Extracting travel patterns from floating car data to identify electric mobility needs: A case study in a metropolitan area

The main objective of this paper is to identify the potential of electric vehicles in current car mobility scenarios. Firstly, distances traveled daily are analyzed to understand if the car usage observed can be satisfied by the expected range of electric vehicles. Secondly, idle times between trips are studied to assess vehicle needs and identify the requirements for electric charging stations to support the trip-chains observed. The datasets were derived from floating car data recorded for 365 days and include more than 30 million trips crossing the Metropolitan City of Turin (Italy). Approximately 70,000 km were observed daily for more than 10,000 vehicles for 400 different vehicle models to identify their activities over 24 hours. This daily activity in the observation period can be considered a reference scenario, in synergy with the battery range, to plan charging points in road networks. Results show that 98% of daily VKT (vehicle kilometers traveled) are lower than 300 km, over a year of observation. Cars are also classified according to their market segment to identify specific vehicle usage, defining a data dictionary to relate the models and segments. For instance, daily VKT values estimated for segment A (city cars) average 34 km, whereas for segment E (executive cars) the average is 75 km. The spatial analysis of idle times reveals a higher number of shorter breaks in the city center compared to peripheral districts, suggesting that recharging solutions should be adapted to zones according to how they are used for parking.