Main line bus dynamic speed optimization method based on time headway equalization

Abstract

The invention discloses a trunk bus dynamic speed optimization method based on time headway equalization, and a dynamic optimization model is constructed by taking the minimum total deviation of the time headway on a bus line as a target. According to the method, factors such as departure time, driving speed and signalized intersection delay of intelligent networked buses are comprehensively considered, and the departure time of the buses at stations and the driving speed of the buses when the signalized intersection delay is not considered are optimized by establishing a calculation method for the bus delay at the signalized intersection. Meanwhile, according to a time headway balance principle, a calculation method based on the waiting time of passengers is provided so as to coordinate the operation efficiency of the motorcade and the experience of the passengers. According to the method, the fluctuation of the time headway is effectively reduced, the bunching effect is avoided, the overall operation efficiency and the service level of the bus route are remarkably improved, and theoretical support and technical guarantee are provided for design and implementation of an intelligent network connection bus system.

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 optimizing the dynamic speed of trunk buses based on headway balance. Background Art

[0002] With the acceleration of urbanization and the rapid growth of urban population, the pressure on urban transportation networks is increasing. Especially in large cities, as a key component that carries a large number of travel demands, the operating efficiency and service quality of the public transportation system have a direct impact on the overall operation of urban transportation. However, the current urban trunk bus system generally has problems such as uneven headway, unstable vehicle arrival time, and long passenger waiting time, resulting in a decline in service level and poor passenger experience. This situation stems from the lack of adaptability of the traditional scheduled departure mode to changes in complex traffic environment, which further aggravates the problems of low fleet operation efficiency and large headway fluctuations. In the framework of modern intelligent transportation systems, optimizing bus speeds to minimize headway deviations and improve the overall operating efficiency of bus lines has become a key issue that needs to be addressed. Currently, most studies focus on priority control or speed guidance at single intersections. Although these methods can reduce delays locally, the lack of continuity optimization of the entire line leads to a significant decrease in vehicle operating indicators at downstream intersections, making it difficult to significantly improve the operating efficiency of the overall transportation system. In addition, existing studies often focus on a single optimization objective (such as delay, energy consumption, number of stops, or travel time), which makes it difficult for the optimization results to balance multiple demands and achieve comprehensive optimization of the bus system. Headway is an important indicator that affects the efficiency of public transportation and the passenger experience. Its dynamic optimization research is still in the exploratory stage. By reasonably adjusting the vehicle speed to balance the headway, the headway fluctuation caused by traffic signals or passengers getting on and off the bus can be effectively reduced, and the vehicle spacing or "car stringing effect" can be avoided. However, the current research on headway control lacks a comprehensive optimization strategy within the entire line, and cannot improve the overall performance of public transportation from a systematic perspective. In recent years, the application of connected and autonomous vehicles (CAV) technology in improving the efficiency of public transportation has become increasingly widespread. Through the perception of real-time traffic data and bus operating status, combined with dynamic vehicle speed optimization algorithms, the headway time of the fleet can be finely adjusted. Some intelligent scheduling systems based on prediction models have initially achieved real-time prediction of traffic conditions and vehicle arrival times, but the core challenge of overall optimization across the entire line has not been effectively solved. Studies have shown that the deep integration of public buses with roadside sensing equipment and traffic signal control systems can reduce the headway time deviation through dynamic vehicle speed adjustment, while improving the capacity and service quality of bus lines. In order to solve the above problems, the present invention proposes a method for dynamic speed optimization of trunk buses based on headway balance. By constructing a dynamic optimization model with the goal of minimizing the total headway deviation, the speed of intelligent networked buses is systematically guided from the perspective of the entire line system. This method effectively avoids the local effects caused by the optimization of a single intersection, significantly reduces the headway fluctuations of the fleet, and takes into account multiple goals such as reducing passenger waiting time, reducing delays and optimizing the number of stops, thereby improving the operating efficiency and service level of the bus system. This research not only provides theoretical support for the sustainable development of urban transportation, but also lays a technical foundation for the design and implementation of future intelligent networked bus systems. Summary of the invention

