挖掘无桩共享单车用户的个体日常通勤模式：一个融合时空流聚类与规则决策树的双层框架
Mining individual daily commuting patterns of dockless bike-sharing users: a two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees
AI总结：
本文提出融合时空流聚类与规则决策树的双层框架，基于深圳超2亿条共享单车记录挖掘个体通勤模式，识别两类通勤者并揭示居住分布、加班时长及潮汐需求等规律，为主动交通规划提供低成本分析手段。
摘要：
无桩共享单车系统的兴起引发了人们对利用共享单车数据进行可持续交通和出行行为研究的日益关注。然而，这些研究很少聚焦于个体日常出行模式，阻碍了其与日益精细化的主动交通规划需求之间的契合。为弥合这一差距，本文提出一个双层框架，融合改进的流聚类方法和多个基于规则的决策树，从带有用户ID的无桩共享单车出行数据中挖掘个体骑行者的日常家—工作通勤模式。该框架的有效性和适用性通过深圳超过2亿条共享单车出行记录得到验证。基于挖掘结果，我们获得两类共享单车通勤者（74.38%的仅骑行通勤者和25.62%的骑行+公交通勤者），并发现了一些关于其日常通勤模式的有趣现象。例如，大量共享单车通勤者居住在城市村和旧社区附近，生活成本较低，尤其是在中心城区。仅骑行通勤者的加班比例高于骑行+公交通勤者，而以制造业为主的龙华工业园平均工作时间最长（每天超过10小时）。此外，大量用户使用共享单车上班通勤的频率高于下班回家，这与通勤高峰期间工作地周边共享单车过度需求密切相关。总之，该框架为理解居民细致的非机动化出行模式和低碳出行链提供了一种成本效益高的方法，也为改善共享单车服务和主动交通方式规划提供了新的见解。
英文摘要：
The rise of dockless bike-sharing systems has led to increased interest in using bike-sharing data for sustainable transportation and travel behavior research. However, these studies have rarely focused on the individual daily mobility patterns, hindering their alignment with the increasingly refined needs of active transportation planning. To bridge this gap, this paper presents a two-layer framework, integrating improved flow clustering methods and multiple rule-based decision trees, to mine individual cyclists' daily home-work commuting patterns from dockless bike-sharing trip data with user IDs. The effectiveness and applicability of the framework is demonstrated by over 200 million bike-sharing trip records in Shenzhen. Based on the mining results, we obtain two categories of bike-sharing commuters (74.38% of Only-biking commuters and 25.62% of Biking-with-transit commuters) and some interesting findings about their daily commuting patterns. For instance, lots of bike-sharing commuters live near urban villages and old communities with lower costs of living, especially in the central city. Only-biking commuters have a higher proportion of overtime than Biking-with-transit commuters, and the Longhua Industrial Park, a manufacturing-oriented area, has the longest average working hours (over 10 hours per day). Moreover, massive users utilize bike-sharing for commuting to work more frequently than for returning home, which is intricately related to the over-demand for bikes around workplaces during commuting peak. In sum, this framework offers a cost-effective way to understand the nuanced non-motorized mobility patterns and low-carbon trip chains of residents. It also offers novel insights for improving the bike-sharing services and planning of active transportation modes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
