Two-Step Coordinated Optimization Model of Mixed Demand Responsive Feeder Transit

Wang, Zhengwu; Yu, Jie; Hao, Wei; Tang, Jinjun; Zeng, Qiang; Ma, Changxi; Yu, Rongjie

doi:10.1061/jtepbs.0000317

Cited by 23 publications

(16 citation statements)

References 50 publications

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“…Their results show that the mixed lead time feeder line would be able to serve medium-sized demand with low trip density. A door-to-door approach with mixed lead times is investigated by Wang et al (2020). The operator's profit is maximized and the passengers' travel time is minimized.…”

Section: Multi-objectivementioning

confidence: 99%

A survey on demand-responsive public bus systems

Vansteenwegen

Melis

Aktaş

et al. 2022

Transportation Research Part C: Emerging Technologies

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Section: Multi-objectivementioning

confidence: 99%

A survey on demand-responsive public bus systems

Vansteenwegen

Melis

Aktaş

et al. 2022

Transportation Research Part C: Emerging Technologies

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“…e MIP model, constructed by equations ( 6)- (15), represents a more complicated vehicle routing problem (VRP) that is solved by heuristic algorithm, such as genetic algorithm (GA) [62][63][64], hyperheuristic algorithm [65], and hybrid of metaheuristic algorithm [66]. Ho et al [67] argues that combining metaheuristics with other types of metaheuristics and mathematical programming is a growing trend to solve the DAR problem, and hybrids of metaheuristics usually come into two forms: (i) each metaheuristic is executed sequentially and (ii) a metaheuristic is executed within another metaheuristic.…”

Section: Solution Algorithmmentioning

confidence: 99%

Designing High-Freedom Responsive Feeder Transit System with Multitype Vehicles

Wang

Hao

et al. 2020

Journal of Advanced Transportation

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The last mile travelling problem is the most challenging part when using public transit. This study designs a high-freedom responsive feeder transit (HFRFT) system to serve at the transfer station, given vehicle routes, departure time, and service area based on demand. The proposed feeder transit system employs a travelling mode with multitype vehicles. In order to improve the operation of the HFRFT system, the optimization design methods are suggested for vehicle routes, scheduling, and service area. A mixed integer programming model and its hybrid of a metaheuristic algorithm are proposed to efficiently and integrally solve the vehicle routes and scheduling parameters according to the reservation requirements. A heuristic method is proposed to optimize the service area based on the equilibrium of system supply and demand. Case studies show that the mixed running mode of multiple models can significantly improve the seat utilization, which can also significantly reduce the number of departures and the average travel distance per passenger. The proposed service area optimization method is proved to be feasible to improve the last mile travel.

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“…Huang et al (2020) optimised the route of DRT with the objective of maximising the benefits of the system while considering the preferred time windows of passengers. Wang et al (2020) proposed a two-step coordinated optimisation model, in which the vehicle running route and schedule and the response to the passenger's real-time application were optimised, respectively. Franco, Johnston, and McCormick (2020) developed an agent-based model to predict the performance of a DRT service using an activity-based approach.…”

Section: Introductionmentioning

confidence: 99%

Joint optimisation of regular and demand-responsive transit services

Zhao

Sun

Cats

2021

Transportmetrica A: Transport Science

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This study aims to jointly optimise regular and demand responsive transit (DRT) services, which can offer opportunities for leveraging on their respective advantages. An optimisation model with the objective of minimising the total travel time of passengers and the total fleet size is proposed. The terminal bus stops of regular bus lines, the service area of the DRT, and the fleet size of both regular and DRT are optimised simultaneously. A rule-based optimisation preparation step is added to the proposed model to obtain a reasonable design scheme and to reduce the computational load. The model is solved using a tailored boundary-start-based two-step heuristic algorithm. The performance of the mixed network is affected by the preference of the decision maker and the operation mode adopted for the DRT service. A reduction in the operational level of the DRT results in a considerable increase in the travel time of DRT passengers.

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Two-Step Coordinated Optimization Model of Mixed Demand Responsive Feeder Transit

Cited by 23 publications

References 50 publications

A survey on demand-responsive public bus systems

A survey on demand-responsive public bus systems

Designing High-Freedom Responsive Feeder Transit System with Multitype Vehicles

Joint optimisation of regular and demand-responsive transit services

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