Analysing bicycle-sharing system user destination choice preferences: Chicago's Divvy system
Analysing bicycle-sharing system user destination choice preferences: Chicago's Divvy system
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DOI：
10.1016/j.jtrangeo.2015.03.005
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发表时间：
2015-04-01
影响因子：
6.1
通讯作者：
Eluru, Naveen
中科院分区：
文献类型：
--
作者：
Faghih-Imani, Ahmadreza;Eluru, Naveen
近年来，人们越来越关注自行车共享系统（BSS）作为一种可行且可持续的短途交通方式。然而，由于BSS的采用相对较新，很少有研究探讨人们如何在现有的交通选择中考虑这些系统。考虑到最近BSS在世界各地的增长，人们对确定鼓励个人使用这些系统的促成因素非常感兴趣。目前的研究有助于这一不断增长的文献研究BSS行为在行程水平上分析骑自行车的目的地偏好。具体来说，我们研究的决策过程中，在BSS站拿起自行车后，确定目的地的位置，使用随机效用最大化方法的形式的多项logit模型（MNL）。所开发的定量框架是使用芝加哥Divvy系统2013年的数据进行估计的。在我们的建模工作中，我们区分了年度会员的BSS用户和每日通行证的短期客户。开发的模型应允许自行车共享系统运营商通过检查旅行距离，土地使用，建筑环境和公共交通基础设施对用户目的地偏好的影响，更有效地规划服务。使用估计的模型，我们生成的效用曲线作为距离和各种其他属性的函数，使我们能够直观地表示个人在决策过程中所做的权衡。为了进一步说明所提出的框架规划的目的，目的地站选择概率预测的适用性。(C)2015爱思唯尔有限公司版权所有。
In recent years, there has been increasing attention on bicycle-sharing systems (BSS) as a viable and sustainable mode of transportation for short trips. However, due to the relatively recent adoption of BSS, there is very little research exploring how people consider these systems within existing transportation options. Given recent BSS growth around the world, there is substantial interest in identifying contributing factors that encourage individuals to use these systems. The current study contributes to this growing literature by examining BSS behavior at the trip level to analyze bicyclists' destination preferences. Specifically, we study the decision process involved in identifying destination locations after picking up a bicycle at a BSS station, using a random utility maximization approach in the form of a multinomial logit model (MNL). The quantitative frameworks developed have been estimated using 2013 data from the Chicago's Divvy system. In our modeling effort, we distinguish between BSS users with annual membership and short-term customers with daily passes. The developed model should allow bicycle-sharing system operators to plan services more effectively by examining the impact of travel distance, land use, built environment, and access to public transportation infrastructure on users' destination preferences. Using the estimated model, we generated utility profiles as a function of distance and various other attributes, allowing us to represent visually the trade-offs that individuals make in the decision process. To illustrate further the applicability of the proposed framework for planning purposes, destination station-choice probability prediction is undertaken. (C) 2015 Elsevier Ltd. All rights reserved.