{'fraction_non_alphanumeric': 0.022364217252396165, 'fraction_numerical': 0.0, 'mean_word_length': 5.392857142857143, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Data-driven Electric Vehicle Charging Station Placement for Incentivizing Potential Demand

It is believed that Electric Vehicles (EVs) will play an increasingly important role in making the city greener and smarter. However, a critical challenge raised by the transportation electrification process is the proper planning of city-wide EV charging infrastructures, i.e., the siting and sizing of charging stations, especially for the cities that just start promoting the adoption of EVs. In this paper, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed to promote the transition of EVs from traditional cars? We propose a δ-nearest model that captures people's satisfaction towards a certain design and formulate the EV charging station placement problem as a monotone submodular maximization problem, equipped with gridded population data and trip data. We then propose a greedy-based algorithm to solve the problem efficiently with a provable approximation ratio. A case study using fine-grained Haikou population data, Point of Interest (POI) data, and trip data is also provided to demonstrate the effectiveness of our approach.