Skip to main content

Advertisement

SpringerLink
Log in

Understanding and visualizing passengers’ travel behaviours: a device-free sensing way leveraging taxi trajectory data

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

Abstract

Recent years witness the unprecedented proliferation of human-related trajectory data, such as GPS data and Local-Based Social Networks data. Each kind of trajectory data has its pros and cons in instrumenting and charactering human mobility and activities, which provides a valuable opportunity to understand the in-depth intelligence of human travel behaviors from the perspective of data fusion. In this paper, we focus on the motivations of passengers’ travel behaviors, i.e., trip purposes. Rather than deploying sensors to collect data regarding passengers behaviours directly, we propose a device-free sensing way which leverages the taxi trajectory data left when passengers taking taxis. In another word, the data is collected by GPS devices installed in taxis. Specifically, we establish a visual analytics system called VizTripPurpose to understand taxi trip purposes by fusing the taxi trajectory data and human check-in data. Specifically, our system allows for not only obtaining taxi trip purpose on both collective and individual scales, but also visually enable analysts, such as urban planners, to (1) intuitively understand the time-evolving taxi trip purpose patterns by profiling them at different time periods in the multi-scale spatial domain; (2) timely infer the trip purpose for a given taxi trip containing origin, destination, and time information. To illustrate the effectiveness of our system, we provide case studies on real taxi trip data and Foursquare check-in data generated in the region of Manhattan, New York City (NYC), US.

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

Similar content being viewed by others

Notes

  1. Human-related trajectory data is belonging to the family of spatio-temporal urban data.

  2. Foursquare. https://foursquare.com/

  3. Mapbox GL JS API. https://docs.mapbox.com/mapbox-gl-js/api/

References

  1. Al-Kodmany K (2001) Bridging the gap between technical and local knowledge: tools for promoting community-based planning and design. J Archit Plan Res 18:110–130

    Google Scholar 

  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. Netw Spatial Econ 17(4):1231–1253

    Article  Google Scholar 

  3. Buchmüller J, Janetzko H, Andrienko G, Andrienko N, Fuchs G, Keim DA (2015) Visual analytics for exploring local impact of air traffic. Computer Graphics Forum 34(3):181–190

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Chen C, Zhang D, Guo B, Ma X, Pan G, Wu Z (2014) TripPlanner: personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Trans Intell Transp Syst 16(3):1259–1273

    Article  Google Scholar 

  6. Chen C, Zhang D, Ma X, Guo B, Wang L, Wang Y, Sha E (2016) CROWDDELIVER: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans Intell Transp Syst 18(6):1478–1496

    Google Scholar 

  7. Chen C, Jiao S, Zhang S, Liu W, Feng L, Wang Y (2018a) TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Trans Intell Transp Syst 19(10):3292–3304

  8. Chen C, Ding Y, Xie X, Zhang S, Wang Z, Feng L (2019) TrajCompressor: an online map-matching-based trajectory compression framework leveraging vehicle heading direction and change. IEEE Trans Intell Transport Sys

  9. Chen C, Liao C, Xie X, Wang Y, Zhao J (2019b) Trip2vec: a deep embedding approach for clustering and profiling taxi trip purposes. Pers Ubiquit Comput 23(1):53–66

  10. Chen W, Huang Z, Wu F, Zhu M, Guan H, Maciejewski R (2018b) VAUD: a visual analysis approach for exploring spatio-temporal urban data. IEEE Trans Vis Comput Graph 24(9):2636– 2648

  11. 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: Proceedings of the traffic and transportation studies 2010, pp 768–777

  12. Dewri R, Annadata P, Eltarjaman W, Thurimella R (2013) Inferring trip destinations from driving habits data. In: Proceedings of the 12th ACM workshop on privacy in the electronic society, pp 267–272

  13. Ding Y, Chen C, Zhang S, Guo B, Yu Z, Wang Y (2017) GreenPlanner: planning personalized fuel-efficient driving routes using multi-sourced urban data. In: Proceedings of the 2017 IEEE international conference on pervasive computing and communications (PerCom), pp 207–216

  14. El Faouzi NE, Leung H, Kurian A (2011) Data fusion in intelligent transportation systems: progress and challenges–a survey. Inform Fusion 12(1):4–10

    Article  Google Scholar 

  15. Ferreira N, Poco J, Vo HT, Freire J, Silva CT (2013) Visual exploration of big spatio-temporal urban data: a study of New York city taxi trips. IEEE Trans Vis Comput Graph 19(12):219–2158

    Article  Google Scholar 

  16. Furletti B, Cintia P, Renso C, Spinsanti L (2013) Inferring human activities from GPS tracks. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing

  17. Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010) An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 899–908

  18. Gingerich K, Maoh H, Anderson W (2016) Classifying the purpose of stopped truck events: an application of entropy to GPS data. Transportation Research Part C: Emerging Technologies 64:17–27

