01018675

Component

https://doi.org/10.3141/1923-22

http://scholar.google.com...+Xia&publication_year=2005

This study develops a dynamic bus arrival time prediction model using the data collected by the automatic vehicle location and automatic passenger counter systems. It is based on the Kalman filter algorithm with a two-dimensional state variable in which the prediction error in the most recent observation is used to optimize the arrival time estimate for each downstream stop. The impact of schedule recovery is considered as a control factor in the model to reflect the driver’s schedule recovery behavior. The algorithm performs well when tested with a set of automatic vehicle location-automatic passenger counter data collected from a real-world bus route. The algorithm does not require intensive computation or an excessive data preprocessing effort. It is a promising approach for real-time bus arrival time prediction in practice.

01018662

English

pp 208-217

2005

Transportation Research Record: Journal of the Transportation Research Board

030909397X

Print

Figures (8) ; References (15) ; Tables (2)

Algorithms; Bus transit operations; Dynamic models; Kalman filtering; Mathematical prediction; Real time data processing

Arrival time (Bus transit); Schedule recovery

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Feb 4 2006 8:02AM

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