Comparison of Two Algorithms for Multiline Bus Dynamic Dispatching

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

doi:10.1155/2022/2086717

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…But not all passengers are able to be served by the first arriving bus. Therefore, Liu et al (2022) modeled the timetabling problem taking into account the extra waiting time of these passengers and developed a heuristic algorithm to solve the model. In summary, many scholars have studied the timetable problem of HBs from the perspective of frequency and departure time respectively.…”

Section: Hbs Timetable Optimizationmentioning

confidence: 99%

Joint design of timetabling and capacity for mixed transit system with autonomous and human-driven buses considering passengers’ acceptance

Zhou,

Liu,

Zhao

et al. 2024

Preprint

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Human-driven buses (HBs) with fixed-capacity are difficult to adapt to time-varying demand, resulting in overcrowding and excessive operating cost. With the introduce of self-driving technology in public transit, autonomous buses (ABs) with flexible capacity are intended to alleviate this problem. In this paper, based on a mixed transit system (MTS) with ABs and HBs, a mixed integer nonlinear programming (MINLP) model is formulated for jointly optimizing timetabling and capacity, by taking into account time-varying demand and passenger acceptance of HBs and ABs. The objective function is to minimize the operating cost and passenger cost. Genetic algorithm is employed to solve the model. The results show that MTS, compared to HBs system, can reducing total cost by 10.57% during peak periods, increase the frequency by 29.73%, and improve full load factor by 7.45% during off-peak periods. The sensitivity analysis of passenger acceptance reveals that the total cost of MTS tends to decrease when the proportion of passengers who accept ABs increases.

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Section: Hbs Timetable Optimizationmentioning

confidence: 99%

Joint design of timetabling and capacity for mixed transit system with autonomous and human-driven buses considering passengers’ acceptance

Zhou,

Liu,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Regarding bus scheduling, some hyper-heuristic methods have been applied. Liu et al [11] compare GAs and a hyper-heuristic for the dynamic bus scheduling which proved superior to the GA. Hyper-heuristics have been also used to optimise the bus routes themselves [1].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sequence-Based Selection Hyper-Heuristic for Application to Electric Bus Scheduling

Chitty

Lewis²,

Keedwell

2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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Buses are important for public transportation and beneficial for the environment. However, diesel buses are significant polluters emitting greenhouse gases and particulates. Consequently, with the advent of electric vehicles there has been a drive to transition to electric buses. Key to this transition is to optimise electric bus fleets to reduce distance travelled whilst maintaining service levels. This is complex due to the added constraint of the limited range of electric buses. This paper considers the use of a Sequence-based Selection Hyper Heuristic (SSHH) method to solve this problem. Moreover, an adaptive SSHH (A-SSHH) technique is introduced which significantly improves upon SSHH. Indeed, bus fleet non-service distances and sizes are reduced by as much as 10% using A-SSHH over SSHH. Comparing with an optimised diesel bus fleet electric buses reduce carbon dioxide emissions by over 60% and importantly for fleet operators, energy costs are similarly reduced. CCS CONCEPTS• Computing methodologies → Search methodologies.

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“…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]. Markevych et al [21] established the influence of bus dwell time on the inter-route transfer impedance with simulation.…”

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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Comparison of Two Algorithms for Multiline Bus Dynamic Dispatching

Cited by 4 publications

References 18 publications

Joint design of timetabling and capacity for mixed transit system with autonomous and human-driven buses considering passengers’ acceptance

Joint design of timetabling and capacity for mixed transit system with autonomous and human-driven buses considering passengers’ acceptance

An Adaptive Sequence-Based Selection Hyper-Heuristic for Application to Electric Bus Scheduling

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

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