Bike-Share Systems: Accessibility and Availability

Kabra, Ashish; Belavina, Elena; Girotra, Karan

doi:10.2139/ssrn.2555671

Cited by 45 publications

(19 citation statements)

References 29 publications

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“…Using high-frequency systemuse data from a bicycle-sharing system, Kabra et al (2015) measure the accessibility of stations and the availability of bicycles. They then estimate the effects of accessibility and availability on ridership through a novel transformation technique and reveal a potential opportunity for service improvement in Paris using a calibrated counterfactual simulation.…”

Section: Literature Reviewmentioning

confidence: 99%

Service Region Design for Urban Electric Vehicle Sharing Systems

Mak

Rong

et al. 2017

M&SOM

209

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Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles' special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib', car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by Zipcar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operation costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of car2go's service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service.

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Section: Literature Reviewmentioning

confidence: 99%

Service Region Design for Urban Electric Vehicle Sharing Systems

Mak

Rong

et al. 2017

M&SOM

209

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“…How much of demand can be encouraged by altering the proximity of stations is an empirical question. Using real-time data on the number of bikes at stations in central Paris, Kabra et al (2015a) find that 6.7 % more bike-sharing trips occur for every 10 % reduction in distance between stations.…”

Section: Station Locationsmentioning

confidence: 99%

“…The proportion of time for which bikes are available at a station can be used as its measure of bike availability. Kabra et al (2015a) identify two effects of low bike availability. One is the short-term effect due to stock-outs.…”

Section: Bike Availabilitymentioning

confidence: 99%

“…8.2b, which shows that higher levels of system use can be achieved (albeit with less bike availability) by locating stations more densely. In the case of central Paris, Kabra et al (2015a) find that the optimal design involves making the system dense enough to increase the number of system trips by 29.41 %. Most of these gains are achieved by making the system from two to four times more dense; returns to this strategy are diminishing after that point.…”

Section: Balancing the Demands Of Accessibility And Availabilitymentioning

confidence: 99%

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Environmentally Responsible Supply Chains

Atasu¹

2016

Springer Series in Supply Chain Management

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“…Orthogonal approaches to the question of where to allocate docks have been taken by Kabra et al [2015] and . The former considers demand as endogeneous and aims to identify the station density that maximizes sales, whereas we consider demand and station locations as exogeneously given and aim to allocate docks and bikes to maximize the amount of demand that is being met.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Multimodular Functions and Allocating Capacity in Bike-Sharing Systems

Freund

Henderson

Shmoys

2017

Integer Programming and Combinatorial Optimization

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Abstract. The growing popularity of bike-sharing systems around the world has motivated recent attention to models and algorithms for the effective operation of these systems. Most of this literature focuses on their daily operation for managing asymmetric demand. In this work, we consider the more strategic question of how to (re-)allocate dock-capacity in such systems. We develop mathematical formulations for variations of this problem (service performance over the course of one day, long-runaverage performance) and exhibit discrete convex properties in associated optimization problems. This allows us to design a practically fast polynomial-time allocation algorithm to compute optimal solutions for this problem, which can also handle practically motivated constraints, such as a limit on the number of docks moved in the system. We apply our algorithm to data sets from Boston, New York City, and Chicago to investigate how different dock allocations can yield better service in these systems. Recommendations based on our analysis have been adopted by system operators in Boston and New York City. Beyond optimizing for improved quality of service through better allocations, our results also quantify the reduction in rebalancing achievable through strategically reallocating docks.

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Bike-Share Systems: Accessibility and Availability

Cited by 45 publications

References 29 publications

Service Region Design for Urban Electric Vehicle Sharing Systems

Service Region Design for Urban Electric Vehicle Sharing Systems

Environmentally Responsible Supply Chains

Minimizing Multimodular Functions and Allocating Capacity in Bike-Sharing Systems

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