Thailand's EV Taxi Situation and Charging Station Locations

Keawthong, Pichamon; Muangsin, Veera

doi:10.1145/3458359.3458364

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…This is the same method as was used in the study conducted by Jung et al [29] but differs from the studies by Han et al [30] and Hu et al [32], which focused on empty areas or parking spaces in city areas as candidate sites. According to Keawthong and Muangsin [38], gas stations are suitable in terms of power supply, charger installation, good accessibility to main roads, adequate parking lots, and facilities for drivers while waiting to charge.…”

Section: Discussionmentioning

confidence: 99%

“…This would determine which one would reduce the overall system time by considering the travel time generated by the Google Map Distance Matrix API. However, Thailand has approximately 100 electric taxis at present, with the potential to increase to 4000 electric taxis in the next five years [38]. COVID-19 is a significant factor contributing to the slowdown in electric taxi expansion, as the number of tourists and taxi travel is decreasing.…”

Section: Conclusion 61 Research Conclusionmentioning

confidence: 99%

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

2022

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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.

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