|With the long-lasting concerns of climate change, electric vehicles are widely accepted as a solution of environment-friendly transportation. However, range anxiety originated from the low-density but expensive nature of battery hinders the adoption of electric vehicles. In the case of taxis, especially, the aforementioned anxiety is even more significant as the daily distance run by a taxi is much more than the range allowed. Currently, we are doing research on deploying charging stations for electric taxis hoping to solve this range anxiety. This project, inspired by the undergoing research, aims at finding good charging station locations for electric taxis using taxis trajectory tracking data. The project is comprised of three parts. First, map matching the trajectories on the map, then infer the charging demand location based on the energy consumption profile of electric vehicles. However, we extend the constant electric vehicle consumption profile assumption to a more realistic energy consumption profile. Finally, the best location will be chosen according to reasonable criteria like weighted total distance to the closest charging station, etc.
Good knowledge of map matching, location-problem-solving algorithms, and discrete choice models is a plus.|