吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 406-411.doi: 10.13229/j.cnki.jdxbgxb201602011

• Orginal Article • Previous Articles     Next Articles

Prediction of commuter's daily activity-travel duration time with support vector regression

ZONG Fang, WANG Zhan-zhong, JIA Hong-fei, JIAO Yu-ling, WU Yang   

  1. College of Transportation, Jilin University, Changchun 130022, China
  • Received:2014-06-09 Online:2016-02-20 Published:2016-02-20

Abstract: The commuter's daily activity-travel schedule is proposed and its key time allocation is investigated. According to the comparison of hazard model and Support Vector Regression (SVR) model, travel time prediction models and activity duration prediction models are developed by employing SVR. Then, the continuous time allocation, i.e. all the travel times and activity durations in daily activity-travel schedule are derived and the transit priority policy is evaluated using these models. The results indicate that the model system has a high level of prediction accuracy, and the goodness-of-fit of SVR models is higher than that of Hazard models. This study provides useful insights into commuter's activity-travel time allocation decision. It also serves a foundation that future models of full-scale daily activity-travel pattern can be built on. Moreover, it provides potential for transportation demand management policy analysis.

Key words: engineering of communications and transportation, commute travel time, support vector regression

CLC Number: 

  • U491.1
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