结合轨迹挖掘和强化学习的互联电动出租车深度在线推荐

IF 4.3 1区地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Geographical Information Science Pub Date : 2023-11-15 DOI:10.1080/13658816.2023.2279969

Wei Tu, Haoyu Ye, Ke Mai, Meng Zhou, Jincheng Jiang, Tianhong Zhao, Shengao Yi, Qingquan Li

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引用次数: 0

摘要

在城市环境中，车队管理的优化日益引起人们的兴趣。然而，对电动出租车车队的综合优化问题的研究却很少。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning

There is a growing interest in the optimization of vehicle fleets management in urban environments. However, limited attention has been paid to the integrated optimization of electric taxi fleets a...

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来源期刊

International Journal of Geographical Information Science 地学-计算机：信息系统

CiteScore

11.00

自引率

7.00%

发文量

审稿时长

9 months

期刊介绍： International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.