An Optimisation Algorithm to Establish the Location of Stations of a Mixed Fleet Biking System: An Application to the City of Lisbon

Martínez, Luis; Caetano, Luís; Eiró, Tomás; Cruz, Francisco

doi:10.1016/j.sbspro.2012.09.769

Cited by 120 publications

(61 citation statements)

References 7 publications

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“…Dell'Olio et al (2011) develop a comprehensive methodology for implementation, from estimating potential demand to optimizing locations. Martinez et al (2012) and Prem Kumar and Bierlaire (2012) present MIP models for the location problem. Lin and Yang (2011) create a model trading off the interests of both users and investors.…”

Section: Related Workmentioning

confidence: 99%

Inventory rebalancing and vehicle routing in bike sharing systems

Schuijbroek

Hampshire

Hoeve

2017

European Journal of Operational Research

469

304

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Bike sharing systems have been installed in many cities around the world and are increasing in popularity. A major operational cost driver in these systems is rebalancing the bikes over time such that the appropriate number of bikes and open docks are available to users. We combine two aspects that have previously been handled separately in the literature: determining service level requirements at each bike sharing station, and designing (near-)optimal vehicle routes to rebalance the inventory. Since finding provably optimal solutions is practically intractable, we propose a new cluster-first route-second heuristic, in which the polynomialsize Clustering Problem simultaneously considers the service level feasibility constraints and approximate routing costs. Extensive computational results on real-world data from Hubway (Boston, MA) and Capital Bikeshare (Washington, DC) are provided, which show that our heuristic outperforms a pure mixed integer programming formulation and a constraint programming approach.

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Section: Related Workmentioning

confidence: 99%

Inventory rebalancing and vehicle routing in bike sharing systems

Schuijbroek

Hampshire

Hoeve

2017

European Journal of Operational Research

469

304

View full text Add to dashboard Cite

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“…Due to the uncertainty of travelers' demands, the location{allocation of the bike sharing system should be considered under uncertain environment. In most of studies, expected value of uncertain demand is considered during the modeling of the bike sharing system [10,11,37], but in this study, we try to consider uncertainty nature of demands.…”

Section: Model Formulation 21 Problem Descriptionmentioning

confidence: 99%

A capacitated bike sharing location-allocation problem under demand uncertainty using sample average approximation: A greedy Genetic-Particle Swarm Optimization algorithm

2017

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Abstract. This paper considers a stochastic location-allocation problem for a capacitated bike sharing system (S-L&A-CBSS), in which bike demand is uncertain. To tackle this uncertainty, a Sample Average Approximation (SAA) method is used. Because this problem is an NP-hard problem, a hybrid greedy/evolutionary algorithm based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), namely greedy GA-PSO, is embedded in the SAA method in order to solve the given large-sized problems. The performance of the proposed hybrid algorithm is tested by a number of numerical examples and used for empirical test based on Tehran business zone. Furthermore, the associated results show its e ciency in comparison to an exact solution method in solving small-sized problems. Finally, the conclusion is provided.

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“…The prioritization index was aggregated from several indicators including OD of actual bicycle trips, OD of short car trips, cyclists' route preferences, and concentration of bicycle crashes. Martinez et al [39] proposed a heuristic algorithm, which encompassed a mixed integer linear program (MILP) and a p-median location-allocation problem to optimize the location of bike sharing stations in Lisbon, Portugal. The locations of bike stations were determined based on a list of factors related to user demand, the required investment, and operational costs.…”

Section: Bike Stations and Location-allocation Modelsmentioning

confidence: 99%

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

Shaw

Fang

et al. 2016

IJGI

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Abstract:This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning.

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An Optimisation Algorithm to Establish the Location of Stations of a Mixed Fleet Biking System: An Application to the City of Lisbon

Cited by 120 publications

References 7 publications

Inventory rebalancing and vehicle routing in bike sharing systems

Inventory rebalancing and vehicle routing in bike sharing systems

A capacitated bike sharing location-allocation problem under demand uncertainty using sample average approximation: A greedy Genetic-Particle Swarm Optimization algorithm

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

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