Analyzing multiday route choice behavior of commuters using GPS data

In this study, accurate global position system and geographic information system data were employed to reveal multiday routes people used and to study multiday route choice behavior for the same origin–destination trips, from home to work. A new way of thinking about route choice modeling is provided in this study. Travelers are classified into three kinds based on the deviation between actual routes and the shortest travel time paths. Based on the classification, a two-stage route choice process is proposed, in which the first step is to classify the travelers and the second one is to model route choice behavior. After analyzing the characteristics of different types of travelers, an artificial neural network was adopted to classify travelers and model route choice behavior. An empirical study using global position systems data collected in Minneapolis–St Paul metropolitan area was carried out. It finds that most travelers follow the same route during commute trips on successive days. And different types of travelers have a significant difference in route choice property. The modeling results indicate that neural network framework can classify travelers and model route choice well.