Bus route design with limited travel time

Kuo, Yiyo; Luo, Chi-Ming; Wang, Chi-Chang

doi:10.3846/16484142.2013.865103

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…In terms of algorithm design, researchers have proposed numerous heuristic or metaheuristic algorithms to solve feeder bus network optimization problems, such as simulated annealing algorithm [13], genetic algorithm [4], [14], hybrid enhanced artificial bee colony algorithm [6], and a complex two-phase heuristic algorithm [11]. Reference [11] proposed a two-phase heuristic algorithm to optimize the route network design and frequency setting problems.…”

Section: Introductionmentioning

confidence: 99%

Route Network Design of Community Shuttle for Metro Stations Through Genetic Algorithm Optimization

Xiong

Chen

et al. 2019

IEEE Access

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This paper investigates an issue for optimizing community shuttle network linked with metro stations. Considering a set of trip demands originating from several bus stops within a large community area, a mixed integer optimization model is formulated to obtain the optimized shuttle route network and the frequency of each route simultaneously, to minimize the total transit system cost, including user and supplier costs. To solve the problem, a solution framework consisting of three primary components is proposed: an initial route network scheme is developed to ensure that any generated network is feasible; a network analysis procedure is performed that assigns the demand of each stop to a set of paths on the generated network according to a certain proportion; a heuristic algorithm is proposed to optimize the service frequency of each route; a genetic algorithm consisting of a set of specifically designed operators is proposed to guide the solution population evolving process. A real-life study is tested successfully using the proposed solution framework. The related sensitivity analysis and impacts of some important variables on the solution are performed.

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Section: Introductionmentioning

confidence: 99%

Route Network Design of Community Shuttle for Metro Stations Through Genetic Algorithm Optimization

Xiong

Chen

et al. 2019

IEEE Access

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“…It is well known that the travel time is hard to be precisely measured and predicted (Chakroborty, Kikuchi 2004;Vu, Khan 2010;Ng et al 2011). However, the travel times and their distributions are essential and fundamental for the research in different areas of public transport, including, for example, the bus route design (Kuo et al 2013), service planning (Furth, Muller 2007), reliability measurement (Van Lint et al 2008;Mazloumi et al 2010), timetable optimization (Yan et al 2012), bus dispatching (Dessouky et al 1999), and vehicle scheduling (Zhao et al 2006;Xu, Shen 2012). Moreover, the travel time is also the concern of passengers, which is one of key criteria for bus service quality (Abkowitz, Tozzi 1987;Uniman et al 2010;Van Oort et al 2012;Mori et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Modelling Travel Time Distribution and Its Influence Over Stochastic Vehicle Scheduling

Shen

et al. 2019

Transport

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Due to the paucity of well-established modelling approaches or well-accepted travel time distributions, the existing travel time models are often assumed to follow certain popular distributions, such as normal or lognormal, which may lead to results deviating from actual ones. This paper proposes a modelling approach for travel times using distribution fitting methods based on the data collected by Automatic Vehicle Location (AVL) systems. By this proposed approach, a compound travel time model can be built, which consists of the best distribution models for the travel times in each period of a day. Applying to stochastic vehicle scheduling, the influence of different travel time models is further studied. Results show that the compound model can fit more precisely to the actual travel times under various traffic situations, whilst the on-time performance of resulting vehicle schedules can be improved. The research findings have also potential benefit for the other research based on travel time models in public transport including timetabling, service planning and reliability measurement.

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Network Analyses in Geography of Transport

Borowska-Stefańska

Wiśniewski

2018

Studia Informatica

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Bus route design with limited travel time

Cited by 3 publications

References 14 publications

Route Network Design of Community Shuttle for Metro Stations Through Genetic Algorithm Optimization

Route Network Design of Community Shuttle for Metro Stations Through Genetic Algorithm Optimization

Modelling Travel Time Distribution and Its Influence Over Stochastic Vehicle Scheduling

Network Analyses in Geography of Transport

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