[0003] Technical problem: Existing research on speed guidance of intelligent networked buses mainly focuses on single intersection optimization. Although it can reduce delays in a local area, due to the lack of continuity of the entire line, the operating indicators of vehicles may drop significantly after entering the downstream intersection, and it is difficult to significantly improve the operating efficiency of the overall transportation system. In addition, current research on the speed guidance problem of intelligent networked buses generally lacks a systematic optimization strategy from the perspective of the entire line, and fails to effectively reduce the fluctuation of headway time from the perspective of overall coordination of trunk line operation, resulting in insufficient stability of fleet operation and affecting the comprehensive service level of the bus system. In response to the above problems, the present invention aims to propose a dynamic speed optimization method for trunk buses based on headway balance. By minimizing the total headway deviation on the bus line as the goal, a dynamic speed optimization model is constructed to achieve overall control of vehicle operation on the entire line, thereby improving the overall operating efficiency of the bus line, improving passenger experience, and significantly enhancing the service quality of the public transportation system.

[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: The time difference between two buses leaving the same station minus the departure interval of the first station is the headway deviation. The closer the value is to 0, the smaller the headway deviation is. The larger the absolute value is, the larger the headway deviation is. The increase of headway deviation will lead to bus jam. Therefore, the optimization model is constructed with the minimum total headway deviation on the bus line as the objective function. The objective function is shown in formula (1): in, It represents the headway deviation between the j-1 car and the j car at station i, in seconds; is the speed of the intelligent networked bus j from the i-1th station to the i-th station, which is the decision variable; i is the number of the bus station, i=1,2,…,n; j is the vehicle number, j=1,2,…,m; n represents the number of bus stations on a route, and m represents the number of buses on the route; ω(·) represents the optimization function with the minimum deviation; The headway deviation in formula (1) Calculated by formula (2): In the formula, represents the departure time of car j at station i; represents the departure time of the j-1 train at station i; H j-1,j represents the departure interval between car j-1 and car j at the first station, in s, calculated by formula (3); represents the departure time of the jth vehicle at the first station; represents the departure time of the j-1th vehicle at the first station; The departure time of smart connected bus j at bus stop i+1 It is the departure time of station i Interval running time and station parking time The sum is calculated by formula (4): in, represents the departure time of car j at station i+1; represents the departure time of train j at station i; represents the running time of smart connected bus j between the i-th station and the i+1-th station, in seconds; represents the parking time of vehicle j at bus stop i+1.

[0006] Step 2, a method for calculating the departure time of the intelligent network-connected bus from the bus station, includes the following steps: Step 21: The parking time of vehicle j at bus stop i is Unit s, calculated by formula (5): in, represents the parking time of the jth bus at bus stop i, in seconds; It represents the average time taken by each passenger to get on and off the bus, in seconds; Y i j represents the number of people boarding bus j at bus stop i, in units of people; represents the number of passengers getting off bus j at bus stop i, in units of people; R j Indicates the number of doors of the bus, in units; In formula (5), the number of passengers getting off smart connected bus j at station i is Calculated by formula (6): in, is the number of people on the bus when bus j arrives at station i, in units of people; is the alighting rate of bus j at station i; In formula (5), the number of passengers Y on bus j at bus stop i is i j is the smaller value of the remaining capacity of bus j and the number of passengers that need to board the bus at this station during this time period. The number of passengers that need to board the bus at this station during this time period is the sum of the number of passengers that arrive in succession during this time period and the number of passengers who failed to board the previous bus, as shown in formula (7): Among them, Q j is the maximum capacity of the smart connected bus, per person; is the passenger arrival rate of bus j at station i; is the number of passengers who failed to take the next bus at station i, in person; In formula (7), the number of passengers who failed to take the bus at station i is Calculated by formula (8): In formula (7), the number of passengers on the bus is Calculated by formula (9): Step 22: If there is a signalized intersection between bus stops i and i+1, the delay of the signalized intersection should be considered when calculating the running time of the smart connected bus j from the i-th stop to the i+1-th stop, as shown in formula (10): Among them, L i,i+1 represents the distance between the i-th station and the i+1-th station, di is the average delay of vehicles in phase i, unit: s; represents the running time of smart connected bus j between the i-th station and the i+1-th station, in seconds; is the travel speed of bus j without considering the delay time at the signalized intersection, in m / s.

