Location Selection of Charging Stations for Electric Taxis: A Bangkok Case

Abstract

The transition from ICE to BEV taxis is one of the most important methods for reducing fossil fuel consumption and air pollution in cities such as Bangkok. To support this transition, an adequate number of charging stations to cover each area of charging demand must be established. This paper presents a data-driven process for determining suitable charging locations for BEV taxis based on their characteristic driving patterns. The location selection process employs GPS trajectory data collected from taxis and the locations of candidate sites. Suitable locations are determined based on estimated travel times and charging demands. A queueing model is used to simulate charging activities and identify an appropriate number of chargers at each station. The location selection results are validated using data from existing charging services. The validation results show that the proposed process can recommend better locations for charging stations than current practices. By using the traveling time data that take the current traffic condition into account, e.g., via Google Maps API, we can minimize the overall travel time to charging stations of the taxi fleet better than using the distance data. This process can also be applied to other cities.

The Potential of Battery Electric Taxis in Santiago de Chile

“…Since addressing this issue will significantly increase the complexity of the procedure, future work should consider these specific cases in a computationally efficient way. In addition, two recent works (see [31,32]) have applied data-oriented techniques to locate charging stations. Thus, another line for future work will involve the development of optimization models with data-oriented models to localize the minimum number of charging stations and the type of charging power at each station, to optimize the number of feasible trips.…”

Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand

“…Each charging station is treated as an agent, and they introduce a shared meta generator to create tailored dynamic pricing policies for a wide range of agents based on extracted meta characteristics. In addition, different studies have been conducted in the literature for different countries and cities to select charging station locations for EVs [14][15][16][17][18].…”

Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data

“…A vehicle's GNSS trajectory is a record of the vehicle's path, containing rich road information (e.g., lanes, turns, speed limits, road widths, and road intersections) that directly reflects the road network's geometric characteristics and provides a new database for road intersection extraction [8,9]. Therefore, an increasing number of scholars are utilizing vehicle GNSS trajectory data in tandem with machine learning to extract road geometry data and examine vehicle behavior, among other applications [10][11][12][13][14][15][16] The traditional road intersection detection algorithm takes the vehicle trajectory's unique turning information and speed information at the road intersection as the benchmark, extracts the turning points after ensuring the intersection's accuracy, and then extracts the road intersection on the basis of the turning points' clustering. Qixing developed a scale-and orientation-invariant traj-SIFT feature to localize and recognize junctions using a supervised learning framework [17].…”