Indicates the maximum speed the vehicle is allowed to travel.

[0007] In step 3, based on the road section speed guidance, the intersection signal timing is optimized, including the following steps: Step 31: Calculate the delay reduction; the extension of the green light will reduce the delay of the smart connected bus and the delay of social vehicles traveling in the same phase as the smart connected bus; the delay reduced by the bus is the remaining red light time when the bus reaches the stop line of the intersection The total delay reduced by extending the green light is the sum of the delay reduced by public transportation and the delay reduced by private vehicles, calculated by formulas (12)-(13): Where: represents the reduced delay of smart connected bus j arriving at intersection p, unit s; It indicates the remaining red light time of the signal light of this phase when the smart connected bus j arrives at the intersection p, in seconds; Δd xy is the delay of social vehicles reduced in the y import lanes in the x direction, the unit is s, and Y represents the total number of import lanes; r x is the red light time in the x direction, in seconds; q xy is the vehicle arrival rate at the y entrance lane in the x direction, in pcu / s; S xy is the vehicle saturation rate of the y entrance lane in the x direction, in units of pcu / h; is the green light extension time or red light early off time when the smart connected bus j arrives at the intersection p, in seconds, calculated by formula (14): Where: L bus Indicates the distance from the bus to the stop line of the intersection; Step 32: Calculate the increase in delay. Although the extension of the green light will reduce the vehicle delay in the direction of the bus operation, it will increase the delay in other directions. Calculated by formula (15)-(16): Where: It indicates the intersection delay caused by the extension of green light or early breaking of red light, in seconds; X represents the number of phases affected by the intersection signal adjustment; Y x Indicates the number of lanes passing in the xth phase; Step 33: Construct a traffic light adjustment principle; compare the delay reduced by the traffic light timing adjustment with the delay increased, and determine whether to turn off the red light early and extend the green light. The following are the judgment criteria: when When the green light time is extended or the red light time is shortened; when At this time, the signal timing is not adjusted and the smart connected bus stops before the stop line at the intersection.

[0008] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0009] Unlike the traditional method of implementing coordinated optimization of vehicle speed guidance and signal timing for only a single intersection, the present invention can continuously adjust the vehicle speed and signal timing across the entire line, thereby avoiding the situation where the headway deviation is too large in local sections, and can accurately control the interaction between vehicles and traffic lights according to real-time traffic conditions, thereby achieving efficient and stable operation of buses. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a flow chart of the method of the present invention;

[0011] Figure 2 This is a schematic diagram of the driving trajectory of the trunk intelligent network bus;

[0012] Figure 3 This is a schematic diagram of the operation of intelligent connected public transportation;

[0013] Figure 4 It is the spatiotemporal trajectory diagram of the bus without optimization control measures;

[0014] Figure 5 This is the bus operation trajectory diagram under speed and signal control. DETAILED DESCRIPTION

[0015] In conjunction with the accompanying drawings and embodiments, the technical solution of the present invention is described in detail as follows: Figure 2 Figure 1 is a schematic diagram of the driving trajectory of the trunk intelligent network bus. The intelligent network bus ah starts from the origin at an equal departure interval and runs at the same speed, keeping the initial headway unchanged. Since the current phase signal lights of a, c, and f vehicles are all red when they arrive at the intersection stop line, the vehicles must stop here and wait for the green light to turn on. However, the following vehicles b, d, and g still maintain their original speed, resulting in the shortened headway at the first intersection. The two groups of vehicles c and d, and f and g even have a series of vehicles here. After the vehicle exits the intersection and stops at the bus stop, the headway of the vehicle is further shortened, and the time for the next bus to arrive at the bus stop will appear to be the time for the next bus to arrive at the bus stop immediately after the previous bus. Since the previous bus accommodates most of the waiting passengers at this station, the stop time of the next bus is too short, and this effect will form a conduction effect on the entire bus line. For example, c, d, and e in the figure form a three-vehicle series at the second intersection. The emergence of this situation is an important reason for the low efficiency of bus operation. Figure 4This is a time-space trajectory diagram of buses without optimization control measures. Each curve represents the time-space trajectory of a bus. It can be seen from the figure that the bus departs from the first station with a departure interval of 4 minutes. Due to the difference in speed, the length of the stop time at the intersection, and the influence of the signal intersection, it is difficult for the bus to continue to maintain a 4-minute headway after running a certain distance. For example, the first and second buses in the figure maintain a very small headway in the late stage of operation, which leads to a larger headway with the third bus. Therefore, the instability of the headway will be transmitted to the following bus, affecting the quasi-efficiency of the bus operation on the entire line. For example, the third and fourth buses have a serious phenomenon of vehicles running in the middle and late stages.

