Better understanding the choice of travel mode by urban residents: New insights from the catchment areas of rail transit stations

目的地 运输工程 许可证 模式选择 旅游调查 运输方式 工作(物理) 模式(计算机接口) 城市轨道交通 住所 集水区 旅游行为 业务 中国 地理 计算机科学 工程类 流域 公共交通 经济 旅游 人口经济学 机械工程 地图学 考古 操作系统
作者
Xiaojin Luan,Lin Cheng,Yan Song,Jingya Zhao
出处
期刊:Sustainable Cities and Society [Elsevier BV]
卷期号:53: 101968-101968 被引量:34
标识
DOI:10.1016/j.scs.2019.101968
摘要

The primary objective of this paper is to systematically and quantitatively analyze the peculiarities of the mode of travel chosen by residents. More specifically, this study focuses on two major aspects: (1) shedding light on the prominent factors that affect choice of travel mode from an innovative perspective of the catchment areas (CAs) of urban rail transit (URT) stations; and (2) mining and comparing the features and advantages of a diverse range of travel modes. Furthermore, it puts forward some proposals for optimizing the structure of the urban transportation system available to the residents. Using a valuable, screened and discretized, survey dataset of good size (10,385 travel activities of 4,080 individuals in 1,454 households in Nanjing, China), two mixed-logit (ML) models are established. One is based on trip origins/destinations within the CAs of the URT stations. The other is based on residence locations. The modeling results reveal that most residents, except those who own a car and/or driving license, are inclined to choose a slow mode of transport (including walking and cycling). Travelers do not notably err towards walking and bus modes during the peak hours and/or when going to work or school instead of cycling. Residents adjacent to the CAs of URT stations are attracted away from other travel modes and have a tendency to select non-motorized modes to access the URT (metro). The contrastive analysis and discussion results can help to provide urban planners, managers, decision and policy makers with useful suggestions and data support for exploring the various factors (household properties, individual attributes, and trip information) affecting residents’ choice of travel mode and seeking the residents’ travel rules. Moreover, our findings have important implications with respect to improving the structure of the choice of travel modes made available to residents and simultaneously be important in maintaining green, low-carbon, and sustainable development of urban traffic systems.

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