    Article  Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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: Proceedings of the international conference on intelligent computing. Springer, pp 759–770

  21. Krüger R, Thom D, Ertl T (2014) Visual analysis of movement behavior using web data for context enrichment. In: Proceedings of the 2014 IEEE pacific visualization symposium (PacificVis), pp 193–200

  22. Krüger R, Thom D, Ertl T (2015) Semantic enrichment of movement behavior with foursquare–a visual analytics approach. IEEE Trans Vis Comput Graph 21(8):903–915

    Article  Google Scholar 

  23. 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 

  24. Lundblad P, Jern M, Forsell C (2008) Voyage analysis applied to geovisual analytics. In: Proceedings of the 12th international conference information visualisation, pp 381–388

  25. Lundblad P, Eurenius O, Heldring T (2009) Interactive visualization of weather and ship data. In: Proceedings of the 2009 international conference on information visualisation, pp 379–386

  26. Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in foursquare. In: Proceedings of the 15th international AAAI conference on weblogs and social media

  27. Perhac J, Zeng W, Asada S, Arisona SM, Schubiger S, Burkhard R, Klein B (2017) Urban fusion: visualizing urban data fused with social feeds via a game engine. In: Proceedings of the 21st international conference on information visualisation (IV), pp 312–317

  28. Psyllidis A, Bozzon A, Bocconi S, Bolivar CT (2015) A platform for urban analytics and semantic data integration in city planning. In: Proceedings of the international conference on computer-aided architectural design futures, pp 21–36

  29. Psyllidis A, Bozzon A, Bocconi S, Titos Bolivar C (2015) Harnessing heterogeneous social data to explore, monitor, and visualize urban dynamics. In: Proceedings of the 14th international conference on computers in urban planning and urban management (CUPUM)

  30. Rodrigues F, Markou I, Pereira FC (2019) Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach. Information Fusion 49:120–129

    Article  Google Scholar 

  31. Vaccari A, Liu L, Biderman A, Ratti C, Pereira F, Oliveirinha J, Gerber A (2009) A holistic framework for the study of urban traces and the profiling of urban processes and dynamics. In: Proceedings of the 12th international ieee conference on intelligent transportation systems

  32. 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

  33. 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 

  34. Xin T, Guo B, Wang Z, Wang P, Lam JCK, Li V, Yu Z (2018) Freesense: a robust approach for indoor human detection using wifi signals. Proceedings of the ACM on Interactive, Mobile Wearable and Ubiquitous Technologies 2(3):143

    Article  Google Scholar 

  35. Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIs. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 186–194

  36. Zeng W, Fu CW, Arisona SM, Schubiger S, Burkhard R, Ma KL (2017) Visualizing the relationship between human mobility and points of interest. IEEE Trans Intell Transp Syst 18(8):2271–2284

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Zhang D, Li N, Zhou ZH, Chen C, Sun L, Li S (2011) iBAT: detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th international conference on ubiquitous computing, pp 99–108

  39. Zhang F, Yuan NJ, Wilkie D, Zheng Y, Xie X (2015) Sensing the pulse of urban refueling behavior: a perspective from taxi mobility. ACM Transactions on Intelligent Systems and Technology (TIST) 6(3):37

    Google Scholar 

  40. Zhang X, Xu Y, Tu W, Ratti C (2018) Do different datasets tell the same story about urban mobility—a comparative study of public transit and taxi usage. J Transp Geogr 70:78–90

    Article  Google Scholar 

  41. Zheng Y (2015) Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST) 6(3):29

    Google Scholar 

  42. Zheng Y, Liu Y, Yuan J, Xie X (2011) Urban computing with taxicabs. In: Proceedings of the 13th international conference on ubiquitous computing, pp 89–98

  43. Zhu L, Yu FR, Wang Y, Ning B, Tang T (2018) Big data analytics in intelligent transportation systems: a survey. IEEE Transactions on Intelligent Transportation Systems (99):1–16

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

Download references

Funding

The work was supported by the National Natural Science Foundation of China (No. 61872050 and No. 61602067), the Fundamental Research Funds for the Central Universities (No. 2018cdqyjsj0024), and the Chongqing Basic and Frontier Research Program (No. cstc2018jcyjAX0551).

Author information

Authors and Affiliations

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

    Chengwu Liao, Chao Chen & Hong Xie

  2. College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, China

    Zhiqing Zhang

  3. Chongqing University-University of Cincinnati Joint Coop Institute, Chongqing, 400044, China

    Zhiqing Zhang

Authors
  1. Chengwu Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiqing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Chao Chen or Zhiqing Zhang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Chengwu Liao and Chao Chen contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, C., Chen, C., Zhang, Z. et al. Understanding and visualizing passengers’ travel behaviours: a device-free sensing way leveraging taxi trajectory data. Pers Ubiquit Comput 26, 491–503 (2022). https://doi.org/10.1007/s00779-019-01346-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-019-01346-6

Keywords

Search

Navigation