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Articles

The Flow Termini Coverage Model for Locating Bike-Sharing Stations

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Received 31 Jan 2023, Accepted 04 Dec 2023, Published online: 20 Mar 2024
 

Abstract

As of 2022, more than 1,900 cities around the world had implemented bike-sharing systems (BSSs) with docking stations. Because there is a limit on how far most people are willing to walk to and from BSS stations, maximal coverage models have been a popular approach to optimizing dock station locations. Existing maximal coverage models, however, mainly evaluate demand coverage based on proximity to individual stations without considering the fact that BSS users must have dock stations convenient to both their pickup and drop-off sites. Given this, traditional single-node maximal coverage models might not locate stations to maximize the number of complete-able trips and will also tend to overestimate the coverage of demand. This study proposes a flow termini coverage model (FTCM) to locate BSS stations by considering the flow-based BSS usage demands and network connectivity. The model is tested on the Youbike system in Taipei, one of the most heavily used dock-station BSSs worldwide. This study compares the FTCM with both the traditional single-station maximal coverage model and Taipei’s official planning strategy. Results suggest that the FTCM consistently covers more origin–destination trips, especially in areas with unbalanced BSS usage. We also provide discussion on the cost–benefit trade-offs for transportation planners to choose the most appropriate modeling approaches. The FTCM could also be adapted to optimizing micromobility hubs for parking dockless shared bikes and e-scooters.

截至2022年, 全球有1,900多个城市实施了基于停放站的共享单车系统(BSS)。由于大多数人愿意步行往返BSS停放站的距离是有限的, 最大覆盖模型是优化停放站位置的主流方法。然而, 现有的最大覆盖模型, 主要根据停放站距离来评估需求覆盖范围, 没有考虑BSS用户必须拥有便捷的取车和还车站点。因此, 传统的单节点最大覆盖模型可能无法确定出行数量最大化的站点, 而且往往会高估需求覆盖范围。本研究采用基于流动的BSS使用需求和网络连通性, 提出了确定BSS站点的流动终点覆盖模型(FTCM)。该模型在全球最繁忙的停放站BSS——台北Youbike系统得到测试, 并分别与传统单站最大覆盖模型和台北官方规划进行了比较。结果表明, FTCM始终能涵盖更多的起止出行, 尤其是在BSS使用不平衡的区域。本文讨论了交通规划者选择最佳建模方法的成本效益权衡。FTCM还可以优化无停放站共享单车和电动踏板车的微型流动性中心。

Más de 1.900 ciudades de todo el mundo habían implementado en 2022 sistemas de bicicletas compartidas (BSS), servidas con estaciones de acoplamiento. En cuanto existe un límite de la distancia que la mayoría de la gente está dispuesta a recorrer hacia las estaciones del BSS, y su regreso, los modelos de cobertura máxima han constituido el enfoque de uso más popular para optimizar la ubicación de aquellas estaciones. Sin embargo, los modelos de cobertura máxima existentes evalúan principalmente la cobertura de la demanda en función de la proximidad a las estaciones individuales, sin tener en cuenta el hecho de que los usuarios de las BSS deben disponer de estaciones de acoplamiento de conveniencia, tanto para sus sitios de recogida como de entrega. Teniendo en cuenta esta condición, los modelos tradicionales de cobertura máxima de un solo nodo podrían no ubicar a las estaciones para maximizar el número de viajes completos posibles, y también tenderán a sobreestimar la cobertura de la demanda. El presente estudio propone un modelo de cobertura de terminales de flujo (FTCM) para localizar las estaciones del BSSS, tomando en cuenta las demandas de uso de las BSS basadas en el flujo y la conectividad de la red. El modelo es puesto a prueba en el sistema Youbike de Taipéi, una de las estaciones de BSS más intensamente utilizadas en el mundo. Este estudio compara el FTCM tanto con el modelo tradicional de cobertura máxima de estación única, como con la estrategia de planificación oficial de Taipéi. Los resultados sugieren que el FTCM cubre consistentemente más viajes origen–destino, especialmente en las áreas con un uso desequilibrado del sistema BSS. También proveemos una discusión sobre las compensaciones costo–beneficio para que los planificadores del transporte escojan los enfoques de modelización más apropiados. El FTCM podría también adaptarse para optimizar los puntos de micromovilidad para estacionar bicicletas compartidas y scooters eléctricos carentes de base.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Hung-Chi Liu

HUNG-CHI LIU is a GIS PhD Student in the School of Geographical Sciences and Urban Planning at Arizona State University, Tempe, AZ 85281. E-mail: [email protected]. His research interests include spatial data analytics and spatial optimization, with applications in transport geography and urban planning.

Daoqin Tong

DAOQIN TONG is a Professor in the School of Geographical Sciences and Urban Planning at Arizona State University, Tempe, AZ 85281. E-mail: [email protected]. Her research interests include spatial data analytics, spatial optimization, and spatial statistics to support urban system design, operation, and sustainability.

Michael Kuby

MICHAEL KUBY is a Professor in the School of Geographical Sciences and Urban Planning at Arizona State University, Tempe, AZ 85287. E-mail: [email protected]. His research interests include spatial optimization and sustainable transportation.

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