Dynamic Travel Time Prediction Models for Buses Using Only GPS Data
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 times. The 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 real-time information at a given downstream bus stop using only global positioning system (GPS) data. Historical 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 models. Promising results are obtained from the case study, indicating that the models can be used to implement an Advanced Public Transport System. The 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 congestion. The performances of the three models were assessed and compared with each other under two criteria: overall prediction accuracy and robustness. It was shown that the ANN outperformed the other two models in both aspects. In 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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Supplemental Notes:
- This paper was sponsored by TRB committee AP010 Transit Management and Performance.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Gurmu, Zegeye Kebede
- Fan, Wei (David)
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Conference:
- Transportation Research Board 93rd Annual Meeting
- Location: Washington DC
- Date: 2014-1-12 to 2014-1-16
- Date: 2014
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Maps; References; Tables;
- Pagination: 17p
- Monograph Title: TRB 93rd Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Advanced public transportation systems; Arrivals and departures; Buses; Case studies; Global Positioning System; Kalman filtering; Mathematical prediction; Neural networks; Travel time
- Subject Areas: Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01516326
- Record Type: Publication
- Report/Paper Numbers: 14-0378
- Files: TRIS, TRB, ATRI
- Created Date: Feb 28 2014 1:32PM