[0007] Step 3, a method for calculating bus delays at signalized intersections, comprises the following steps: Step 31: Delay of buses at signalized intersections d i Calculated by formulas (11)-(18): Among them, d i is the average delay of vehicles in phase i, in seconds; C is the signal period, in seconds; λ i is the green-to-signal ratio of phase i; x i is the saturation of phase i; P i is the proportion of vehicles arriving at phase i within the green light time; f i is the correction factor for the arrival of the queue at phase i during the green light period; K is the delay correction factor for the signal control type; Q i is the number of initial queue vehicles in phase i of the analysis period, in units of pcu; t i is the time during the analysis period when phase i cannot meet the traffic demand, in h; u i is the delay parameter of phase i; c i is the traffic capacity of phase i, in units of pcu / h; T is the green light time in the signal cycle; the saturation index I of phase i i Calculated by formula (12): Where: X i The saturation of all upstream contributing vehicles in phase i is weighted by the flow rate; The time t during the analysis period that phase i cannot meet traffic demand i Calculated by the formula: The delay parameter u of phase i i Calculated by the formula: Green-to-signal ratio λ of phase i i , calculated by the formula: The effective green light duration g of phase i i , calculated by the formula: g i =G i -ls +e (16) Where: G i is the actual display duration of the green light in phase i, in seconds; l s is the pre-loss time, in seconds; e is the post-compensation time, in seconds; c i =S i λ i (18) q i is the traffic volume in phase i, in pcu / h; S i is the saturation flow rate of phase i, in pcu / h.

[0008] Step 4: The bus travel speed without considering the delay time at the signalized intersection and the departure time of the bus at the bus stop meet the constraints, including the following steps: Step 41: Limit the speed within the range allowed by the road section, as shown in formula (19): Among them, V min and V max Respectively represent the minimum speed and maximum speed allowed for the road section, in m / s; Step 42: The intelligent network-connected buses are not allowed to overtake and travel in order, satisfying the constraint of formula (20): Step 5: Calculate the passenger waiting time based on the headway, as shown in formula (21): Among them, E(W) is the average waiting time of passengers, in minutes; E(H) is the mean headway time; Represents the standard deviation coefficient of headway.

[0009] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0010] In the trunk bus operation environment, this paper aims at the speed guidance problem of intelligent networked buses. Instead of directly adopting the traditional multi-objective optimization model, it innovatively proposes a method that can achieve multiple optimization goals at the same time by controlling the headway deviation between intelligent networked buses. This method simplifies the complexity of the speed guidance problem in theory, and verifies its effectiveness through case analysis. The results show that this method can not only effectively maintain a reasonable headway, but also significantly reduce the waiting time of passengers at bus stops and shorten the overall travel time, reflecting good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a flow chart of the method of the present invention;

[0012] Figure 2 This is a schematic diagram of the speed guidance of trunk intelligent networked buses;

[0013] Figure 3 This is a schematic diagram of the change of the headway deviation of buses without speed guidance;

[0014] Figure 4 Schematic diagram of the change of headway deviation of buses under speed guidance. DETAILED DESCRIPTION

[0015] In conjunction with the accompanying drawings and embodiments, the technical solution of the present invention is described in detail as follows:

