Analysis of inertial choice behaviour based expected and experienced savings from a real-world route choice experiment

In the context of route choice, inertial behaviour shows that drivers make choices that are satisfactory rather than optimal. Consequently, drivers may not necessarily alter their choice when confronted with a travel time increase on the current choice or a travel time decrease of a choice alternative. As a consequence it can be assumed that driver only alter their choice when the utility difference between alternatives becomes larger than some individual-situation-specific threshold. Route choice data from a real-world driving experiment was used to study inertial behaviour and estimate inertia thresholds. The data analysis consisted of two parts. One addressing expected travel time savings relative to choice alternatives and another addressing experienced travel time savings on the current route. With regard to the former it was found that on average roughly one-fourth of the choices were inertial choices. Small travel time differences and dominant non-travel time route attributes had a positive effect on the frequency of inertial choices. Based on lost travel time, inertia thresholds up to 4.5 minutes or 30% of the average travel time were found, while thresholds up to 1.3 minutes or 13% were most common. Considerable differences between OD-pairs and routes indicated that thresholds are probabilistic and dependent on the choice set. Considering experienced savings, it was found that on average roughly half of the choices concerned inertial behaviour. Inertia thresholds up to 3.31 minutes or 37% of the average travel time were found, while thresholds up to 1.18 minutes or 11% were more common. Switching behaviour could not be explained by experienced savings as participants were much less responsive to experienced loss on the current route as opposed to expected loss relative to the choice alternative. Future research opportunities are: moving-average thresholds, inertia thresholds of different situations and driver behaviour types, asymmetry between gains and losses, impact of advanced traffic information systems, and estimation of a model that better matches the route choice data than a simple model that is based on travel time alone does