Skip to main content

Advertisement

SpringerLink
Log in

Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

With the wide availability of GPS trajectory data, sustainable development on understanding travel behaviors has been achieved in recent years. But relatively less attention has been paid to uncovering the trip purposes, i.e., why people make the trips. Unlike to the GPS trajectory data, the trip purposes cannot be easily and directly collected on a large scale, which necessitates the inference of trip purposes automatically. To this end, in this paper, we propose a device-free and novel model called Trip2Vec, which consists of three components. In the first component, it augments the context on trip origins and destinations, respectively, by extracting the information about the nearby point of interest configurations and human activity popularity at particular time periods (i.e., activity period popularity) from two crowdsourced datasets. Such context is well-recognized as the clear clue of trip purposes. In the second component, on the top of the augmented context, a deep embedding approach is developed to get a more semantical and discriminative context representation in the latent space. In the third component, we simply adopt the common clustering algorithm (i.e., K-means) to aggregate trips with similar latent representation, then conduct trip purpose interpretation based on the clustering results, followed by understanding the time-evolving tendency of trip purpose patterns (i.e., profiling) in the city-wide level. Finally, we present extensive experiment results with real-world taxi trajectory and Foursquare check-in data generated in New York City (NYC) to demonstrate the effectiveness of the proposed model, and moreover, the obtained city-wide trip purpose patterns are quite consistent with real situations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. https://www.openstreetmap.org

References

  1. Alahi A, Goel K, Ramanathan V, Robicquet A, Fei-Fei L, Savarese S (2016) Social lstm: Human trajectory prediction in crowded spaces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 961–971

  2. Bao J, Xu C, Liu P, Wang W (2017) Exploring bikesharing travel patterns and trip purposes using smart card data and online point of interests. Networks and Spatial Economics 17(4):1231–1253

    Article  Google Scholar 

  3. Beeferman D, Berger A (2000) Agglomerative clustering of a search engine query log. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp 407–416

  4. Bohte W, Maat K (2009) Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Transportation Research Part C: Emerging Technologies 17(3):285–297

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Chen C, Zhang D, Li N, Zhou ZH (2014) B-Planner: Planning bidirectional night bus routes using large-scale taxi GPS traces. IEEE Trans Intell Transp Syst 15(4):1451–1465

    Article  Google Scholar 

  7. Chen C, Zhang D, Guo B, Ma X, Pan G, Wu Z (2015) TripPlanner: Personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Trans Intell Transp Syst 16(3):1259–1273

    Article  Google Scholar 

  8. Chen C, Wang Z, Guo B (2016) The road to the Chinese smart city: progress, challenges, and future directions. IT Professional 18(1):14–17

    Article  Google Scholar 

  9. Chen C, Chen X, Wang L, Ma X, Wang Z, Liu K, Guo B, Zhou Z (2017) MA-SSR: A memetic algorithm for skyline scenic routes planning leveraging heterogeneous user-generated digital footprints. IEEE Trans Veh Technol 66(7):5723–5736

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Chen C, Zhang D, Ma X, Guo B, Wang L, Wang Y, Sha E (2017) CrowdDeliver: Planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans Intell Transp Syst 18(6):1478–1496

    Google Scholar 

  12. Chen C, Jiao S, Zhang S, Liu W, Feng L, Wang Y (2018) TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Transactions on Intelligent Transportation Systems to appear: 1–13

  13. Chen L, Jakubowicz J, Yang D, Zhang D, Pan G (2017) Fine-grained urban event detection and characterization based on tensor cofactorization. IEEE Transactions on Human-Machine Systems 47(3):380–391

    Article  Google Scholar 

  14. Cramer H, Rost M, Holmquist LE (2011) Performing a check-in: emerging practices, norms and ‘conflicts’ in location-sharing using foursquare. In: Proceedings of the 13th international conference on human computer interaction with mobile devices and services, pp 57–66

  15. De Brébisson A, Simon É, Auvolat A, Vincent P, Bengio Y (2015) Artificial neural networks applied to taxi destination prediction. arXiv:150800021

  16. Deng Z, Ji M (2010) Deriving rules for trip purpose identification from GPS travel survey data and land use data: a machine learning approach. In: Traffic and transportation studies 2010, pp 768–777

  17. Dong W, Yuan T, Yang K, Li C, Zhang S (2017) Autoencoder regularized network for driving style representation learning. arXiv:170101272

  18. Endo Y, Toda H, Nishida K, Kawanobe A (2016) Deep feature extraction from trajectories for transportation mode estimation. In: Pacific-Asia conference on knowledge discovery and data Mining. Springer, Berlin, pp 54–66

  19. Feng S, Cong G, An B, Chee YM (2017) Poi2vec: Geographical latent representation for predicting future visitors. In: AAAI Conference on Artificial Intelligence

  20. Furletti B, Cintia P, Renso C (2013) Spinsanti LInferring human activities from GPS tracks. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, p 5

  21. Gong L, Morikawa T, Yamamoto T, Sato H (2014) Deriving personal trip data from GPS data: a literature review on the existing methodologies. Procedia-Social and Behavioral Sciences 138:557–565

    Article  Google Scholar 

  22. Gong L, Liu X, Wu L, Liu Y (2016) Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartogr Geogr Inf Sci 43(2):103–114. https://doi.org/10.1080/15230406.2015.1014424

    Article  Google Scholar 

  23. Guo B, Chen H, Han Q, Yu Z, Zhang D, Wang Y (2017) Worker-contributed data utility measurement for visual crowdsensing systems. IEEE Trans Mob Comput 16(8):2379–2391

