Should I stay or should I go? Investigating route switching behavior from revealed preferences data

Abstract

A large variety of factors influence the route choice decisions of road users, and modelers consider these factors within the perceived utility that road users are assumed to maximize. However, this perceived utility may be different even for the same origin–destination pair and this leads road users to choose different routes for different trips. In this study, we focus on this particular phenomenon of route switching behavior by estimating discrete choice models with the aim of understanding the key factors at its foundation. The estimated route choice models account for route characteristics, socioeconomic information, activity based data, inertial mechanism and learning effects, and they are applied to revealed preference data consisting of 677 actual day by day route choices (referred to 77 road users) collected by GPS in Cagliari (Italy). Route switching models were estimated with both fixed and random coefficient models. The model estimation results show that the variables referred to habit and learning have an important relevance on explaining the route switching phenomenon. Specifically, the higher is the travel habit, the less is the propensity of the road users to switch their route. Moreover, the learning effect shows that the accumulation of past experiences has more influence on the choice than the most recent ones

Modeling Mode Choice of Air Passengers’ Ground Access to Brisbane Airport

“…This parameter is estimated for each SP alternative and can also be specified as a generic parameter. State dependency can also consider choice inertia by including travel habit in the utility specification (36,37). Vacca et al (37) investigated various specifications to include travel habits and found that inertia should be specified as the habit of repeating a similar choice in a series of SP experiments on the basis of previous choices: where h nik is equal to 1 if mode i is chosen by the respondent n in the previous choice tasks k, and 0 otherwise.…”

Analysing Spatial Intrapersonal Variability of Road Users Using Point-to-Point Sensor Data

“…Since habit and inertia also play a role in route switching behaviour (Vacca et al 2019), for example, it is crucial to be able to quantify the regularity of current behaviour if the network operators wish to affect change.…”

“…Intrapersonal variability in route choice has typically been undertaken on smartcard data (Kurauchi et al 2014;Kim et al 2017), although some small GPS surveys have also been undertaken to examine the degree of habit in route choices of road users (Vacca et al 2019). Route choice variability is usually calculated separately for each OD pair, which is useful for understanding service choices through a public transport network at an aggregate level but it does not generate a single measure for each traveller which could provide insights for a road network manager, for example.…”

Shortest paths, travel costs, and traffic

“…Expectations of travel time are based on inaccurate perceptions and faulty memories (Carrion and Levinson, 2019; Di et al., 2017; Meng et al., 2018; Parthasarathi et al., 2013; Peer et al., 2014). People consider known variability as well as mean travel times (Amirgholy and Gonzales, 2017; Levinson and Zhu, 2013; Liu et al., 2004; Vacca et al., 2019). There are errors in otherwise rational assessment, and there is stochasticity throughout the route choice process (Daganzo and Sheffi, 1977).…”