Bayesian Inference of Passenger Boarding Strategies at Express Stops with Real-Time Bus Arrival Information

Efficient design of express and local bus services in urban corridors requires accurate understanding of the travel demand and heterogeneities in passengers’ preferences and needs. Public transit Automated Fare Collection (AFC) systems provide a high-coverage source of data that facilitates an unprecedented opportunity for understanding the demand patterns and passenger preferences for more efficient service designs.In this paper, a Bayesian inference method is proposed to analyze the AFC repeated boarding records of passengers in the presence of real-time bus arrival information. A continuous representation of boarding strategies is introduced that can capture the behavior of passengers if they extend their waiting times to board a preferred route that is due shortly. The proposed method is tested in a case study on the Western Avenue corridor in Chicago, Illinois. The case study demonstrates the possibility of making confident inferences (95%) for thousands of the corridor passengers. The case study also confirms intuitive correlation of the inferred strategies with variables such as travel distance, egress distance, time of day, and availability of countdown timers at the stop. Potential biases of the inference sample and possible applications in service planning are discussed.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AP050 Standing Committee on Bus Transit Systems.
• Authors:
  • Nassir, Neema
  • Zhao, Jinhua
  • Attanucci, John
  • Salvucci, Frederick
  • Wilson, Nigel
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: 4p

Subject/Index Terms

• TRT Terms: Arrivals and departures; Boarding; Bus stops; Bus transit operations; Express buses; Passengers; Real time information
• Uncontrolled Terms: Bayesian statistical inference
• Geographic Terms: Chicago (Illinois)
• Subject Areas: Operations and Traffic Management; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01660898
• Record Type: Publication
• Report/Paper Numbers: 18-05616
• Files: TRIS, TRB, ATRI
• Created Date: Feb 22 2018 9:17AM