    Article  Google Scholar 

  24. Guo B, Han Q, Chen H, Shangguan L, Zhou Z, Yu Z (2017) The emergence of visual crowdsensing: Challenges and opportunities. IEEE Commun Surv Tutorials 19(4):2526–2543

    Article  Google Scholar 

  25. Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics) 28(1):100–108

    MATH  Google Scholar 

  26. Huang Z, Zhao Z, Shijia E, Yu C, Shan G, Li T, Cheng J, Sun J, Xiang Y (2017) Prace: A taxi recommender for finding passengers with deep learning approaches. In: International Conference on Intelligent Computing, Springer, pp 759–770

  27. Jiang X, de Souza EN, Pesaranghader A, Hu B, Silver DL, Matwin S (2017) Trajectorynet: An embedded GPS trajectory representation for point-based classification using recurrent neural networks. arXiv:170502636

  28. Kong X, Xia F, Ning Z, Rahim A, Cai Y, Gao Z, Ma J (2018) Mobility dataset generation for vehicular social networks based on floating car data. IEEE Trans Veh Technol 67(5):3874–3886

    Article  Google Scholar 

  29. Krumm J, Rouhana D (2013) Placer: semantic place labels from diary data. In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp 163–172

  30. Lin Y, Wan H, Jiang R, Wu Z, Jia X (2015) Inferring the travel purposes of passenger groups for better understanding of passengers. IEEE Trans Intell Transp Syst 16(1):235–243

    Article  Google Scholar 

  31. Ma X, Wu Y J, Wang Y, Chen F, Liu J (2013) Mining smart card data for transit riders’ travel patterns. Transportation Research Part C: Emerging Technologies 36:1–12

    Article  Google Scholar 

  32. Maaten Lvd, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(Nov):2579–2605

    MATH  Google Scholar 

  33. Morency C, Trépanier M, Agard B (2007) Measuring transit use variability with smart-card data. Transp Policy 14(3):193–203

    Article  Google Scholar 

  34. Ruths D, Pfeffer J (2014) Social media for large studies of behavior. Science 346(6213):1063–1064

    Article  Google Scholar 

  35. Song X, Kanasugi H, Shibasaki R (2016) Deeptransport: prediction and simulation of human mobility and transportation mode at a citywide level. In: IJCAI, pp 2618–2624

  36. Wang J, Gu Q, Wu J, Liu G, Xiong Z (2016) Traffic speed prediction and congestion source exploration: a deep learning method. In: 2016 IEEE 16th international conference on data mining (ICDM), IEEE, pp 499–508

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

    Article  Google Scholar 

  38. Wang P, Fu Y, Liu G, Hu W, Aggarwal C (2017) Human mobility synchronization and trip purpose detection with mixture of Hawkes processes. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 495–503

  39. Wang R, Chow CY, Lyu Y, Lee VC, Kwong S, Li Y, Zeng J (2018) Taxirec: recommending road clusters to taxi drivers using ranking-based extreme learning machines. IEEE Trans Knowl Data Eng 30(3):585–598

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. Wolf J (2000) Using GPS data loggers to replace travel diaries in the collection of travel data. PhD thesis, Georgia Institute of Technology

  42. Wu Y, Tan H (2016) Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv:161201022

  43. Xiao G, Juan Z, Gao J (2015) Travel mode detection based on neural networks and particle swarm optimization. Information 6(3):522–535

    Article  Google Scholar 

  44. Yang C, Bai L, Zhang C, Yuan Q, Han J (2017) Bridging collaborative filtering and semi-supervised learning: a neural approach for POI recommendation. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1245–1254

  45. Yu Z, Xu H, Yang Z, Guo B (2016) Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems 46(1):151–158

    Article  Google Scholar 

  46. Yue Y, Lan T, Yeh AG, Li QQ (2014) Zooming into individuals to understand the collective: a review of trajectory-based travel behaviour studies. Travel Behaviour and Society 1(2):69–78

    Article  Google Scholar 

  47. Zhang D, Sun L, Li B, Chen C, Pan G, Li S, Wu Z (2014) Understanding taxi service strategies from taxi GPS traces. IEEE Trans Intell Transp Syst 16(1):123–135

    Article  Google Scholar 

  48. Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI, pp 1655–1661

  49. Zhu Z, Blanke U, Tröster G (2014) Inferring travel purpose from crowd-augmented human mobility data. In: Proceedings of the 1st international conference on IoT in urban space, pp 44–49

Download references

Funding

The work was supported by the National Key R&D Project of China (No. 2017YFB1002000), the National Science Foundation of China (No. 61602067), the Fundamental Research Funds for the Central Universities (No. 2018cdqyjsj0024), and the Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University).

Author information

Author notes

    Authors and Affiliations

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

      Chao Chen & Chengwu Liao

    2. School of Media and Communication, University of Leeds, Leeds, LS2 9JT, UK

      Xuefeng Xie

    3. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

      Yasha Wang & Junfeng Zhao

    Authors
    1. Chao Chen
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Chengwu Liao
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Xuefeng Xie
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Yasha Wang
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Junfeng Zhao
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding authors

    Correspondence to Chao Chen or Xuefeng Xie.

    Additional information

    Chao Chen and Chengwu Liao contributed equally on this work.

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Chen, C., Liao, C., Xie, X. et al. Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes. Pers Ubiquit Comput 23, 53–66 (2019). https://doi.org/10.1007/s00779-018-1175-9

    Download citation

    • Received:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s00779-018-1175-9

    Keywords

    Search

    Navigation