Mode choice with latent availability and consideration: Theory and a case study

Chiara Calastri, Stephane Hess, Charisma Choudhury, Andrew Daly and Lorenzo Gabrielli

Transportation Research Part B: Methodological, 2019, vol. 123, issue C, 374-385

Abstract: Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high costs associated with it. On the other hand, many other data sources exist in which respondents’ movements are tracked. These data sources have thus far been underexploited for choice modelling. Indeed, although some information on the chosen mode and basic socio-demographic data is collected in such surveys, they (as well as in fact also some purpose collected surveys) lack information on mode availability and consideration. This paper addresses the data challenges by estimating a mode choice model with probabilistic availability and consideration, using a secondary dataset consisting of ‘annotated’ GPS traces. Stated mode availability by part of the sample enabled the specification of an availability component, while the panel nature of the data and explicit incorporation of spatial and environmental factors enabled estimation of latent trip specific consideration sets. The research thus addresses an important behavioural issue (explicit modelling of availability and choice set) in addition to enriching the data for choice modelling purposes. The model produces reasonable results, including meaningful value of travel time (VTT) measures. Our findings further suggest that a better understanding of mode choices can be obtained by looking jointly at availability, consideration and choice.

Date: 2019
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DOI: 10.1016/j.trb.2017.06.016

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