Prateek Bansal, Daniel Hörcher, Daniel J. Graham
arXiv 7 Jul 2020 · Statistics — Applications · 4 citations (OpenAlex)
arXiv:2007.03682 · PDF · DOI · OpenAlex · Extracted main text
Crowding valuation of subway riders is an important input to various supply-side decisions of transit operators. The crowding cost perceived by a transit rider is generally estimated by capturing the trade-off that the rider makes between crowding and travel time while choosing a route. However, existing studies rely on static compensatory choice models and fail to account for inertia and the learning behaviour of riders. To address these challenges, we propose a new dynamic latent class model (DLCM) which (i) assigns riders to latent compensatory and inertia/habit classes based on different decision rules, (ii) enables transitions between these classes over time, and (iii) adopts instance-based learning theory to account for the learning behaviour of riders. We use the expectation-maximisation algorithm to estimate DLCM, and the most probable sequence of latent classes for each rider is retrieved using the Viterbi algorithm. The proposed DLCM can be applied in any choice context to capture the dynamics of decision rules used by a decision-maker. We demonstrate its practical advantages in estimating the crowding valuation of an Asian metro's riders. To calibrate the model, we recover the daily route preferences and in-vehicle crowding experiences of regular metro riders using a two-month-long smart card and vehicle location data. The results indicate that the average rider follows the compensatory rule on only 25.5% of route choice occasions. DLCM estimates also show an increase of 47% in metro riders' valuation of travel time under extremely crowded conditions relative to that under uncrowded conditions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hörcher, D., Graham, D. J., and Anderson, R. J (2017) Crowding cost estimation with large scale smart card and vehicle location data self | 1.000 | 6 | 3 | 100% |
| 2 | Zarwi, F. E., Vij, A., and Walker, J (2017) Modeling and forecasting the evolution of preferences over time: A hidden markov model of travel behavior | 0.737 | 3 | 2 | 100% |
| 3 | Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T (2002) A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking | 0.644 | 2 | 2 | 100% |
| 4 | Bansal, P., Hurtubia, R., Tirachini, A., and Daziano, R. A (2019) Flexible estimates of heterogeneity in crowding valuation in the new york city subway self | 0.644 | 2 | 2 | 100% |
| 5 | Batarce, M., Muñoz, J. C., de Dios Ortúzar, J., Raveau, S., Mojica,… (2015) Use of mixed stated and revealed preference data for crowding valuation on public transport in santiago, chile | 0.644 | 2 | 2 | 100% |
| 6 | Kroes, E., Kouwenhoven, M., Debrincat, L., and Pauget, N (2014) Value of crowding on public transport in le-de-france, france | 0.644 | 2 | 2 | 100% |
| 7 | Netzer, O., Ebbes, P., and Bijmolt, T. H (2017) Hidden markov models in marketing | 0.644 | 2 | 2 | 100% |
| 8 | Tang, Y., Gao, S., and Ben-Elia, E (2017) An exploratory study of instance-based learning for route choice with random travel times | 0.644 | 2 | 2 | 100% |
| 9 | Wardman, M. and Whelan, G (2011) Twenty years of rail crowding valuation studies: evidence and lessons from british experience | 0.644 | 2 | 2 | 100% |
| 10 | Jou, R.-C. and Chen, K.-H (2013) An application of cumulative prospect theory to freeway drivers’ route choice behaviours | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 38 scored citations.