A Dynamic Choice Model to Estimate the User Cost of Crowding with Large-Scale Transit Data

Prateek Bansal · Dániel Hörcher · Daniel J. Graham

24 citations0/0 files storedJournal of the Royal Statistical Society Series A (Statistics in Society)

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Transportation Planning and Optimization Economic and Environmental Valuation Transportation and Mobility Innovations

Abstract

Abstract Efficient mass transit provision should be responsive to the behaviour of passengers. Operators often conduct surveys to elicit passenger perspectives, but these can be expensive to administer and can suffer from hypothetical biases. With the advent of smart card and automated vehicle location data, operators have reliable sources of revealed preference (RP) data that can be utilized to estimate transit riders’ valuation of service attributes. To date, effective use of RP data has been limited due to modelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based on experience-based learning is also formulated. We develop an expectation–maximization algorithm to estimate the DCM and apply our model to estimate passenger valuation of crowding. Using large-scale data of 2 months with over four million daily trips by an Asian metro, our DCM estimates show an increase of 47% in passenger’s valuation of travel time under extremely crowded conditions. Furthermore, the average passenger follows the compensatory rule on only 25.5% or fewer trips. These results are valuable for supply-side decisions of transit operators.

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References

1994Stated preference analysis of travel choices: the state of practice 2001The Influence of Task Complexity on Consumer Choice: A Latent Class Model of Decision Strategy Switching 2009A choice prediction competition: Choices from experience and from description 2001A non-compensatory choice model incorporating attribute cutoffs 2010Twenty Years of Rail Crowding Valuation Studies: Evidence and Lessons from British Experience 2011Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies 2016Crowding cost estimation with large scale smart card and vehicle location data 2018Crowding valuation in urban tram and bus transportation based on smart card data

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2023I can board, but I’d rather wait: Active boarding delay choice behaviour analysis using smart card data in metro systems 2024Mode choice inertia and shock: Three months of almost fare-free public transport in Germany 2023Fare evasion in public transport: How does it affect the optimal design and pricing?2024Habits and the subexploration of better transportation options: A dual-system approach

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