[0016] Figure 3 The diagram below shows the change of headway deviation of buses without speed guidance. The horizontal axis represents the number of bus stops, and the vertical axis represents the headway deviation value of buses. It is not difficult to see from the change curve that the headway deviation value of buses increases with the change of driving distance after the bus leaves the starting station, which is called the "long tail effect". This situation will lead to the phenomenon of buses being strung together, and passengers will pile up in large numbers at the platform, increasing the waiting time for passengers. Take a bus route with 20 bus stops, 8 signalized intersections and a total length of 6.8 kilometers as an example. The departure interval is 8 minutes. The bus operation section is a two-way four-lane road, and the bus drives in the right lane. The maximum and minimum speeds allowed on the road section are 40km / h and 20km / h respectively. The signal timing scheme for intersections on the collected information bus routes is shown in Table 1: Table 1 Information on signalized intersections where buses pass The distance between 20 bus stops is shown in Table 2: Table 2 Spacing between 20 bus stops Through the dynamic speed optimization method for trunk buses proposed in the present invention, after the vehicle departs from the first station, as the travel distance increases, the deviation of the headway time shows a trend of gradually increasing. However, during the entire operation process, the headway time deviation is always controlled within a small range, about 70 seconds when it reaches the terminal, which is significantly smaller than the headway time deviation when speed guidance is not implemented, and the fluctuation range is small. This shows that reasonable dynamic speed guidance for intelligent networked buses can effectively reduce the deviation of headway time, thereby improving the stability of operation and the service level of the line. According to step 5, the average waiting time of passengers at each station when the headway deviation is not controlled and the average waiting time of passengers at each station after the headway deviation is controlled are calculated, as shown in Table 3: Table 3 Changes in passenger waiting time before and after speed guidance Before the vehicle speed was controlled, the maximum waiting time for passengers was 5.66 minutes, far exceeding the 4 minutes of departure interval. By controlling the vehicle speed, the maximum waiting time for passengers at each station along the bus line was 4.19 minutes, a decrease of 26%. Looking at the entire line, the waiting time of passengers at each station was relatively uniform with little variation.

[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 optimizing the speed of trunk buses based on headway deviation, characterized in that: The method comprises the following steps: Step 1: The time difference between two buses leaving the same station minus the departure interval of the first station is the headway deviation. The closer the value is to 0, the smaller the headway deviation is. The larger the absolute value is, the larger the headway deviation is. The increase of headway deviation will lead to bus jam. Therefore, the optimization model is constructed with the minimum total headway deviation on the bus line as the objective function. The objective function is shown in formula (1): in, It represents the headway time deviation between the -1 car and the,car at station i, in s; is the speed of the intelligent networked bus from the i-1th station to the i-th station, which is the decision variable; i is the number of the bus station, i=1, 2, ..., n; j is the vehicle number, j=1, 2, ..., m; n is the number of bus stations on a route, and m is the number of buses on the route; ω(·) represents the optimization function with the minimum deviation; The headway deviation in formula (1) Calculated by formula (2): In the formula, Indicates the departure time of the car at station i; Indicates the departure time of car -1 at station i; H j-1，j It represents the departure interval between car -1 and car j at the first station, in units of s, calculated by formula (3); represents the departure time of the jth vehicle at the first station; represents the departure time of the j-1th vehicle at the first station; Smart connected buses, departure time at i+1 bus stops It is the departure time of station i Interval running time and station parking time The sum is calculated by formula (4): in, Indicates the departure time of the car at station i+1; Indicates the departure time of the bus at station i; It represents the running time of the intelligent network-connected bus between the i-th station and the i+1-th station, in seconds; Indicates the parking time of the vehicle at bus stop i+1.

