Abstract

Cycling is a sustainable form of transportation that can reduce car usage and benefit both individuals and society. Bike-sharing systems (BSSs) help to position cycling as a daily transportation option and have been widely established in many countries. Previous studies have investigated the association between urban environmental factors and BSSs’ demand to promote the broader use of BSSs and determine whether demand is affected by various factors. However, research on the effects of the urban environment on BSS demand according to the trip purpose (e.g., commuting and leisure) is rare due to the difficulty in understanding users’ trip purposes. In this regard, recent advancements in big data technologies make massive BSSs trip data available to the public, which is useful for in-depth analyzing BSS travel patterns and inferring the trip purposes. This study thus analyzes to what extent demand is affected by urban environmental factors for different trip purposes, focusing on Seoul Bike, through the integration of clustering users’ travel patterns and analyzing geospatial data affecting demand. By observing trip data, BSS trips were clustered into short-distance travel for utilitarian purposes and longer-distance roaming for recreational purposes. The utilitarian trips were more affected by the large floating population and high land-use mix, and they were more concentrated during the rush hours in the crowded areas, while the leisure trips were more concentrated in secluded residential areas and were close to the waterfront. This study can contribute to establishing plans to increase the demand for and optimize the operation of BSSs.

摘要

骑自行车是一种可持续的交通方式，可以减少汽车的使用，使个人和社会都受益。自行车共享系统 (BSS) 有助于将自行车定位为日常交通选择，并已在许多国家广泛建立。以前的研究已经调查了城市环境因素与 BSS 需求之间的关联，以促进 BSS 的更广泛使用，并确定需求是否受到各种因素的影响。然而，由于难以理解用户的出行目的，很少根据出行目的（如通勤和休闲）研究城市环境对 BSS 需求的影响。在这方面，大数据技术的最新进展使海量的 BSS 出行数据可供公众使用，这有助于深入分析 BSS 出行模式和推断出行目的。因此，本研究通过整合聚集用户的出行模式和分析影响需求的地理空间数据，分析不同出行目的的城市环境因素对需求的影响程度，重点关注首尔自行车。通过观察旅行数据，BSS旅行分为实用目的的短途旅行和娱乐目的的长途漫游。功利出行受流动人口多、土地利用组合高的影响较大，高峰时段多集中在人流密集的地区，休闲出行多集中在僻静的住宅区和靠近水岸的地方。这项研究有助于制定计划以增加对 BSS 的需求并优化其运行。