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Understanding bike sharing travel patterns: An analysis of trip data from eight cities

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  • Kou, Zhaoyu
  • Cai, Hua

Abstract

As a new mobility option, bike sharing is gaining popularity around the world. Understanding the travel patterns of bike sharing trips can provide fundamental basis for researchers to model the use of bike sharing and the associated multi-modal transportation systems, inform bike sharing system design and operation, and guide policy decisions for sustainable transportation development. Using bike sharing trip data from eight cities in the United States, we analyzed the distributions of trip distance and trip duration for bike sharing trips for commuting and touristic purposes. Our results show that both the trip distance and duration follows a lognormal distribution in larger bike sharing systems (e.g., in Boston, Washington DC, Chicago, and New York), while the distribution for smaller systems varies among Weibull, gamma, and lognormal because the systems’ geographical boundary restricts the movement of users. Our analysis of the long trips also show that the trip distance and duration also displays a power law decay in the larger systems.

Suggested Citation

  • Kou, Zhaoyu & Cai, Hua, 2019. "Understanding bike sharing travel patterns: An analysis of trip data from eight cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 785-797.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:785-797
    DOI: 10.1016/j.physa.2018.09.123
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