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Enjoy the most beautiful scene now: a memetic algorithm to solve two-fold time-dependent arc orienteering problem

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Abstract

Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road networks. However, in real cases, especially in the era of smart cities where many kinds of transportation-related data become easily available, recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria, e.g., finding the route with the highest accumulated scenic score along (utility) while not exceeding the given travel time budget (cost). Such problem can be viewed as a variant of arc orienteering problem (AOP), which is well-known as an NP-hard problem. In this paper, targeting a more realistic AOP, we allow both scenic score (utility) and travel time (cost) values on each arc of the road network are time-dependent (2TD-AOP), and propose a memetic algorithm to solve it. To be more specific, within the given travel time budget, in the phase of initiation, for each population, we iteratively add suitable arcs with high scenic score and build a path fromthe origin to the destination via a complicate procedure consisting of search region narrowing, chromosome encoding and decoding. In the phase of the local search, each path is improved via chromosome selection, local-improvement-based mutation and crossoveroperations. Finally, we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively, and the experimental results demonstrate that it outperforms the baselines.

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Acknowledgments

The work was supported by the National Key Research and Development Project of China (2017YFB1002000), the National Natural Science Foundation of China (Grant Nos. 61602067 and 61872050), the Fundamental Research Funds for the Central Universities (2018cdqyjsj0024), the Chongqing Basic and Frontier Research Program (cstc2018jcyjAX0551), and the Frontier Interdisciplinary Research Funds for the Central Universities (106112017cdjqj188828). Chao Chen and Liping Gao contributed equally on this work.

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Authors and Affiliations

  1. School of Computer Science, Chongqing University, Chongqing, 400044, China

    Chao Chen, Liping Gao & Xuefeng Xie

  2. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhu Wang

Authors
  1. Chao Chen
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  2. Liping Gao
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  3. Xuefeng Xie
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  4. Zhu Wang
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Corresponding authors

Correspondence to Chao Chen or Xuefeng Xie.

Additional information

Chao Chen is a full professor at College of Computer Science, Chongqing University, China. He obtained his PhD degree from Pierre and Marie Curie University and Institut Mines-Télécom/Télécom SudParis, France in 2014. He received the BSc and MSc degrees in control science and control engineering from Northwestern Polytechnical University, China in 2007 and 2010, respectively. Dr. Chen got published over 80 papers including 20 ACM/IEEE Transactions. His work on taxi trajectory data mining was featured by IEEE Spectrum in 2011 and 2016 respectively. He was also the recipient of the Best Paper Runner-Up Award at MobiQuitous 2011.

In 2009, he worked as a research assistant with Hong Kong Polytechnic University, China. His research interests include pervasive computing, mobile computing, urban logistics, data mining from large-scale GPS trajectory data, and big data analytics for smart cities.

Liping Gao is currently a master student at College of Computer Science, Chongqing University, China. She obtained her bachelor degree from the College of Software Engineering of Chongqing University of Posts and Telecommunications, China in 2016. Her research interests include travel route planning, crowdsourced data mining for smart services.

Xuefeng Xie is a research assistant at the College of Computer Science, Chongqing University, China. She obtained her Master of Arts degree with the highest honor from the School of Media and Communication, University of Leeds, Leeds, UK. She received the Bachelor of Arts degree in film art from Sichuan Fine Art Institute, China in 2012. Her research interests include data visualization, aesthetic beauty, and quantitative analysis.

Zhu Wang is an associate professor of computer science at Northwestern Polytechnical University, China. He obtained his PhD degree in computer science from Northwestern Polytechnical University, China in 2013. His research interests include pervasive computing, mobile social network analysis, and mobile healthcare.

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Chen, C., Gao, L., Xie, X. et al. Enjoy the most beautiful scene now: a memetic algorithm to solve two-fold time-dependent arc orienteering problem. Front. Comput. Sci. 14, 364–377 (2020). https://doi.org/10.1007/s11704-019-8364-1

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  • DOI: https://doi.org/10.1007/s11704-019-8364-1

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