Electric Taxi Charging Load Prediction Based on Trajectory Data and Reinforcement Learning—A Case Study of Shenzhen Municipality

Xiaojia Liu, Bowei Liu, Yunjie Chen, Yuqin Zhou and Dexin Yu ()
Additional contact information
Xiaojia Liu: College of Navigation, Jimei University, Xiamen 361021, China
Bowei Liu: College of Navigation, Jimei University, Xiamen 361021, China
Yunjie Chen: College of Navigation, Jimei University, Xiamen 361021, China
Yuqin Zhou: College of Navigation, Jimei University, Xiamen 361021, China
Dexin Yu: College of Navigation, Jimei University, Xiamen 361021, China

Sustainability, 2024, vol. 16, issue 4, 1-26

Abstract: In order to effectively solve the problem of electric taxi charging load prediction and reasonable charging behaviour discrimination, in this paper, we use taxi GPS trajectory data to mine the probability of operation behaviour in each area of the city, simulate the operation behaviour of a day by combining it with reinforcement learning ideas, obtain the optimal operation strategy through training, and count the spatial and temporal distributions and power values at the time of charging decision making, so as to predict the charging load of electric taxis. Experiments are carried out using taxi travel data in Shenzhen city centre. The results show that, in terms of taxi operation behaviour, the operation behaviour optimized by the DQN algorithm shows the optimal effect in terms of the passenger carrying time, mileage, and daily net income; in terms of the charging load distribution, the spatial charging demand of electric taxis in each area shows obvious differences, and the charging demand load located in the city centre area and close to the traffic hub is higher. In time, the peak charging demand is distributed around 3:00 to 4:00 and 14:00 to 15:00. Compared with the operating habits of drivers based on the Monte Carlo simulation, the DQN algorithm is able to optimise the efficiency and profitability of taxi drivers, which is more in line with the actual operating habits of drivers formed through accumulated experience, thus achieving a more accurate charging load distribution.

Keywords: electric taxi; trajectory data; spatio-temporal distribution; reinforcement learning; charging loads (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/4/1520/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/4/1520/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:4:p:1520-:d:1337251

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().