2. The method for optimizing the speed of trunk buses based on headway deviation according to claim 1, characterized in that: The method for calculating the departure time of the intelligent network-connected bus from the bus station in step 1 includes the following steps: Step 21: The vehicle stops at the bus stop i for a certain period of time. Unit s, calculated by formula (5): in, represents the parking time of the ,th bus at bus stop i, in seconds; It represents the average time taken by each passenger to get on and off the bus, in seconds; represents the number of people boarding the bus at bus stop i, in units of people; represents the number of passengers getting off bus j at bus stop i, in units of people; R j Indicates the number of doors of the bus, in units; In formula (5), the number of people getting off at station i for a smart connected bus is Calculated by formula (6): in, is the number of people on the bus when it arrives at station i, in units of people; is the bus,get-off rate at station i; In formula (5), the number of people boarding a bus at bus stop i is is the smaller value of the remaining capacity of bus j and the number of passengers that need to board the bus at this station during this time period. The number of passengers that need to board the bus at this station during this time period is the sum of the number of passengers that arrive in succession during this time period and the number of passengers who failed to board the previous bus, as shown in formula (7): Among them, Q j is the maximum capacity of the smart connected bus, per person; is the passenger arrival rate of bus at stop i; is the number of passengers who failed to take the next bus at station i, in units of persons; In formula (7), the number of passengers who failed to take the bus at station i is Calculated by formula (8): In formula (7), the number of passengers on the bus is Calculated by formula (9): Step 22: If there is a signalized intersection between bus stops i and i+1, the delay of the signalized intersection should be considered when calculating the running time of the smart connected bus j from the i-th stop to the i+1-th stop, as shown in formula (10): Among them, L i，i+1 represents the distance between the i-th station and the i+1-th station, d i is the average delay of vehicles in phase i, unit s; represents the running time of smart connected bus j between the i-th station and the i+1-th station, in seconds; is the travel speed of bus j without considering the delay time at the signalized intersection, in m / s.

3. The method for optimizing the speed of trunk buses based on headway deviation according to claim 2, characterized in that: The method for calculating the average delay of buses at the signalized intersection in step 2 comprises the following steps: Step 31: Delays of buses at signalized intersections i Calculated by formulas (11)-(18): Among them, d i is the average delay of vehicles in phase i, in seconds; C is the signal period, in seconds; λ i is the green-to-signal ratio of phase i; x i is the saturation of phase i; P i is the proportion of vehicles arriving at phase i within the green light time; f i is the correction factor for the arrival of the queue at phase i during the green light period; K is the delay correction factor for the signal control type; Q i is the number of initial queue vehicles in phase i of the analysis period, in units of pcu; t i is the time during the analysis period when phase i cannot meet the traffic demand, in h; u i is the delay parameter of phase i; c i is the traffic capacity of phase i, in units of pcu / h; T is the green light time in the signal cycle; Saturation index I of phase i i Calculated by formula (12): Where: X i The saturation of all upstream contributing vehicles in phase i is weighted by the flow rate; The time t during the analysis period that phase i cannot meet traffic demand i Calculated by formula (13): The delay parameter u of phase i i Calculated by formula (14): Green-to-signal ratio λ of phase i i , calculated by formula (15): The effective green light duration g of phase i i , calculated by formula (16): g i =G i -l s +e (16) Where: G i is the actual display duration of the green light in phase i, in seconds; l s is the pre-loss time, in seconds; e is the post-compensation time, in seconds; c i =S i l i (18) q i is the traffic volume in phase i, in units of pcu / h; S i is the saturation flow rate of phase i, in pcu / h.

4. The method for optimizing the speed of trunk buses based on headway deviation according to claim 2, characterized in that: In step 2, the driving speed of the bus without considering the delay time at the signalized intersection and the departure time of the bus at the bus stop meet the constraint conditions, including the following steps: Step 41: Limit the speed within the range allowed by the road section, as shown in formula (19): Among them, V min and V max Respectively represent the minimum speed and maximum speed allowed for the road section, in m / s; Step 42: The intelligent network-connected buses are not allowed to overtake and travel in order, satisfying the constraint of formula (20): Step 5: Calculate the passenger waiting time based on the headway, as shown in formula (21): Among them, E(W) is the average waiting time of passengers, in minutes; E(H) is the mean headway time; Represents the standard deviation coefficient of headway.

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