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Bikesharing trip patterns in New York City: Associations with land use, subways, and bicycle lanes

机译:纽约市的单车共享出行方式:与土地使用,地铁和自行车道的关联

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摘要

As bikesharing systems have proliferated, few studies have examined the trips made on these systems. In this paper, we examine trips between origin-destination pairs during three months in 2015 on New York City's Citi Bike system. Findings suggest considerable variation across user types, across months, and across times of day. Principal findings indicate that bikesharing is used for transit access and egress during rush hours, and that stations located along the same high-quality bicycle route see far more trips than do other station pairs. Casual users complement subscribers' usage by using bicycles more frequently during midday and the evening, and between areas characterized by nearby recreational land uses. Loop trips to and from the same station also occur and are likely recreational trips. The data analyzed is essentially a form of "big data." That is, large data sets that are ubiquitously collected. The analysis suggests that in this case, "big data" that lacks the socio-economic data commonly collected and used in travel analysis can provide useful insights to planners.
机译:随着自行车共享系统的激增,很少有研究检查这些系统的行程。在本文中,我们研究了2015年在纽约市的Citi Bike系统上进行的三个月内起点-终点之间的旅行。调查结果表明,不同用户类型,不同月份和一天中的不同时间会有很大差异。主要研究结果表明,在高峰时段将共享单车用于过境通道和出站,沿同一条高质量自行车路线行驶的站点出行次数比其他站点对要多得多。临时用户在中午和晚上以及附近以休闲娱乐为特征的区域之间更频繁地骑自行车来补充订户的使用。也可能发生往返于同一站点的循环旅行,并且很可能是娱乐旅行。分析的数据实质上是“大数据”的一种形式。也就是说,无处不在的大数据集被收集。分析表明,在这种情况下,缺少通常在旅行分析中收集和使用的社会经济数据的“大数据”可以为计划者提供有用的见解。

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  • 作者单位

    Rutgers State Univ, Edward J Bloustein Sch Planning & Publ Policy, 33 Livingston Ave, New Brunswick, NJ 08901 USA;

    Rutgers State Univ, Edward J Bloustein Sch Planning & Publ Policy, 33 Livingston Ave, New Brunswick, NJ 08901 USA;

    Rutgers State Univ, Edward J Bloustein Sch Planning & Publ Policy, 33 Livingston Ave, New Brunswick, NJ 08901 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bikeshare; big data; travel patterns;

    机译:Bikeshare;大数据;旅行模式;

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