Incorporating recharging and cruising recommendation for urban taxies leveraging massive trajectories

Authors: Ke Mai, Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University; The Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Wei Tu*, Shenzhen University, Yatao Zhang, Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University; The Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Yang Yue, Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University; The Guangdong Key Laboratory of Urban Informatics, Shenzhen University
Topics: Transportation Geography
Keywords: Taxi recommendation, taxi trajectories, action based tree
Session Type: Paper
Day: 4/10/2020
Start / End Time: 11:10 AM / 12:25 PM
Room: Plaza Ballroom E, Sheraton, Concourse Level
Presentation File: No File Uploaded


The electric vehicle (EV) is for future green transportation. But EVs still face great challenges, including short driving range, long charging time, and few charging stations. Especially, these characteristics of EV hamper its acceptability to taxi drivers. Leveraging massive taxi GPS trajectories, this study presents a novel spatial-temporal route recommendation system for E-taxi driver by the cruising on the road and the recharging at the station. Taxi travel knowledge, including the probability of picking-up passengers and the distribution of client destination, is learned from raw fuel taxi GPS trajectories. Considering the cascading effect of route decision-making, consecutive actions of an individual ET driver is modeled by an action tree. The expected net revenue of the E-Taxi actions is estimated with the learned knowledge by incorporating cruising on the road and charging at the station. An online recommendation prototype system is developed for high-efficient real-time route recommendations, i.e., going to a charging station, cruising on some roads, etc. An experiment using real-world GPS trajectories of 16,146 fuel taxies is conducted or the performance evaluation. Results show that the net revenue of the ET drivers is up to 91.4 \% better than real-world fuel taxi drivers. The presented approach not only increases the avenue of ET drivers but also improves the EV viability.

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