A Framework for Reliability-Sensitive Real-Time Bus Travel Time Prediction
Surface transit systems face many challenges in their operation, which can result in poor schedule adherence. These challenges include traffic congestion, on-road obstructions or emergencies, compounded delays from traffic signals, and bunching. Passenger information systems that predict arrival times using real-time data from location tracking devices on transit vehicles can reduce the inconvenience caused by schedule deviations. However, real-time location data streams are often noisy, incomplete or inaccurate, requiring special consideration in real-time prediction applications. Additionally, transit may operate in conditions of high or low service reliability, which may require different treatments. This study proposes and tests a real-time prediction framework that uses measures of both service reliability and data reliability/quality to characterize conditions while making predictions sequentially and leveraging a variety of Machine Learning methods. The tests performed show that the proposed framework offers distinct advantages, while highlighting shortcomings and potential for future research.
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Supplemental Notes:
- This paper was sponsored by TRB committee AP050 Standing Committee on Bus Transit Systems.
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Corporate Authors:
Transportation Research Board
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Authors:
- Williams, Ryan
- Shalaby, Amer
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Conference:
- Transportation Research Board 100th Annual Meeting
- Location: Washington DC, United States
- Date: 2021-1-5 to 2021-1-29
- Date: 2021
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 16p
Subject/Index Terms
- TRT Terms: Bus transit; Predictive models; Real time information; Reliability; Travel time
- Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;
Filing Info
- Accession Number: 01763671
- Record Type: Publication
- Report/Paper Numbers: TRBAM-21-03560
- Files: TRIS, TRB, ATRI
- Created Date: Feb 4 2021 10:57AM