[0016] According to step 1, a bus line is selected as the research object, and the bus line information is collected, including the total length of the line L = 10 kilometers, a total of 26 bus stops along the entire route, M = 26, and a departure interval of 4 minutes during the peak period of 11:30-12:30. There are 6 signalized intersections along the entire route, P = 6, and all other intersections along the route are non-signalized intersections. The distance between two adjacent bus stops is measured. As shown in Table 1, the bus stops on the line are set relatively closely, with an average station spacing of 400m. Table 1 Bus stop spacing According to step 1, the signal phase information of the signalized intersection is collected, as shown in Table 2. The maximum speed allowed for the road section is 40 km / h, and the minimum speed is 20 km / h. Table 2 Information on signalized intersections where buses pass According to step 2 and step 3, the bus speed and signal intersection timing scheme are optimized in a coordinated manner. The stability of the headway time of the vehicles is greatly improved. Taking the 8 vehicles studied as an example, there is no large headway time deviation or vehicle jamming phenomenon. The bus's full travel time is shortened and the bus's operating efficiency is improved. The comparison results are shown in Table 3 using travel time, travel speed, delay, etc. as indicators for analysis: Table 3 Analysis of key indicators before and after speed control As shown in Table 3, the average travel time of the eight vehicles studied was reduced by 192 seconds after the control, a decrease of 11.9%, the average driving speed increased by 14.8%, and the average delay at the intersection was greatly reduced, a decrease of 80.1%. Therefore, the coordinated control of the speed and signal timing of intelligent networked buses can effectively improve the operating efficiency of buses on trunk lines and reduce vehicle delays.

[0017] The present invention is not limited to this single example; any other changes, modifications, substitutions, combinations, simplifications that deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for coordinated optimization of speed and signal timing of trunk intelligent networked buses, characterized in that: The method comprises the following steps: Step 1: Collect information about bus routes, including departure time interval, route length, represented by L, number of bus stops along the route, represented by M, distance between adjacent stops, and distance between the i-th stop and the i+1-th stop, represented by L i,i+1 Indicates that the intelligent networked buses operating in the same direction on the entire line are represented by S, and vehicle j+1 is always behind vehicle j, and there will be no overtaking. The signal intersections on the bus line are represented by p, p = 1, 2, ..., P, P represents the total number of signal intersections on the line; collect signal phase information at signal intersections; Step 2: Construct a speed control model for intelligent networked buses, including calculating the headway time between adjacent vehicles, and dividing the model into three types for speed guidance based on the headway time deviation between the current vehicle and the vehicles in front and behind; Step 3: Based on the road speed guidance, optimize the intersection signal light timing to reduce the impact of signal intersections on the stability of bus headway; The comprehensive delay index of intersections is used to determine whether to increase the green light travel time. The calculation is based on the reduction in vehicle delays in the bus direction caused by the extension of the green light and the early closure of the red light, and the increase in vehicle delays in other directions, including the calculation of the reduction in delays, the increase in delays, and the construction of signal light adjustment principles.

