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Personalized route recommendation through historical travel behavior analysis

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Abstract

Popular navigation applications and services optimize routes based on either distance or time, disregarding drivers’ preferences when suggesting routes. Various unknown circumstances may affect users’ travel behaviors between two locations on the road network, hence it is complicated to provide satisfactory personalized route recommendations. In this paper, it is believed that users’ travel behaviors are implicitly reflected and can be learned from their historical Global Positioning System (GPS) trajectories. The Behavior-based Route Recommendation (BR2) method is proposed to compute personalized routes based exclusively on users’ travel preferences. The concepts of appearance and transition behaviors are defined to describe users’ travel behaviors. The behaviors are extracted from users’ past travels and the missing behaviors, of unvisited locations, are estimated with the Optimized Random Walk with Restart technique. Furthermore, the temporal dependency of travel behaviors is considered by constructing a time difference interval histogram. A behavior graph is generated to allow the maximum probability route computation with the shortest path algorithm, resulting in the most likely route to be taken by a user. An extension is proposed, named BR2+, to better consider the temporal dependency and incorporate distance in the recommendation process. Experiments conducted on two real GPS trajectory data sets demonstrate the efficiency and effectiveness of the proposed method. In addition, a web-based geographic information system (GIS) called MPR is implemented to demonstrate differences in route recommendation when time, distance, or users’ preferences are considered, besides providing insight about users’ movement through data visualization of their spatial and temporal coverage.

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Notes

  1. https://vuejs.org.

  2. https://vuetifyjs.com.

  3. https://leafletjs.com.

  4. https://developer.tomtom.com/routing-api.

  5. https://nodejs.org.

  6. https://koajs.com.

  7. https://www.postgresql.org.

  8. https://postgis.net.

  9. https://www.python.org.

  10. https://www.djangoproject.com.

  11. https://spark.apache.org.

  12. https://www.mongodb.com.

References

  1. Dai J, Yang B, Guo C, Ding Z (2015) Personalized route recommendation using big trajectory data. In 2015 IEEE 31st international conference on data engineering. IEEE, New York, pp 543-554

  2. Mc Ginty L, Smyth B (2001) Collaborative case-based reasoning: Applications in personalised route planning. In International Conference on Case-Based Reasoning. Springer, Berlin, Heidelberg, pp 362-376

  3. Tawfik AM, Rakha HA, Miller SD (2010) Driver route choice behavior: Experiences, perceptions, and choices. In 2010 IEEE Intelligent Vehicles Symposium. IEEE, New York, pp 1195-1200

  4. Cui G, Wang X (2017) MaP2R: a personalized maximum probability route recommendation method using GPS trajectories. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, pp 168-180

  5. Cui G, Luo J, Wang X (2018) Personalized travel route recommendation using collaborative filtering based on GPS trajectories. Int J Digit Earth 11(3):284–307

    Article  Google Scholar 

  6. Tong H, Faloutsos C, Pan JY (2006) Fast random walk with restart and its applications. In Sixth international conference on data mining (ICDM’06). IEEE, New York, pp 613-622

  7. Zhang Y, Cao T, Li S, Tian X, Yuan L, Jia H, Vasilakos AV (2016) Parallel processing systems for big data: a survey. Proc IEEE 104(11):2114–2136

    Article  Google Scholar 

  8. Park H, Jung J, Kang U (2017) A comparative study of matrix factorization and random walk with restart in recommender systems. In 2017 IEEE International Conference on Big Data (Big Data). IEEE, New York, pp 756-765

  9. Rahimi SM, Silva e, Far RADO, B., & Wang X (2019) Optimized random walk with restart for recommendation systems. In Canadian Conference on Artificial Intelligence. Springer, Cham, pp 320-332

  10. Wei S, Ye N, Zhang S, Huang X, Zhu J (2012) Item-based collaborative filtering recommendation algorithm combining item category with interestingness measure. In 2012 International Conference on Computer Science and Service System. IEEE, New York, pp 2038-2041

  11. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pp 285-295

  12. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  13. Lv B, Yu W, Wang L, McCann JA (2014, September) Efficient processing node proximity via random walk with restart. In Asia-Pacific Web Conference. Springer, Cham, pp 542-549

  14. Qiu Y, Xu X (2018) RPSBPT: A Route Planning Scheme with Best Profit for Taxi. In 2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN). IEEE, New York, pp 121-126

