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Forecasting with a joint mode/time-of-day choice model based on combined RP and SC data

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  • Lizana, Pedro
  • Ortúzar, Juan de Dios
  • Arellana, Julián
  • Rizzi, Luis I.

Abstract

The factors influencing trip departure time are taking more importance in urban planning practice since congestion is increasingly being addressed by travel demand management (TDM) strategies. In this paper we formulate and estimate a joint travel mode-departure time model for commuting trips using combined revealed preference (RP) and stated choice (SC) data. The RP data considered nine alternative modes and up to 11 time periods, and the level-of-service data were obtained at an unusual level of precision using GPS measurements. The travel time, cost and cost divided by the wage rate coefficients were fairly similar in both the RP and SC environments, suggesting equal error variances for both datasets. The only parameters that differed between each type of data were those associated with the schedule delay early (SDE) and late (SDL) variables required by Small’s Scheduling Model. This may be due to the potentially different temporal perspectives between RP choices (longer term decisions) and SC decisions, arguably shorter term given the nature of the experiment and the context presented in it (implementation of a congestion charging policy and a flexible working-hours scheme). The models were used to forecast the impacts of a hypothetic congestion charging scheme in Santiago, showing that the schedule delay coefficients derived from the SC context produced a smoother and less-peaked temporal distribution of travel demand than the RP parameters.

Suggested Citation

  • Lizana, Pedro & Ortúzar, Juan de Dios & Arellana, Julián & Rizzi, Luis I., 2021. "Forecasting with a joint mode/time-of-day choice model based on combined RP and SC data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 302-316.
    • Handle: RePEc:eee:transa:v:150:y:2021:i:c:p:302-316
    DOI: 10.1016/j.tra.2021.06.006
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    2. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.

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