Pappelis, Dimitrios; Kamargianni, Maria; Chaniotakis, Emmanouil; (2022) Dynamic Travel Behaviour: Findings of a Stated Adaptation Experiment and Modelling Framework. In: Proceedings of the 16th International Conference on Travel Behaviour Research (ICTBR 2022). International Association for Travel Behaviour Research (IATBR): Santiago, Chile.

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Abstract

The evolution of advanced, traveller information and the increased availability of travel alternatives - especially in urban settings- are altering the travel decision making process. While still constrained by unforeseen events (exogenous, such as weather and car malfunction or endogenous, such as congestion), individuals can better plan and subsequently act in a dynamic setting (1). In adverse conditions (e.g. ineffective short-term rescheduling, repeatedly increased travel time), individuals might evaluate their options based on real-time information and their accumulated experience (2), or even consider day-to-day and longterm adaptations. For instance, in the case of road closure, a traveller may make both short-term adaptations by modifying choice facets of his trip (e.g., departure time, mode, route), and/or account for this travel time fluctuation and accumulated experience when creating his future daily schedule (e.g. frequency of activity, work from home). Ultimately, individuals are found to progress towards a stable state (3) and (re)develop a set of day-to-day travel habits through the reinforcement of specific strategies. The re-evaluation process described above is highly dynamic and often overlooked due to its high complexity, both in terms of data collection and modelling. The former requires panel data which are hard and costly to collect, with the literature thus focusing on -limited- stated adaptation experiments performed (4). The latter is usually based on the independent estimation of a set of models capturing aspects of the multilevel decision in a sequential fashion or loose coupling through the iterative feedback of inputs and outputs (5-6). This paper aims to contribute to existing literature of travel decision making by, a) introducing a joint revealed and stated preference (RP-SP) adaptation experimentthat captures within-day re-evaluation and dayto-day learning, tested with a panel of 282 individuals (3240 observations) in the Turin region (IT); b) proposing a modelling framework for the integration of travel behaviour dynamicity across temporal levels.

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