A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

  • Pengxiang ZhaoEmail author
  • Dominik Bucher
  • Henry Martin
  • Martin Raubal
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Travel mode choice analysis is a central aspect of understanding human mobility and plays an important role in urban transportation and planning. The emergence of passively recorded movement data with spatio-temporal and semantic information offers opportunities for uncovering individuals’ travel mode choice behavior. Considering that many of these choices are highly regular and are performed in similar manners by different groups of people, it is desirable to identify these groups and their characteristic behavior (e.g. for educational or political incentives or to find environmentally-friendly people). Previous research mainly grouped people according to “mobility snapshots”, i.e. mobility patterns exhibited at a single point in time. We argue that especially when considering the change of behavior over time, we need to investigate the behavioral dynamic processes resp. the change of travel mode choices over time. We present a framework that can be used to cluster people according to the dynamics of their travel mode choice behavior, based on automatically tracked GPS data. We test the framework on a large user sample of 107 persons in Switzerland and interpret their travel mode choice behavior patterns based on the clustering results. This facilitates understanding people’s travel mode choice behavior in multimodal transportation and how to design reasonable alternatives to private cars for more sustainable cities.


Human movement data Travel mode choice behavior Autocorrelation Hierarchical clustering 



This research was supported by the Swiss Data Science Center (SDSC), by the Swiss Innovation Agency Innosuisse within the Swiss Competence Center for Energy Research (SCCER) Mobility and by the Swiss Federal Railways SBB.


