Mining Travel Time from Smart Card Fare Data
The wide applications of smart card payment techniques in public transit systems provide a new way of collecting travel time information.In this paper,one method is proposed to estimate travel times with smart card fare payment data and bus schedule data.The proposed method first classifies two sequential card swipes to infer if they occur at the same stop with Naive Bayesian Classifier (NBC).Travel time is estimated from the NBC results using Maximum Likelihood Estimation (MLE), Dynamic Programming (DP)and Quadratic Programming (QP)methods.In order to solve the problem with imprecise initial parameters,coordinate descent method is applied,which updates parameters and estimate values alternatively until it converges. An experiment with real-world data is designed to quantify the reliability of this algorithm and the outcomes is contrast with GPS data.It shows that the error of this method is small and the convergence is fast.
GAO Lianxiong LIANG Hong
School of Electricity &Information Engineering,Yunnan Nationalities University,Kunming 650031,P.R.Ch School of Information,Yunnan University,Kunming 650091,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）