交通运输系统工程与信息

交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (4): 161-165.

• 系统工程理论与方法 • 上一篇    下一篇

基于出行者认知的理想通勤时间研究

何明卫1,2，赵胜川*1，何民2   

  1. 1. 大连理工大学交通运输学院，辽宁，大连116024；2. 昆明理工大学交通工程学院，昆明650051
  • 收稿日期:2015-03-13 修回日期:2015-05-04 出版日期:2015-08-25 发布日期:2015-08-25
  • 作者简介:何明卫（1981-），四川南江人，讲师，博士生.
  • 基金资助:

    国家自然科学基金资助项目（51278087）.

Ideal Commute Time Analysis Based on Traveler’s Cognition

HE Ming-wei1, 2, ZHAO Sheng-chuan 1, HE Min2   

  1. 1. School of Transportation & Logistics, Dalian University of Technology, Dalian 116024, Liaoning, China；2. Faculty of Transportation Engineering, Kunming University of Science & Technology, Kunming 650051, China
  • Received:2015-03-13 Revised:2015-05-04 Online:2015-08-25 Published:2015-08-25

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被引次数

10

摘要：

理想通勤时间对住址选择及职住关系、通勤出行方式选择和城市土地利用与空间布局等相关研究有重要意义.以昆明市为案例，研究了理想通勤时间的分布及其与实际通勤时间的差异，并基于序次Logistic 回归模型分析了影响理想通勤时间的因素.研究结果表明，理想通勤时间在[10，20）min 区间所占比例最大，并不总趋于0，这印证了通勤出行具有一定的正效用.71.3%的通勤者对实际通勤时间不满意，期望缩短通勤时间，仍有8.4%的通勤者的理想通勤时间大于实际通勤时间.年龄、性别、受教育程度、家庭年收入、居住区位对理想通勤时间有显著的影响.

关键词: 城市交通, 理想通勤时间, 通勤出行, 序次Logistic回归模型

Abstract:

Ideal commute time can provide some important implications for understanding residential location choice, jobs-housing relationship, commute mode choice, urban land use and spatial layout and so on. Taking Kunming as a case study, the distributions of ideal commute times and the differences between actual and ideal commute times are analyzed. Further, the factors affecting ideal commute time are identified based on ordered Logistic regression model. The results show that ideal commute time has the largest proportion in the interval of [10, 20) minutes and doesn’t always tend to 0, which supports the commute time can offer a positive utility. 71.3% of commuters is not satisfied with actual commute time and expect to shorten it. On the other hand, 8.4% of commuters asserts that his ideal commute time is longer than the actual commute time. At the same time, the variables such as age, gender, education level, household income, and residential location have significant impact on ideal commute times.

Key words: urban traffic, ideal commute time, commute trip, ordered Logistic regression model

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