Assessment of the transit ridership prediction errors using AVL/APC data
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Assessment of the transit ridership prediction errors using AVL/APC data You‑Jin Jung1 · Jeffrey M. Casello2
© Springer Science+Business Media, LLC, part of Springer Nature 2019
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. Keywords Travel demand prediction errors · AVL/APC data · Transit stop activities · Calibration of mode choice models · Transit ridership forecasting
* You‑Jin Jung [email protected] Jeffrey M. Casello [email protected] 1
Department of Civil and Environmental Engineering, University of Waterloo, 200 University Ave. West, Waterloo, ON N2L 3G1, Canada
2
School of Planning and Department of Civil and Environmental Engineering, University of Waterloo, 200 University Ave. West, Waterloo, ON N2L 3G1, Canada
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Transportation
Introduction Transit is widely recognized as a viable option supporting sustainability paradigm providing benefits such as reducing air pollution, alleviating traffic congestion, and enhancing mobility. Well-planned and well-implemented transit systems may support social wellbeing, for instance, by serving people who have fewer transportation choices and by promoting health through walk- and bike-access to transit. Given the emphasis on public transportation, many transit investments have been evaluated using travel forecasting mod
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