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Assessment of the transit ridership prediction errors using AVL/APC data

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Abstract

The disparity between actual and forecasted transit ridership has been an important area of study and a concern for researchers for several decades. In order to decrease the discrepancy caused by model property errors, a number of studies focus on better representation of difficult-to-measure cost functions and incorporation of behavioral variables in mode choice models. In spite of the improvement, some gaps still remain in practical applications, particularly for large-scale regional travel forecasting models which are zone-based and aggregated. With automated data collection systems including Automatic Vehicle Location/Automatic Passenger Count (AVL/APC), modellers have great potential to use these technologies as new or complementary data sources to reliably estimate system performances and observed transit ridership. In particular, an opportunity exists to explore model prediction errors at a more disaggregate spatial scale. In this paper, using AVL/APC data, a method to effectively identify and evaluate the source of transit ridership prediction errors is proposed. Multinomial regression models developed in this research produce equations for mode choice prediction errors as a function of: measurable but omitted market segmentation variables in current mode choice utility functions; and newly quantifiable attributes with new data sources or techniques including quality of service variables. Further, the proposed composite index can systematically evaluate and prioritize the major source of prediction errors by quantifying total magnitudes of prediction error and a possible error component. The outcomes of the research can serve as foundation towards more reliable and accurate mode choice models and ultimately enhanced transit travel forecasting.

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(Sources of variables: Outwater et al. 2011; Lutin et al. 2008; Kittelson & Associates Inc 2003a)

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Notes

  1. The WRT model generates transit trip prediction outputs for AM peak period defined as 7–8 a.m. As reliability is examined as a possible factor that could affect the AM peak prediction errors, transit on-time performance is also estimated during the same period of time.

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Acknowledgements

The authors are grateful to the Regional Municipality of Waterloo for their support of this research.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada

    You-Jin Jung

  2. School of Planning and Department of Civil and Environmental Engineering, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada

    Jeffrey M. Casello

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  1. You-Jin Jung
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Contributions

Study design and direction: YJ and JC. Data collection, interpretation, and analysis: YJ. Manuscript drafting: YJ. Manuscript review and editing: JC. All authors approved the final manuscript.

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Correspondence to You-Jin Jung.

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Jung, YJ., Casello, J.M. Assessment of the transit ridership prediction errors using AVL/APC data. Transportation 47, 2731–2755 (2020). https://doi.org/10.1007/s11116-019-09985-7

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