Dynamic Travel Time Prediction Models for Buses Using Only GPS Data
作者机构：Associate ProfessorDepartment of Civil and Environmental EngineeringThe University of North Carolina at CharlotteEPIC BuildingRoom 32619201 University City BoulevardCharlotteNC 28223 Traffic Engineer-Teague Nall&PerkinsInc.1100 Macon StreetFort WorthTexas 76102
出 版 物：《International Journal of Transportation Science and Technology》 (交通科学与技术(英文))
年 卷 期：2015年第4卷第4期
页 面：353-366页
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：operators attracting conclusion
摘 要：Providing real-time and accurate travel time information of transit vehicles can be very helpful as it assists passengers in planning their trips to minimize waiting *** purpose of this research is to develop and compare dynamic travel time prediction models which can provide accurate prediction of bus travel time in order to give realtime information at a given downstream bus stop using only global positioning system(GPS)*** Average(HA),Kalman Filtering(KF)and Artificial Neural Network(ANN)models are considered and developed in this paper.A case has been studied by making use of the three *** results are obtained from the case study,indicating that the models can be used to implement an Advanced Public Transport *** implementation of this system could assist transit operators in improving the reliability of bus services,thus attracting more travelers to transit vehicles and helping relieve *** performances of the three models were assessed and compared with each other under two criteria:overall prediction accuracy and *** was shown that the ANN outperformed the other two models in both *** conclusion,it is shown that bus travel time information can be reasonably provided using only arrival and departure time information at stops even in the absence of traffic-stream data.
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