Dynamic Bus Scheduling of Multiple Routes Based on Joint Optimization of Departure Time and Speed

Liu, Yingxin; Luo, Xinggang; Cheng, Shengping; Yu, Yang; Tang, Jiafu

doi:10.1155/2021/4213837

Cited by 6 publications

(1 citation statement)

References 62 publications

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“…Targeting the relationship among bus routes, Barabino et al [18] proposed an offline framework to monitor the reliability of transfers at all the bus stops and time periods by analysing automatic vehicle location data. Liu et al [19] constructed a joint optimization model of the bus departure time and speed for multiple routes with several shared stops to avoid bus bunching and reduce passenger wait time by approximately 30%. To simplify the transfer coordination model among bus routes, transfer passengers' wait time is minimized by adjusting bus departure interval and speed [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Route Assignment at the Bus Hub with Multiple Berths for Minimal Passenger Transfer Distance

Zhang

Jin

Hao

et al. 2022

Journal of Mathematics

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Bus hub is characterized with multiple berths, each serving specific bus routes. When bus routes at different berths share the same stops en route, passengers to the shared stops may rush among these berths to catch the newly arriving bus, causing inconvenience and deteriorating bus service level. Thus, this research attempts to optimize bus route assignment to the berths at a bus hub to minimize passenger transfer distance among the optional berths. The problem is modeled with integer linear programming, with the constraints of bus service capacity. To enhance computation efficiency, exclusive and inclusive constraints based on the count of shared stops among the bus routes are additionally added to the original model, though they may bring gap from the optimal solution. Case study follows to apply the proposed set of models to a bus terminal, where a total of 26 routes are assigned to 11 berths. It is found that the models with additional constraints can greatly reduce calculation time at the cost of small or no gap from the optimal solution. Thus, the models with additional constraints are recommended to obtain ideal solution quickly. This research provides insights into bus route management at the bus terminal with multiple berths, contributing to enhanced passenger waiting experience.

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Section: Introductionmentioning

confidence: 99%

Optimal Route Assignment at the Bus Hub with Multiple Berths for Minimal Passenger Transfer Distance

Zhang

Jin

Hao

et al. 2022

Journal of Mathematics

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Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment

Yang,

Jin,

Yao

et al. 2024

Computer aided Civil Eng

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In order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi‐line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.

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Comparison of Two Algorithms for Multiline Bus Dynamic Dispatching

Liu

Luo

Cheng

et al. 2022

Discrete Dynamics in Nature and Society

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Dynamic bus scheduling refers to adjusting the departure time according to the latest time-varying information or adjusting bus speed in the process of operation. These control strategies can prevent bus bunching and alleviate traffic pressure. The paper studies the multiline bus dynamic scheduling with consideration of departure time and speed meanwhile. The hyperheuristic algorithm is proposed, and low-level heuristics (LLH) operators are designed. The simulation experiment is performed for the passenger flow distribution of different strengths and types of different scenarios. By comparing the experimental results of genetic algorithm (GA) and hyperheuristic algorithm in solving different scenarios, the results show that in smooth, increasing, decreasing, and multiconvex passenger flow mode, the performance of the hyperheuristic algorithm is higher than that of GA. The promotion rate reaches 18∼28%, and especially the average value of the hyperheuristic algorithm designed under multiconvex passenger flow is up to 28.62%, significantly reducing passengers’ waiting time. By comparing the stability of the three passenger flow modes, the results illustrate that the stability of the hyperheuristic algorithm is lower than that of GA. For the smooth passenger flow mode, the stability of medium and lower density of GA is higher than that of the hyperheuristic algorithm. In comparison, the high-density stability of the hyperheuristic algorithm is better than that of GA.

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Dynamic Bus Scheduling of Multiple Routes Based on Joint Optimization of Departure Time and Speed

Cited by 6 publications

References 62 publications

Optimal Route Assignment at the Bus Hub with Multiple Berths for Minimal Passenger Transfer Distance

Optimal Route Assignment at the Bus Hub with Multiple Berths for Minimal Passenger Transfer Distance

Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment

Comparison of Two Algorithms for Multiline Bus Dynamic Dispatching

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