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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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References

  1. Chen C, Zhang D, Ma X, Guo B, Wang L, Wang Y, Sha E. Crowddeliver: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(6): 1478–1496

    Google Scholar 

  2. Delling D, Goldberg A V, Pajor T, Werneck R F. Customizable route planning in road networks. Transportation Science, 2015, 51(2): 566–591

    Article  Google Scholar 

  3. Funke S, Storandt S. Personalized route planning in road networks. In. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2015, 45

    Google Scholar 

  4. Wang Z, Guo B, Yu Z, Zhou X. Wi-Fi CSI-based behavior recognition: from signals and actions to activities. IEEE Communications Magazine, 2018, 56(5): 109–115

    Article  Google Scholar 

  5. Guo B, Zhang D, Yu Z, Liang Y, Wang Z, Zhou X. From the internet of things to embedded intelligence. World Wide Web, 2013, 16(4): 399–420

    Article  Google Scholar 

  6. Castro P S, Zhang D, Chen C, Li S, Pan G. From taxi GPS traces to social and community dynamics: a survey. ACM Computing Surveys (CSUR), 2013, 46(2): 17

    Article  Google Scholar 

  7. Li X, Pan G, Wu Z, Qi G, Li S, Zhang D, Zhang W, Wang Z. Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science, 2012, 6(1): 111–121

    MathSciNet  Google Scholar 

  8. Zhang D, Guo B, Yu Z. The emergence of social and community intelligence. Computer, 2011, 44(7): 21–28

    Article  Google Scholar 

  9. Zhang D, Sun L, Li B, Chen C, Pan G, Li S, Wu Z. Understanding taxi service strategies from taxi GPS traces. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 123–135

    Article  Google Scholar 

  10. Wang L, Guo B, Yang Q. Smart city development with urban transfer learning. Computer, 2018, 51(12): 32–41

    Article  Google Scholar 

  11. Quercia D, Schifanella R, Aiello L M. The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. In. Proceedings of the 25th ACM conference on Hypertext and Social Media. 2014, 116-125

    Google Scholar 

  12. Galbrun E, Pelechrinis K, Terzi E. Urban navigation beyond shortest route: the case of safe paths. Information Systems, 2016, 57: 160–171

    Article  Google Scholar 

  13. Chen C, Chen X, Wang L, Ma X, Wang Z, Liu K, Guo B, Zhou Z. MASSR: a memetic algorithm for skyline scenic routes planning leveraging heterogeneous user-generated digital footprints. IEEE Transactions on Vehicular Technology, 2017, 66(7): 5723–5736

    Article  Google Scholar 

  14. Runge N, Samsonov P, Degraen D, Schöning J. No more autobahn!: scenic route generation using googles street view. In. Proceedings of the 21st International Conference on Intelligent User Interfaces. 2016, 147-151

    Chapter  Google Scholar 

  15. Zheng Y T, Yan S, Zha Z J, Li Y, Zhou X, Chua T S, Jain R. GPSView: a scenic driving route planner. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2013, 9(1): 3

    Google Scholar 

  16. Lu Y, Jossé G, Emrich T, Demiryurek U, Renz M, Shahabi C, Schubert M. Scenic routes now: effciently solving the time-dependent arc orienteering problem. In. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017, 487-496

    Google Scholar 

  17. Liang H, Wang K. Top-k route search through submodularity modeling of recurrent poi features. In. Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018, 545-554

    Google Scholar 

  18. Taylor K, Lim K H, Chan J. Travel itinerary recommendations with must-see points-of-interest. In. Proceedings of the International World Wide Web Conference. 2018, 1198-1205

    Google Scholar 

  19. Hsueh Y L, Huang H M. Personalized itinerary recommendation with time constraints using GPS datasets. Knowledge & Information Systems, 2018, 6: 1–22

    Google Scholar 

  20. Chen C, Jiao S, Zhang S, Liu W, Feng L, Wang Y. TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(10): 3292–3304

    Article  Google Scholar 

  21. Wang L, Yu Z, Guo B, Yi F, Xiong F. Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective. Frontiers of Computer Science, 2018, 12(2): 231–244

    Article  Google Scholar 

  22. Wang L, Zhang D, Wang Y, Chen C, Han X, M’hamed A. Sparse mobile crowdsensing: challenges and opportunities. IEEE Communications Magazine, 2016, 54(7): 161–167

    Article  Google Scholar 

  23. Demiryurek U, Banaei-Kashani F, Shahabi C, Ranganathan A. Online computation of fastest path in time-dependent spatial networks. In. Proceedings of International Symposium on Spatial and Temporal Databases. 2011, 92-111

    Chapter  Google Scholar 

  24. Mei Y, Salim FD, Li X. Effcient meta-heuristics for the multi-objective time-dependent orienteering problem. European Journal of Operational Research, 2016, 254(2): 443–457

    Article  MathSciNet  MATH  Google Scholar 

  25. Chen C, Chen X, Wang Z, Wang Y, Zhang D. ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints. Frontiers of Computer Science, 2017, 11(1): 61–74

    Article  Google Scholar 

  26. Golberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addion Wesley, 1989

    Google Scholar 

  27. Li Y, Yiu M L. Route-saver: leveraging route apis for accurate and efficient query processing at location-based services. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(1): 235–249

    Article  Google Scholar 

  28. Seresinhe C I, Moat H S, Preis T. Quantifying scenic areas using crowdsourced data. Environment and Planning B: Urban Analytics and City Science, 2017, 45(3): 567–582

    Google Scholar 

  29. Wang W, Xiao L, Zhang J, Yang Y, Tian P, Wang H, He X. Potential of Internet street-view images for measuring tree sizes in roadside forests. Urban Forestry & Urban Greening, 2018, 35: 211–220

    Article  Google Scholar 

  30. Gunawan A, Lau H C, Vansteenwegen P. Orienteering problem: a survey of recent variants, solution approaches and applications. European Journal of Operational Research, 2016, 255(2): 315–332

    Article  MathSciNet  MATH  Google Scholar 

  31. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G, Vathis N. Approximation algorithms for the arc orienteering problem. Information Processing Letters, 2015, 115(2): 313–315

    Article  MathSciNet  MATH  Google Scholar 

  32. Feillet D, Dejax P, Gendreau M. The profitable arc tour problem: solution with a branch-and-price algorithm. Transportation Science, 2005, 39(4): 539–552

    Article  Google Scholar 

  33. Verbeeck C, Vansteenwegen P, Aghezzaf E H. An extension of the arc orienteering problem and its application to cycle trip planning. Transportation Research Part E Logistics & Transportation Review, 2014, 68(4): 64–78

    Article  Google Scholar 

  34. Hsieh H P, Li C T. Mining and planning time-aware routes from checkin data. In. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. 2014, 481-490

    Google Scholar 

  35. Vansteenwegen P, Souffriau W, Van Oudheusden D. The orienteering problem: a survey. European Journal of Operational Research, 2011, 209(1): 1–10

    Article  MathSciNet  MATH  Google Scholar 

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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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