  15. Yuen CF, Singh AP, Goyal S, Ranu S, Bagchi A (2019) Beyond shortest paths: Route recommendations for ride-sharing. In The World Wide Web Conference, pp 2258-2269

  16. Chen Z, Shen HT, Zhou X (2011) Discovering popular routes from trajectories. In 2011 IEEE 27th International Conference on Data Engineering. IEEE, New York, pp 900-911

  17. Wei LY, Zheng Y, Peng WC (2012) Constructing popular routes from uncertain trajectories. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 195-203

  18. Campigotto P, Rudloff C, Leodolter M, Bauer D (2016) Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm. IEEE Trans Intell Transp Syst 18(1):92–102

    Article  Google Scholar 

  19. Funke S, Storandt S (2015) Personalized route planning in road networks. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp 1-10

  20. Kriegel HP, Renz M, Schubert M (2010) Route skyline queries: A multi-preference path planning approach. In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). IEEE, New York,  (pp.261-272

  21. Nadi S, Delavar MR (2011) Multi-criteria, personalized route planning using quantifier-guided ordered weighted averaging operators. Int J Appl Earth Obs Geoinf 13(3):322–335

    Google Scholar 

  22. Yawalkar P, Ranu S (2019) Route recommendations on road networks for arbitrary user preference functions. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, New York, pp 602-613

  23. Hu G, Qin Y, Shao J (2020) Personalized travel route recommendation from multi-source social media data. Multimed Tools Appl 79(45):33365–33380

  24. Du S, Zhang H, Xu H, Yang J, Tu O (2019) To make the travel healthier: a new tourism personalized route recommendation algorithm. J Ambient Intell Humaniz Comput 10(9):3551–3562

    Article  Google Scholar 

  25. Zhu X, Hao R, Chi H, Du X (2017) FineRoute: Personalized and time-aware route recommendation based on check-ins. IEEE Trans Veh Technol 66(11):10461–10469

    Article  Google Scholar 

  26. Rahimi SM, Far B, Wang X (2020) Behavior-based location recommendation on location-based social networks. GeoInformatica 24(3):477–504

    Article  Google Scholar 

  27. McGinty L, Smyth B (2000) Personalised route planning: A case-based approach. European Workshop on Advances in Case-Based Reasoning. Springer, Berlin, pp 431–443

    Chapter  Google Scholar 

  28. Letchner J, Krumm J, Horvitz E (2006) Trip router with individualized preferences (trip): Incorporating personalization into route planning. In AAAI, pp 1795–1800

  29. Liu L, Xu J, Liao SS, Chen H (2014) A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication. Expert Syst Appl 41(7):3409–3417

    Article  Google Scholar 

  30. Feng J, Li Y, Zhang C, Sun F, Meng F, Guo A, Jin D (2018) Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 world wide web conference, pp 1459-1468

  31. Wang J, Wu N, Zhao WX, Peng F, Lin X (2019) Empowering A* search algorithms with neural networks for personalized route recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 539-547

  32. Newson P, Krumm J (2009) Hidden Markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 336-343

  33. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013). Time-aware point-of-interest recommendation. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp 363-372

  34. Bastos-Filho CJ, Guimarães AC (2015) Multi-objective fish school search. Int J Swarm Intell Res 6(1):23–40

    Article  Google Scholar 

  35. Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008) Understanding mobility based on GPS data. In Proceedings of the 10th international conference on Ubiquitous computing, pp 312-321

  36. Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on World wide web, pp 791-800

  37. Zheng Y, Xie X, Ma WY (2010) GeoLife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39

    Google Scholar 

  38. Yu W, Lin X (2013) IRWR: incremental random walk with restart. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp 1017-1020

  39. Dwivedi VP, Joshi CK, Laurent T, Bengio Y, Bresson X (2020) Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982

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Authors and Affiliations

  1. Department of Geomatics Engineering, University of Calgary, 2500 University Dr. N.W. T2N 1N4, Calgary, Alberta, Canada

    Rodrigo Augusto de Oliveira e Silva, Ge Cui, Seyyed Mohammadreza Rahimi & Xin Wang

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  1. Rodrigo Augusto de Oliveira e Silva
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  2. Ge Cui
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  3. Seyyed Mohammadreza Rahimi
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  4. Xin Wang
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Correspondence to Xin Wang.

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de Oliveira e Silva, R.A., Cui, G., Rahimi, S.M. et al. Personalized route recommendation through historical travel behavior analysis. Geoinformatica 26, 505–540 (2022). https://doi.org/10.1007/s10707-021-00453-y

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