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Deriving transport appraisal values from emerging revealed preference data

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  • Tsoleridis, Panagiotis
  • Choudhury, Charisma F.
  • Hess, Stephane

Abstract

Transport demand models are widely used to inform policy making and produce forecasts of future demand. A core output derived from demand models is the Value of Travel Time (VTT), which provides insights on the trade-offs that travellers are willing to make in terms of travel time and travel cost. VTT estimates are a critical input to cost-benefit analyses and feasibility assessments of potential projects and thus play a crucial role in transport planning and policy decisions. While much of the early work on VTT made use of revealed preference (RP) data, their use decreased due to growing concerns about reporting errors that may result in omitted observations and measurement errors in the model inputs. As a consequence, VTT measures have, for the last two decades, primarily been estimated using state-preference (SP) surveys. While SP methods can assess the individual trade-offs in a controlled manner, they are prone to behavioural incongruence. More recently, RP data from passively-collected data sources have raised the promise of accounting for some of the limitations of traditional RP surveys due to the minimal (or even no) active input from the respondent. The present study utilises such a dataset that combined a 2-week trip diary captured through smartphone GPS tracking with a household survey containing individual socio-demographic information. A mixed Logit model for mode choice was specified and the estimated parameters were then applied on the National Travel Survey to calculate the VTT estimates. Those estimates were further adjusted based on trip distances to get more representative national VTT values. This process resulted in estimates similar to the official UK guidelines used in transport appraisal that were obtained from SP data, where our results are not affected by concerns about response quality or survey artefacts. The findings hence strengthen the case for shifting towards passively generated RP data sources and are important for transport practitioners.

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  • Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    • Handle: RePEc:eee:transa:v:165:y:2022:i:c:p:225-245
    DOI: 10.1016/j.tra.2022.08.016
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    Cited by:

    1. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2023. "Probabilistic choice set formation incorporating activity spaces into the context of mode and destination choice modelling," Journal of Transport Geography, Elsevier, vol. 108(C).
    2. Heike Link & Dennis Gaus & Neil Murray & Maria Fernanda Guajardo Ortega & Flavien Gervois & Frederik von Waldow & Sofia Eigner, 2023. "Combining GPS Tracking and Surveys for a Mode Choice Model: Processing Data from a Quasi-Natural Experiment in Germany," Discussion Papers of DIW Berlin 2047, DIW Berlin, German Institute for Economic Research.
    3. Ruiz, Elkin & Yushimito, Wilfredo F. & Aburto, Luis & de la Cruz, Rolando, 2024. "Predicting passenger satisfaction in public transportation using machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).

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