Online operations of automated electric taxi fleets: An advisor-student reinforcement learning framework
Online operations of automated electric taxi fleets: An advisor-student reinforcement learning framework
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DOI：
10.1016/j.trc.2020.102844
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
He F.
中科院分区：
文献类型：
--
作者：
Tang X.;Li M.;Lin X.;He F.
Automation and electrification are inevitable trends in the development of intelligent vehicles. It is envisioned that automated electric taxis (AETs) will play an important role in future transportation systems for serving personalized travel demands. To tackle the operational challenges caused by the high spatiotemporal heterogeneity of customer demands entails novel online strategy to intelligently manage AET fleet. This study proposes an advisor-student reinforcement learning framework to solve the online operations problem of AET fleet through which the taxis are intelligently assigned to serve demands, dispatched to zones with excessive future demands, and forced to get refueled at charging stations. Extensive numerical experiments illustrate the advantages of the proposed framework over myopic and nearest distance greedy strategies, especially when vehicle relocation is highly needed.
