Investigation and Modeling on Drivers’ Route and Departure Time Choices from a Big Data Set of Floating Car Data

In this paper, a general analysis methodology aimed at processing a large set of Floating Car Data (FCD) – reconstructing the routes followed by the drivers and then clustering them to achieve suitable choice sets– is applied to a broad set of FCD collected in the metropolitan city of Rome over six months. Through the observation of about 10,000 trips, an analysis of Wardrop’s principle is carried out focused on the morning peak period: the results show that about 75% of the routes chosen by the users have travel times that exceed the minimum value by less than 35%, a value having the same magnitude of the average coefficient of variation of the observed link travel times, that is 24%. The possibility of modeling drivers’ route choice behavior among a set of similar routes is investigated, and different utility functional forms are defined and calibrated. The values of ρ
²
obtained are low, as expected considering that the drivers mostly perceive the routes that were actually chosen as equivalent alternatives. Nevertheless, the coefficients' values are statistically significant: results confirmed that length, travel time, and traffic lights represent three attributes that affect the path choice mechanism with a probability of 95%. Finally, the users’ process to improve their choice is also investigated, and the day-to-day route and departure time choice processes are analyzed to verify the possible existence of a correlation between observed changes and possible delays experienced by the users in the days before the change: for travel time increases or reductions between 5 and 20 minutes, a correlation has been identified with the number of route changes.