  1. Aghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering-a decade review. Inf Syst 53:16–38Google Scholar
  2. An S, Wang Z, Cui J (2015) Integrating regret psychology to travel mode choice for a transit-oriented evacuation strategy. Sustainability 7(7):8116–8131Google Scholar
  3. Bankó Z, Abonyi J (2012) Correlation based dynamic time warping of multivariate time series. Expert Syst Appl 39(17):12814–12823Google Scholar
  4. Barbosa H, Barthelemy M, Ghoshal G, James CR, Lenormand M, Louail T, Menezes R, Ramasco JJ, Simini F, Tomasini M (2018) Human mobility: models and applications. Phys RepGoogle Scholar
  5. Barragan JF, Fontes CH, Embiruçu M (2016) A wavelet-based clustering of multivariate time series using a multiscale spca approach. Comput Ind Eng 95:144–155Google Scholar
  6. Böcker L, Prillwitz J, Dijst M (2013) Climate change impacts on mode choices and travelled distances: a comparison of present with 2050 weather conditions for the randstad holland. J Transp Geogr 28:176–185Google Scholar
  7. Böcker L, van Amen P, Helbich M (2017) Elderly travel frequencies and transport mode choices in Greater Rotterdam, the Netherlands. Transportation 44(4):831–852Google Scholar
  8. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. John Wiley, New JerseyGoogle Scholar
  9. Bucher D, Cellina F, Mangili F, Raubal M, Rudel R, Rizzoli AE, Elabed O (2016) Exploiting fitness apps for sustainable mobility-challenges deploying the Goeco! app. ICT for sustainability (ICT4S)Google Scholar
  10. Bucher D, Mangili F, Cellina F, Bonesana C, Jonietz D, Raubal M (2019) From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel Behav Soc 14:43–56Google Scholar
  11. Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat-Theory Methods 3(1):1–27Google Scholar
  12. Chen C, Gong H, Paaswell R (2008) Role of the built environment on mode choice decisions: additional evidence on the impact of density. Transportation 35(3):285–299Google Scholar
  13. Daisy NS, Millward H, Liu L (2018) Trip chaining and tour mode choice of non-workers grouped by daily activity patterns. J Transp Geogr 69:150–162Google Scholar
  14. Dias JG, Vermunt JK, Ramos S (2015) Clustering financial time series: new insights from an extended hidden markov model. Eur J Oper Res 243(3):852–864Google Scholar
  15. Ding C, Wang D, Liu C, Zhang Y, Yang J (2017) Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transp Res Part A: Policy Pract 100:65–80Google Scholar
  16. Ding L, Zhang N (2016) A travel mode choice model using individual grouping based on cluster analysis. Procedia Eng 137:786–795Google Scholar
  17. D’Urso P, Maharaj EA (2009) Autocorrelation-based fuzzy clustering of time series. Fuzzy Sets Syst 160(24):3565–3589Google Scholar
  18. Froehlich J, Dillahunt T, Klasnja P, Mankoff J, Consolvo S, Harrison B, Landay JA (2009) Ubigreen: investigating a mobile tool for tracking and supporting green transportation habits. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 1043–1052Google Scholar
  19. Gao Z-K, Yang Y-X, Fang P-C, Jin N-D, Xia C-Y, Hu L-D (2015) Multi-frequency complex network from time series for uncovering oil-water flow structure. Sci Rep 5:8222Google Scholar
  20. Glasser W (1999) Choice theory: a new psychology of personal freedom. Harper Perennial, New YorkGoogle Scholar
  21. Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779Google Scholar
  22. Górecki T (2018) Classification of time series using combination of DTW and LCSS dissimilarity measures. Commun Stat-Simul Comput 47(1):263–276Google Scholar
  23. Gower JC, Ross GJ (1969) Minimum spanning trees and single linkage cluster analysis. Appl Stat 54–64Google Scholar
  24. Gunopulos D, Das G (2001) Time series similarity measures and time series indexing. Acm Sigmod Record, vol 30, ACM, p 624Google Scholar
  25. Han Y, Li W, Wei S, Zhang T (2018) Research on passenger’s travel mode choice behavior waiting at bus station based on sem-logit integration model. Sustainability 10(6):1996Google Scholar
  26. Heinen E, Chatterjee K (2015) The same mode again? an exploration of mode choice variability in great britain using the national travel survey. Transp Res Part A: Policy Pract 78:266–282Google Scholar
  27. Huang H, Gartner G, Krisp JM, Raubal M, de Weghe NV (2018) Location based services: ongoing evolution and research agenda. J Locat Based Serv 12(2):63–93Google Scholar
  28. Hunecke M, Blöbaum A, Matthies E, Höger R (2001) Responsibility and environment: ecological norm orientation and external factors in the domain of travel mode choice behavior. Environ Behav 33(6):830–852Google Scholar
  29. Hwang S, VanDeMark C, Dhatt N, Yalla SV, Crews RT (2018) Segmenting human trajectory data by movement states while addressing signal loss and signal noise. Int J Geogr Inf Sci 32(7):1391–1412Google Scholar
  30. Jonietz D, Bucher D (2018) Continuous trajectory pattern mining for mobility behaviour change detection. In: LBS 2018: 14th international conference on location based services. Springer, pp 211–230Google Scholar
  31. Jonietz D, Bucher D, Martin H, Raubal M (2018) Identifying and interpreting clusters of persons with similar mobility behaviour change processes. In: Mansourian A, Pilesjö P, Harrie L, van Lammeren R (eds) AGILE 2018—geospatial technologies for all. Springer International Publishing, Cham, pp 291–307Google Scholar
  32. Klinger T (2017) Moving from monomodality to multimodality? changes in mode choice of new residents. Transp Res Part A: Policy Pract 104:221–237Google Scholar
  33. Liang Q, Weng J, Zhou W, Santamaria SB, Ma J, Rong J (2018) Individual travel behavior modeling of public transport passenger based on graph construction. J Adv Transp 2018Google Scholar
  34. Liu C, Susilo YO, Karlström A (2015) The influence of weather characteristics variability on individual’s travel mode choice in different seasons and regions in Sweden. Transp Policy 41:147–158Google Scholar
  35. Łuczak M (2016) Hierarchical clustering of time series data with parametric derivative dynamic time warping. Expert Syst Appl 62:116–130Google Scholar
  36. Murtagh N, Gatersleben B, Uzzell D (2012) Multiple identities and travel mode choice for regular journeys. Transp Res Part F: Traffic Psychol Behav 15(5):514–524Google Scholar
  37. Shen J, Cheng T (2016) A framework for identifying activity groups from individual space-time profiles. Int J Geogr Inf Sci 30(9):1785–1805Google Scholar
  38. Siła-Nowicka K, Vandrol J, Oshan T, Long JA, Demšar U, Fotheringham AS (2016) Analysis of human mobility patterns from gps trajectories and contextual information. Int J Geogr Inf Sci 30(5):881–906Google Scholar
  39. Song C, Qu Z, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021Google Scholar
  40. Sun B, Ermagun A, Dan B (2017) Built environmental impacts on commuting mode choice and distance: evidence from Shanghai. Transp Res Part D: Transp Environ 52:441–453Google Scholar
  41. Tuchschmid M, Halder M (2010) mobitool–grundlagenbericht: Hintergrund. Methodik & EmissionsfaktorenGoogle Scholar
  42. Vij A, Carrel A, Walker JL (2013) Incorporating the influence of latent modal preferences on travel mode choice behavior. Transp Res Part A: Policy Pract 54:164–178Google Scholar
  43. Wang Y, Qin K, Chen Y, Zhao P (2018) Detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi GPS data. ISPRS Int J Geo-Inf 7(1):25Google Scholar
  44. Weiser P, Scheider S, Bucher D, Kiefer P, Raubal M (2016) Towards sustainable mobility behavior: research challenges for location-aware information and communication technology. GeoInformatica 20(2):213–239Google Scholar
  45. Xiong Y, Yeung D-Y (2002) Mixtures of arma models for model-based time series clustering. In: 2002 IEEE International Conference on Data Mining, 2002. ICDM 2003. Proceedings, IEEE, pp 717–720Google Scholar
  46. Ye Y, Niu C, Jiang J, Ge B, Yang K (2017) A shape based similarity measure for time series classification with weighted dynamic time warping algorithm. In: 4th International conference on information science and control engineering (ICISCE), 2017, IEEE, pp 104–109Google Scholar
  47. Yuan Y, Raubal M (2012) A framework for spatio-temporal clustering from mobile phone data. Workshop on complex data mining in a geospatial context proceedings at AGILE 2012. Association of Geographic Information Laboratories for Europe (AGILE), pp 22–26Google Scholar
  48. Yuan Y, Raubal M (2014) Measuring similarity of mobile phone user trajectories—a spatio-temporal edit distance method. Int J Geogr Inf Sci 28(3):496–520Google Scholar
  49. Yuan Y, Raubal M (2016) Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study. Int J Geogr Inf Sci 30(8):1594–1621Google Scholar
  50. Yue M, Kang C, Andris C, Qin K, Liu Y, Meng Q (2018) Understanding the interplay between bus, metro, and cab ridership dynamics in shenzhen, China. Trans GIS 22(3):855–871Google Scholar
  51. Zhao P, Kwan M-P, Qin K (2017a) Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on individuals’ daily travel. J Transp Geogr 62:122–135Google Scholar
  52. Zhao P, Qin K, Ye X, Wang Y, Chen Y (2017b) A trajectory clustering approach based on decision graph and data field for detecting hotspots. Int J Geogr Inf Sci 31(6):1101–1127Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pengxiang Zhao
    • 1
    Email author
  • Dominik Bucher
    • 1
  • Henry Martin
    • 1
  • Martin Raubal
    • 1
  1. 1.Institute of Cartography and Geoinformation, ETH ZurichZurichSwitzerland

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