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With recent advances in battery technology and the resulting decrease in thecharging times, public charging stations are becoming a viable option forElectric Vehicle (EV) drivers. Concurrently, wide-spread use oflocation-tracking devices in mobile phones and wearable devices makes itpossible to track individual-level human movements to an unprecedented spatialand temporal grain. Motivated by these developments, we propose a novelmethodology to perform data-driven optimization of EV charging stationslocation. We formulate the problem as a discrete optimization problem on ageographical grid, with the objective of covering the entire demand regionwhile minimizing a measure of drivers' discomfort. Since optimally solving theproblem is computationally infeasible, we present computationally efficient,near-optimal solutions based on greedy and genetic algorithms. We then applythe proposed methodology to optimize EV charging stations location in the cityof Boston, starting from a massive cellular phone data sets covering 1 millionusers over 4 months. Results show that genetic algorithm based optimizationprovides the best solutions in terms of drivers' discomfort and the number ofcharging stations required, which are both reduced about 10 percent as comparedto a randomized solution. We further investigate robustness of the proposeddata-driven methodology, showing that, building upon well-known regularity ofaggregate human mobility patterns, the near-optimal solution computed usingsingle day movements preserves its properties also in later months. Whencollectively considered, the results presented in this paper clearly indicatethe potential of data-driven approaches for optimally locating public chargingfacilities at the urban scale.

Optimizing the Deployment of Electric Vehicle Charging Stations Using Pervasive Mobility Data