Bus Arrival Time Prediction: A Spatial Kalman Filter Approach

Public transport buses have uncertainties associated with its arrival/travel times, due to several factors such as signals, dwell times at bus stops, seasonal variations, fluctuating travel demands, and so on. In the developing world, these uncertainties are further magnified by the presence of excess vehicles, diverse modes of transport, and acute lack of lane discipline. Hence, the problem of bus arrival time prediction continues to be a challenging one especially in developing countries. This paper proposes a new methodology for bus arrival time prediction in real time. Unlike existing approaches, the proposed method explicitly learns the spatial (and temporal) correlations/patterns of traffic in a novel fashion. Specifically, it first detects the unknown order of spatial dependence and then learns linear, non-stationary spatial correlations for this detected order. It learns temporal correlations between successive trips as a function of their time difference. To make the optimal prediction feasible, the learnt predictive model is rewritten in a suitable linear state-space form, and then, an appropriate Kalman filter (KF) is applied. The performance was evaluated with real field data and compared with existing methods.

• Record URL:
  https://doi.org/10.1109/TITS.2019.2909314
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Achar, Avinash
  • Bharathi, Dhivya
  • Kumar, Bachu Anil
  • Vanajakshi, Lelitha Devi
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1298-1307
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 21
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Bus transit; Kalman filtering; Mathematical prediction; On time performance; Time series; Travel time
• Geographic Terms: Chennai (India)
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01737791
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Apr 24 2020 5:29PM