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.

• Record URL:
  https://annualmeeting.mytrb.org/OnlineProgram/Details/15647
• Supplemental Notes:
  • This paper was sponsored by TRB committee AP050 Standing Committee on Bus Transit Systems.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Williams, Ryan
  • Shalaby, Amer
• 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