Expected value model for optimizing the multiple bus headways

Li, Yanhong; Xu, Wangtu; He, Shiwei

doi:10.1016/j.amc.2012.11.098

Cited by 38 publications

(27 citation statements)

References 18 publications

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“…Implementing the proposed approach in examples shows that the most significant cost reduction is obtained in cases with low-level of service. Li et al (2013) also consider stochastic parameters such as demand, arrival times, boarding/alighting times, and travel times. The authors define a stochastic optimization approach to find the optimal frequency that minimizes the sum of the expected value of the company profit and the waiting time costs for passengers.…”

Section: Frequency Setting Problemmentioning

confidence: 99%

Planning, operation, and control of bus transport systems: A literature review

Ibarra-Rojas

Delgado

Giesen

et al. 2015

Transportation Research Part B: Methodological

533

238

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Section: Frequency Setting Problemmentioning

confidence: 99%

Planning, operation, and control of bus transport systems: A literature review

Ibarra-Rojas

Delgado

Giesen

et al. 2015

Transportation Research Part B: Methodological

533

238

View full text Add to dashboard Cite

“…Guihaire and Hao (2008) and Ibarra-Rojas et al (2015) review 26 contributions dealing with headway optimization, showing that early works assume fixed demand and are based on analytic models (Newell 1971;Salzborn 1972;Schéele 1980;Han and Wilson 1982). Six of the 26 works Yu et al 2011;Huang et al 2013;Li et al 2013;Wu et al 2015) and Ruano et al (2017) more recently, employ genetic algorithms for the headway optimization problem. Most of them also use non-linear models, though Li et al (2013) offer a simulation model.…”

Section: Introductionmentioning

confidence: 99%

Population-based simulation optimization for urban mass rapid transit networks

2019

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In this paper, we present a simulation-based headway optimization for urban mass rapid transit networks. The underlying discrete event simulation model contains several stochastic elements, including time-dependent demand and turning maneuver times as well as direction-dependent vehicle travel and passenger transfer times. Passenger creation is a Poisson process that uses hourly origin-destination-matrices based on anonymous mobile phone and infrared count data. The numbers of passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms and within vehicles is considered. The bi-objective problem, involving cost reduction and service level improvement, is transformed into a single-objective optimization problem by normalization and scalarization. Population-based evolutionary algorithms and different solution encoding variants are applied. Computational experience is gained from test instances based on real-world data (i.e., the Viennese subway network). A covariance matrix adaptation evolution strategy performs best in most cases, and a newly developed encoding helps accelerate the optimization process by producing better short-term results.

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“…al., 2015). The optimization approach followed in this research differs from many past approaches in that it optimizes the allocation of a fixed total amount of vehicles over a group of routes rather than determining frequencies, although the two problems are closely related (Ceder, 1984;Constantin and Florian, 1995;Furth and Wilson, 1981;Li et. al., 2013;Martínez et.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of vehicles to bus routes using automatically collected data and simulation modelling

Sánchez-Martínez

Koutsopoulos

Wilson

2016

Research in Transportation Economics

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Monitoring the service quality of high-frequency bus transit is important both to agencies running their own operations and those contracting out, where performance measures can be used to assess contract penalties or bonuses. The availability of automatically collected vehicle movement and demand data enables detecting changes in running times and demand, which may present opportunities to improve service quality and fleet utilization. This research develops a framework to maximize service performance in a set of highfrequency bus routes, given their planned headways and a total fleet size constraint. Using automatically collected data and simulation modelling to evaluate the performance of each route with varying fleet sizes, a greedy algorithm adjusts allocation toward optimality. A simplified case study involving morning peak service on nine bus routes in Boston demonstrates the feasibility and potential benefits of the approach. A potential application is automated detection of routes operating with insufficient or excessive resources.

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Expected value model for optimizing the multiple bus headways

Cited by 38 publications

References 18 publications

Planning, operation, and control of bus transport systems: A literature review

Planning, operation, and control of bus transport systems: A literature review

Population-based simulation optimization for urban mass rapid transit networks

Optimal allocation of vehicles to bus routes using automatically collected data and simulation modelling

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