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

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Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

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.

Keywords

  • Human movement data
  • Travel mode choice behavior
  • Autocorrelation
  • Hierarchical clustering

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References

  • Aghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering-a decade review. Inf Syst 53:16–38

    CrossRef  Google Scholar 

  • An S, Wang Z, Cui J (2015) Integrating regret psychology to travel mode choice for a transit-oriented evacuation strategy. Sustainability 7(7):8116–8131

    CrossRef  Google Scholar 

  • Bankó Z, Abonyi J (2012) Correlation based dynamic time warping of multivariate time series. Expert Syst Appl 39(17):12814–12823

    CrossRef  Google Scholar 

  • 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 Rep

    Google Scholar 

  • Barragan JF, Fontes CH, Embiruçu M (2016) A wavelet-based clustering of multivariate time series using a multiscale spca approach. Comput Ind Eng 95:144–155

    CrossRef  Google Scholar 

  • 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 28:176–185

    CrossRef  Google Scholar 

  • Böcker L, van Amen P, Helbich M (2017) Elderly travel frequencies and transport mode choices in Greater Rotterdam, the Netherlands. Transportation 44(4):831–852

    CrossRef  Google Scholar 

  • Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. John Wiley, New Jersey

    Google Scholar 

  • 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 

  • 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 14:43–56

    CrossRef  Google Scholar 

  • Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat-Theory Methods 3(1):1–27

    CrossRef  Google Scholar 

  • 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):285–299

    CrossRef  Google Scholar 

  • Daisy NS, Millward H, Liu L (2018) Trip chaining and tour mode choice of non-workers grouped by daily activity patterns. J Transp Geogr 69:150–162

    CrossRef  Google Scholar 

  • 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):852–864

    CrossRef  Google Scholar 

  • 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 100:65–80

    Google Scholar 

  • Ding L, Zhang N (2016) A travel mode choice model using individual grouping based on cluster analysis. Procedia Eng 137:786–795

    CrossRef  Google Scholar 

  • D’Urso P, Maharaj EA (2009) Autocorrelation-based fuzzy clustering of time series. Fuzzy Sets Syst 160(24):3565–3589

    CrossRef  Google Scholar 

  • 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 1043–1052

    Google Scholar 

  • 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 5:8222

    CrossRef  Google Scholar 

  • Glasser W (1999) Choice theory: a new psychology of personal freedom. Harper Perennial, New York

    Google Scholar 

  • Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779

    CrossRef  Google Scholar 

  • Górecki T (2018) Classification of time series using combination of DTW and LCSS dissimilarity measures. Commun Stat-Simul Comput 47(1):263–276

    CrossRef  Google Scholar 

  • Gower JC, Ross GJ (1969) Minimum spanning trees and single linkage cluster analysis. Appl Stat 54–64

    Google Scholar 

  • Gunopulos D, Das G (2001) Time series similarity measures and time series indexing. Acm Sigmod Record, vol 30, ACM, p 624

    Google Scholar 

  • 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):1996

    CrossRef  Google Scholar 

  • 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 78:266–282

    Google Scholar 

  • 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):63–93

    CrossRef  Google Scholar 

  • 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):830–852

    CrossRef  Google Scholar 

  • 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):1391–1412

    CrossRef  Google Scholar 

  • 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 211–230

    Google Scholar 

  • 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 291–307

    Google Scholar 

  • Klinger T (2017) Moving from monomodality to multimodality? changes in mode choice of new residents. Transp Res Part A: Policy Pract 104:221–237

    Google Scholar 

  • 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 2018

    Google Scholar 

  • 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 41:147–158

    CrossRef  Google Scholar 

  • Łuczak M (2016) Hierarchical clustering of time series data with parametric derivative dynamic time warping. Expert Syst Appl 62:116–130

    CrossRef  Google Scholar 

  • 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):514–524

    CrossRef  Google Scholar 

  • Shen J, Cheng T (2016) A framework for identifying activity groups from individual space-time profiles. Int J Geogr Inf Sci 30(9):1785–1805

    CrossRef  Google Scholar 

  • 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):881–906

    CrossRef  Google Scholar 

  • Song C, Qu Z, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021

    CrossRef  Google Scholar 

  • 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 52:441–453

    CrossRef  Google Scholar 

  • Tuchschmid M, Halder M (2010) mobitool–grundlagenbericht: Hintergrund. Methodik & Emissionsfaktoren

    Google Scholar 

  • 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 54:164–178

    Google Scholar 

  • 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):25

    CrossRef  Google Scholar 

  • 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):213–239

    CrossRef  Google Scholar 

  • 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 717–720

    Google Scholar 

  • 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 104–109

    Google Scholar 

  • 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 22–26

    Google Scholar 

  • 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):496–520

    CrossRef  Google Scholar 

  • 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):1594–1621

    CrossRef  Google Scholar 

  • 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):855–871

    CrossRef  Google Scholar 

  • 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 62:122–135

    CrossRef  Google Scholar 

  • 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):1101–1127

    Google Scholar 

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Acknowledgements

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.

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Correspondence to Pengxiang Zhao .

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Zhao, P., Bucher, D., Martin, H., Raubal, M. (2020). A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D., Mansourian, A. (eds) Geospatial Technologies for Local and Regional Development. AGILE 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-14745-7_5

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