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硕士彭宁叶子,任福的论文在 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 刊出
发布时间:2022-01-05 10:21:34     发布者:易真     浏览次数:

标题: Urban Multiple Route Planning Model Using Dynamic Programming in Reinforcement Learning

作者: Peng, N (Peng, Ningyezi); Xi, YL (Xi, Yuliang); Rao, JM (Rao, Jinmeng); Ma, XY (Ma, Xiangyuan); Ren, F (Ren, Fu)

来源出版物: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS DOI: 10.1109/TITS.2021.3075221 提前访问日期: MAY 2021

摘要: With the development of the economy and the acceleration of urbanization, traffic congestion has become a worldwide problem. Advances in mobile Internet and sensor technologies have increased real-time data sharing, providing a new opportunity for urban route planning. However, due to the difficulty of handling complex global information, making correct decisions in large-scale and complex traffic environments is a problem that urgently needs to be solved. In this paper, a multiple route planning model (multi-route dynamic programming (DP) model) is proposed to solve the urban route planning problem with traffic flow information. In particular, we adopt the DP algorithm in this model, design a reward function suitable for urban path planning problems, and generate multiple routes based on the Q values. In addition, we design different scenarios using real-world road networks to test our model. Through the experiments, we demonstrate that our model has the potential to yield optimal results under large-scale scenarios with high efficiency. The advantages of integrating the distance contribution index (DCI) in the reward function are also elaborated. Moreover, our model can provide alternative routes to divert traffic from the optimal route, thus mitigating the congestion drift problem.

作者关键词: Roads; Planning; Reinforcement learning; Heuristic algorithms; Dynamic programming; Laboratories; Geographic information systems; Dynamic programming; model-based; multiple route planning reinforcement learning; route planning

地址: [Peng, Ningyezi; Xi, Yuliang; Ma, Xiangyuan; Ren, Fu] Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Peoples R China.

[Peng, Ningyezi; Xi, Yuliang; Ma, Xiangyuan; Ren, Fu] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China.

[Peng, Ningyezi] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China.

[Xi, Yuliang] Anhui Univ, Sch Internet, Hefei 231699, Peoples R China.

[Rao, Jinmeng] Univ Wisconsin, Dept Geog, GeoDS Lab, Madison, WI 53706 USA.

通讯作者地址: Ren, F (通讯作者),Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: ningyezi.peng@connect.polyu.hk; yuliangwhu@163.com; jinmeng.rao@wisc.edu; renfu@whu.edu.cn; maxiangyuan@whu.edu.cn

影响因子:6.492


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