Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 5
1298-1302

Mining fastest route using taxi drivers’ experience via constrained deep reinforcement learning

Huang Min
Mao Feng
Qian Yuxiang
Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent System Engineering, Sun Yat-sen University, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2018.10.0810

Abstract

This paper proposed constrained deep reinforcement learning(CDRL) to compute the fastest route online using taxi drivers' experience in different time period. Firstly, this paper described the extraction of experiential road segment database(ERSD). Then it introduced CDRL method, which mainly comprised of two phase: bounded condition of route and deep Q-learning algorithm. In the first phase, the task was to generate alternative constrained road segments of OD pair. In the se-cond phase, it devised deep Q-learning algorithm to learning the experience of taxi drivers, and computed the fastest route of a given OD according to their departure time. Lastly, this paper tested an empirical studies in CBD of Guangzhou. The results show that the routes computed by CDRL method is approximately equal to shortest route(SR) and fastest route(FR) method in travel time and route length. Furthermore, the CDRL method notably outperforms FR and SR in computing efficiency, so it is more suitable for online fastest route computation.

Foundation Support

国家自然科学基金资助项目(U1611461,11574407)
广东省科技计划项目(2016A020223006)
中央高校基本科研业务费专项资金资助项目(17lgjc42)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.10.0810
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 5
Section: Algorithm Research & Explore
Pages: 1298-1302
Serial Number: 1001-3695(2020)05-003-1298-05

Publish History

[2020-05-05] Printed Article

Cite This Article

黄敏, 毛锋, 钱宇翔. 基于出租车司机经验的约束深度强化学习算法路径挖掘 [J]. 计算机应用研究, 2020, 37 (5): 1298-1302. (Huang Min, Mao Feng, Qian Yuxiang. Mining fastest route using taxi drivers’ experience via constrained deep reinforcement learning [J]. Application Research of Computers, 2020, 37 (5): 1298-1302. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)