2. The method for coordinated optimization of speed and signal timing of trunk intelligent networked buses according to claim 1, characterized in that: The step 2 of constructing the speed control model of the intelligent networked bus includes the following steps: Step 21, calculation of the headway between adjacent vehicles; the time consumed by the intelligent network-connected bus j from the i-th station to the i+1-th station is the travel time and the cumulative delay time of all intersections between the two stations, expressed as It is represented by formula (1); Where: L i,i+1 Represents the distance between bus stop i and bus stop i+1, in meters; represents the running speed of bus j between bus stop i and bus stop i+1, in meters. This speed is the actual running speed and does not include the stopping time of the vehicle at the intersection; d j,p represents the delay time of smart connected bus j at intersection p, in seconds; I i,i+1 represents the number of intersections between bus stop i and bus stop i+1, in units, and satisfies I i,i+1 ≤P; When there are multiple signalized intersections between two bus stops, the time it takes for the vehicle to arrive at the next intersection is calculated based on the departure time of the smart connected bus at the current intersection, as shown in formula (2): Where: represents the time when the smart connected bus j arrives at the intersection p, and a represents arrival; represents the time when the smart connected bus j leaves the intersection p-1, and d represents the departure; L p-1,p It represents the distance between intersection p-1 and intersection p, taking the distance between the stop lines of the two intersections as the benchmark, in meters; The time it takes for smart connected bus j to arrive at intersection p Calculated by formula (3): Where: It indicates the remaining red light time of the signal light of this phase when the smart connected bus j arrives at the intersection p, in seconds; The time difference between the time when the smart connected bus j leaves the intersection and the time when it arrives at the intersection is the delay of the vehicle at intersection p. When the bus arrives at the intersection and the light is green, it can pass directly and the delay is 0; if the bus arrives at the intersection within the red light time, the delay can be regarded as the remaining time of the red light, and the delay is d. j,p Calculated by formula (4): The time when smart connected bus j leaves bus stop i is the time when vehicle arrives at i The parking time of the vehicle at the station The sum of is shown in formula (5): Where: represents the time when smart connected bus j leaves bus stop i; represents the time when smart connected bus j arrives at bus stop i; Describe the parking time of bus j at bus stop i; The time when smart connected bus j arrives at bus stop i Calculated by formula (6): The arrival time of the bus departure station can be directly adopted from the departure time of the first station in the timetable; When the departure time of smart connected buses j-1 and j at bus stop i is known, the headway difference between the two vehicles at i can be calculated using formula (7): Where: is the headway difference between smart connected buses j-1 and j at bus stop i, in seconds; represents the time when the smart connected bus j-1 leaves the bus stop i; Step 22, implement speed guidance for the bus; the headway deviation is the difference between the headway and the departure interval between the vehicle and the preceding or following vehicle, calculated by formula (8): Where: It represents the headway deviation between smart connected buses j and j+1 at station i, in seconds; H j-1,j It represents the departure interval between smart connected buses j and j+1, in seconds; Based on the headway deviation between the current vehicle and the vehicles ahead and behind, the speed control of the intelligent connected bus can be divided into three control strategies: The deceleration strategy is that at bus stop i, the headway deviation between smart connected buses j and j+1 is greater than 0, and greater than the headway deviation between buses j-1 and j, that is, and At this time, the vehicle's running speed in the next section should be reduced to reduce the headway between vehicle j and the following vehicle. The speed can be determined by formula (9); Where: represents the running speed of bus j between bus stop i-1 and bus stop i, in meters; It represents the headway deviation between smart connected buses j-1 and j at station i, in seconds; Acceleration strategy: At bus stop i, the headway deviation between smart connected buses j-1 and j is greater than 0, and greater than the headway deviation between vehicles j and j+1. and At this time, the vehicle's running speed in the next section should be increased to reduce the headway between vehicle j and the preceding vehicle. The speed value can be calculated by formula (10): The maximum speed operation strategy, if the headway deviations between vehicle j and the vehicles ahead and behind are both 0, the operating speed of the intelligent networked bus in the section can be increased to the maximum speed to improve the operation efficiency of the bus, that is: Where: V max Indicates the maximum speed the vehicle is allowed to travel.

3. The method for coordinated optimization of speed and signal timing of trunk intelligent networked buses according to claim 1, characterized in that: In step 3, based on the road section speed guidance, the intersection signal light timing is optimized, including the following steps: Step 31: Calculate the delay reduction; the extension of the green light will reduce the delay of the smart connected bus and the delay of social vehicles traveling in the same phase as the smart connected bus; the delay reduced by the bus is the remaining red light time when the bus reaches the stop line of the intersection The total delay reduced by extending the green light is the sum of the delay reduced by public transportation and the delay reduced by private vehicles, calculated by formulas (12)-(13): Where: represents the reduced delay of smart connected bus j arriving at intersection p, unit s; It indicates the remaining red light time of the traffic light of this phase when the smart connected bus j arrives at the intersection p, in seconds; Δd xy is the delay of social vehicles reduced in the y import lanes in the x direction, the unit is s, and Y represents the total number of import lanes; r x is the red light time in the x direction, in seconds; q xy is the vehicle arrival rate at the y entrance lane in the x direction, in pcu / s; S xy is the vehicle saturation rate of the y entrance lane in the x direction, in units of pcu / h; is the green light extension time or red light early off time when the smart connected bus j arrives at the intersection p, in seconds, calculated by formula (14): Where: L bus Indicates the distance from the bus to the stop line of the intersection; Step 32: Calculate the increase in delay. Although the extension of the green light will reduce the vehicle delay in the direction of the bus operation, it will increase the delay in other directions. Calculated by formula (15)-(16): Where: It indicates the intersection delay caused by the extension of green light or early breaking of red light, in seconds; X represents the number of phases affected by the intersection signal adjustment; Y x Indicates the number of lanes passing in the xth phase; Step 33: Construct a traffic light adjustment principle; compare the delay reduced by the traffic light timing adjustment with the delay increased, and determine whether to turn off the red light early and extend the green light. The following are the judgment criteria: when When the green light time is extended or the red light time is shortened; when At this time, the signal timing is not adjusted and the smart connected bus stops before the stop line at the intersection.

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