交通运输工程

基于共享单车出行数据的用户行为分析

  • 韩震 ,
  • 杨丽 ,
  • 徐小凡
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  • (大连海事大学 航运经济与管理学院,辽宁 大连 116026)
韩震(1974 — ),男,教授,E-mail:hanzhen@dlmu.edu.cn.

收稿日期: 2019-03-18

  修回日期: 2019-06-21

  网络出版日期: 2019-06-21

基金资助

国家自然科学基金青年基金(71503029).

User behavior analysis based on shared bike trip data

  • HAN Zhen ,
  • YANG Li ,
  • XU Xiao-fan
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  • (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

Received date: 2019-03-18

  Revised date: 2019-06-21

  Online published: 2019-06-21

摘要

基于共享单车出行数据,对用户总体出行时间和强度进行分析,并据此对高频用户出行日期和路线进行分类,针对用户出行目的地建立预测模型.结果表明,工作日和休息日出行时间分布存在显著差异,出行强度具有频率低、距离短的特征;高频用户出行轨迹在工作和休闲两维度上可归纳为三种类型;基于共享单车历史出行数据,建立用户出行目的地预测模型所得的准确率较为合理.

本文引用格式

韩震 , 杨丽 , 徐小凡 . 基于共享单车出行数据的用户行为分析[J]. 大连海事大学学报, 2019 , 45(4) : 80 -86 . DOI: 10.16411/j.cnki.issn1006-7736.2019.04.011

Abstract

Based on the shared bicycle travel data, the overall travel time and intensity of users were analyzed, and the travel dates and routes of  high frequency users  were classified accordingly,and  a prediction model was established for users' travel destinations.The results show that there are significant differences in travel time distribution between weekdays and weekends, and travel intensity has the characteristics of low frequency and short distance;the travel trajectory of high frequency users can be classified into three types in two dimensions of work and leisure;the accuracy rate of the proposed user travel destination prediction model is more reasonable based on the shared bicycle travel data.

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