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An Open-Source Framework to Implement Kalman Filter Bus Arrival Predictions

Author

Listed:
  • Sean Óg Crudden

    (The Time Keepers)

  • Simon Berrebi

    (The Time Keepers
    Georgia Institute of Technology)

Abstract

The accuracy of vehicle arrival predictions affects every aspect of transit performance including ridership, reliability, and operating costs. Kalman Filter algorithms have been shown to provide more accurate predictions than simple regression. This paper presents a scalable framework to implement Kalman Filters on an entire bus network running live. A novel architecture to cache the data and weight inputs based on current operating conditions is presented. All the necessary features to support Kalman Filter predictions are described and implemented in TheTransitClock, an open-source prediction tool. TheTransitClock was deployed on the Metro Transit bus network in the Minneapolis-St. Paul region for one month. The Kalman Filter algorithm predicted the arrivals of over 900 buses on 167 routes. The accuracy and sensitivity of the method was compared to a schedule-based prediction method used in practice. The Kalman Filter was found to provide more accurate and stable predictions, especially in times and places that are difficult to predict with conventional methods.

Suggested Citation

  • Sean Óg Crudden & Simon Berrebi, 2023. "An Open-Source Framework to Implement Kalman Filter Bus Arrival Predictions," Networks and Spatial Economics, Springer, vol. 23(2), pages 429-443, June.
    • Handle: RePEc:kap:netspa:v:23:y:2023:i:2:d:10.1007_s11067-021-09541-w
    DOI: 10.1007/s11067-021-09541-w
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    References listed on IDEAS

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