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Characterizing Urban Dynamics Using Large Scale Taxicab Data

Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS,volume 38)

Abstract

Understanding urban dynamics is of fundamental importance for the efficient operation and sustainable development of large cities. In this paper, we present a comprehensive study on characterizing urban dynamics using the large scale taxi data in New York City. The pick-up and drop-off locations are firstly analyzed separately to reveal the general trip pattern across the city and the existence of unbalanced trips. The inherent similarities among taxi trips are further investigated using the two-step clustering algorithm. It builds up the relationship among detached areas in terms of land use types, travel distances and departure time. Moreover, human mobility pattern are inferred from the taxi trip displacements and is found to follow two stages: an exponential distribution with short trips and a truncated power law distribution for longer trips. The result indicates that the taxi trip may not fully represent human mobility and is heavily affected by trip expenses and the urban form and geography.

Keywords

  • Census Tract
  • Travel Distance
  • Human Mobility
  • Trip Distance
  • Urban Dynamic

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Batty M, Xie Y (1994) From cells to cities. Environ Plan B 21:31

    CrossRef  Google Scholar 

  2. Batty M, Xie Y, Sun Z et al (1999) Modeling urban dynamics through GIS-based cellular automata. Comput Environ Urban Syst 23:205–233

    CrossRef  Google Scholar 

  3. Brockmann D, Hufnagel L, Geisel T et al (2006) The scaling laws of human travel. Nature 439:462–465

    CrossRef  Google Scholar 

  4. Calabrese F, Diao M, Di Lorenzo G et al (2013) Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp Res Part C Emerg Technol 26:301–313

    CrossRef  Google Scholar 

  5. Chang H, Tai Y, Chen H et al (2008) iTaxi: context-aware taxi demand hotspots prediction using ontology and data mining approaches. In: Proceedings of 13th conferance artificial intelligence and applications (TAAI 2008)

    Google Scholar 

  6. Chiu T, Fang D, Chen J et al (2001) A robust and scalable clustering algorithm for mixed type attributes in large database environment. In: Proceedings of seventh ACM SIGKDD international conferanceon of knowledge discovery and data mining—(KDD 01) 263–268

    Google Scholar 

  7. Gonzalez MC, Hidalgo CA, Barabasi A-L et al (2008) Understanding individual human mobility patterns. Nature 453:779–782

    CrossRef  Google Scholar 

  8. Guiliano G (2004) Land use impacts of transportation investments: highway and transit. In: Hanson S and Giuliano G (eds) Geography of urban transportation, 3rd edn. Guilford Press, New York

    Google Scholar 

  9. Harris B (1985) Urban simulation models in regional science. J Reg Sci 25:545–567

    CrossRef  Google Scholar 

  10. Hasan S, Zhan X, Ukkusuri SV et al (2013) Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In: Proceedings of 2nd ACM SIGKDD international work. Urban computing, p 6

    Google Scholar 

  11. Jiang B, Yin J, Zhao S et al (2009) Characterizing the human mobility pattern in a large street network. Phys Rev E 80:21136

    CrossRef  Google Scholar 

  12. Li B, Zhang D, Sun L et al (2011) Hunting or waiting? discovering passenger-finding strategies from a large-scale real-world taxi dataset. In: IEEE international conference on pervasive computing and communications workshops (PERCOM Workshops), pp 63–68

    Google Scholar 

  13. Liang X, Zheng X, Lv W et al (2012) The scaling of human mobility by taxis is exponential. Phys A Stat Mech Its Appl 391:2135–2144

    CrossRef  Google Scholar 

  14. Liu Y, Wang F, Xiao Y et al (2012) Urban land uses and traffic ’source-sink areas’: evidence from GPS-enabled taxi data in Shanghai. Landsc Urban Plan 106:73–87

    CrossRef  Google Scholar 

  15. NYCTLC(2012) New York city taxi and limousine commission (2012) Annual Report

    Google Scholar 

  16. Pan G, Qi G, Wu Z et al (2013) Land-use cassification using taxi GPS traces. IEEE Trans Intell Transp Syst 14:113–123

    CrossRef  Google Scholar 

  17. Peng C, Jin X, Wong K-C et al (2012) Collective human mobility pattern from taxi trips in urban area. PLoS One 7:e34487

    CrossRef  Google Scholar 

  18. Ratti C, Pulselli RM, Williams S et al (2006) Mobile landscapes: using location data from cell phones for urban analysis. Environ Plan B Plan Des 33:727–748

    CrossRef  Google Scholar 

  19. Reades J, Calabrese F (2007) Cellular census: explorations in urban data collection. Pervasive Comput IEEE 6:30–38

    CrossRef  Google Scholar 

  20. SPSS INC (2001) The SPSS twoStep cluster component: a scalable component to segment your customers more effectively

    Google Scholar 

  21. Sun L, Chen C, Zhang D et al (2013) Understanding urban dynamics from Taxi GPS traces. Creat Pers Soc Urban Aware Through Pervasive Comput, p 299

    Google Scholar 

  22. Szell M, Sinatra R, Petri G et al (2012) Understanding mobility in a social petri dish. Sci Rep 2:457

    CrossRef  Google Scholar 

  23. Yang H, Fung CS, Wong KI et al (2010) Nonlinear pricing of taxi services. Transp Res Part A Policy Pract 44:337–348

    CrossRef  Google Scholar 

  24. Yuan J, Zheng Y, Zhang L et al (2011) Where to find my next passenger. In: Proceedings of 13th international conferance on ubiquitous computing, pp 109–118

    Google Scholar 

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Correspondence to Satish V. Ukkusuri .

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Qian, X., Zhan, X., Ukkusuri, S.V. (2015). Characterizing Urban Dynamics Using Large Scale Taxicab Data. In: Lagaros, N., Papadrakakis, M. (eds) Engineering and Applied Sciences Optimization. Computational Methods in Applied Sciences, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-18320-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-18320-6_2

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