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A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks

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Abstract

Urban travel demand, consisting of thousands or millions of origin–destination trips, can be viewed as a large-scale weighted directed graph. The paper applies a complex network-motivated approach to understand and characterize urban travel demand patterns through analysis of statistical properties of origin–destination demand networks. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand mobility characteristics in urban areas. The paper explores statistical properties of the complex weighted network of urban trips of the selected cities. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results provide a quantitative characterization of the network structure of origin–destination demand in cities, suggesting that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities.

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References

  • Axhausen, K., Garling, T.: Activity-based approaches to travel analysis: conceptual frameworks, models, and research problems. Transp. Rev. 12(4), 323–341 (1992)

    Article  Google Scholar 

  • Bazzani, A., Giorgini, B., Rambaldi, S., Gallotti, R., Giovannini, L.: Statistical laws in urban mobility from microscopic GPS data in the area of Florence. J. Stat. Mech. 2010, P05001 (2010)

    Article  Google Scholar 

  • Betty, M.: The new science of cities. MIT press, Cambridge (2013)

    Google Scholar 

  • Bowman, J.L., Ben-Akiva, M.E.: Activity-based disaggregate travel demand model system with activity schedules. Transp. Res. A 35, 1–28 (2001)

    Article  Google Scholar 

  • Brockmann, D.: Human mobility and spatial disease dynamics. Reviews of nonlinear dynamics and complexity, pp. 1–24. Wiley-VCH, Weinheim (2009)

    Google Scholar 

  • Brockmann, D.: Statistical mechanics: the physics of where to go. Nat. Phys. News Views 6, 720–721 (2010)

    Google Scholar 

  • Brockmann, D., Helbing, D.: The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Calabrese, F., Di Lorenzo, G., Liu, C.L., Ratti, C.: Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Comput. 10(4), 36–44 (2011)

    Article  Google Scholar 

  • Carrasco, J.A., Miller, E.J.: Exploring the propensity to perform social activities: a social network approach. Transportation 33(5), 463–480 (2006)

    Article  Google Scholar 

  • Chen, Y., Frei, A., Mahmassani, H. S.: Exploring activity and destination choice behavior in social networking data. Proceedings of the transportation research board 94th annual meeting (No. 15-5808), Washington (2015)

  • Clauset, A., Shilazi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. Soc. Ind. Appl. Math. Rev. 51(4), 661–703 (2009)

    Google Scholar 

  • Colak, S., Schneider, C.M., Wang, P., González, M.C.: On the role of spatial dynamics and topology on network flows. New J. Phys. 15, 113037 (2013)

    Article  Google Scholar 

  • Çolak, S., Alexander, L.P., Alvim, B.G., Mehndiretta, S.R., González, M.C.: Analyzing cell phone location data for urban travel: current methods, limitations and opportunities. Trans. Res. Rec. 2526, 126–135 (2015)

    Article  Google Scholar 

  • Costa, Da F., Baggio, L.R.: The web connections between tourism companies: structure and dynamics. Physica A 388, 4286–4296 (2009)

    Article  Google Scholar 

  • Daganzo, C.: Urban gridlock: macroscopic modeling and mitigation approaches. Transp. Res. B 41(1), 49–62 (2007)

    Article  Google Scholar 

  • Downs, R.M., Stea, D.: Image and environments. In: Downs, R.M., Stea, D. (eds.) Cognitive maps and spatial behavior: process and products. Aldine, London (1973)

    Google Scholar 

  • Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Nat. Acad. Sci. USA 106(36), 15274–15278 (2009)

    Article  Google Scholar 

  • Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationship of the internet topology. ACM SIGCOMM Comp. Commun. Rev. 29, 251–262 (1999)

    Article  Google Scholar 

  • Fan, Y., Khattak, A.: Urban form, individual spatial footprints, and travel: examination of space-use behavior. Transp. Res. Rec. 2082, 98–106 (2008)

    Article  Google Scholar 

  • Geroliminis, N., Daganzo, C.: Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transp. Res. B 45(3), 605–617 (2008)

    Article  Google Scholar 

  • Golledge, R.G., Stimson, R.J.: Spatial behavior: a geographic perspective. Guilford, New York (1997)

    Google Scholar 

  • González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)

    Article  Google Scholar 

  • Gould, P., White, R.: Mental maps. Pernguin Books, Harmondsworth (1974)

    Book  Google Scholar 

  • Hasan, S., Schneider, C.M., Ukkusuri, S.V., González, M.C.: Spatio-temporal patterns of urban human mobility. J. Stat. Phys. 151, 304–318 (2013)

    Article  Google Scholar 

  • Iqbal, M.S., Choudhury, C.F., Wang, P., González, M.C.: Development of origin–destination matrices using mobile phone call data. Trans. Res. 40, 63–74 (2014)

    Google Scholar 

  • Jiang, B., Yin, J., Zhao, S.: Characterizing the human mobility pattern in a large street network. Phys. Rev. 80, 1–11 (2009)

    Google Scholar 

  • Joubert, J.W., Axhausen, K.: A complex network approach to understand commercial vehicle movement. Transportation 40(3), 729–750 (2013)

    Article  Google Scholar 

  • Kamruzzaman, Md, Hine, J.: Analysis of rural activity spaces and transport disadvantage using a multi-method approach. Transp. Policy 19(1), 105–120 (2012)

    Article  Google Scholar 

  • Kang, C., Ma, X., Tong, D., Liu, Y.: Intra-urban human mobility patterns: an urban morphology perspective. Physica A 391, 1702–1717 (2012)

    Article  Google Scholar 

  • Keegan, B., Ahmed, M.A., Williams, D., Srivastava, J., Contractor, N.: Dark gold: statistical properties of clandestine networks in massively multiplayer online games. IEEE Second Int. Conf. Soc. Comput. (2010). doi:10.1145/1925041.1925043

    Google Scholar 

  • Kelley, R., Ideker, T.: Systematic interpretation of genetic interactions using protein networks. Nat. Biotechnol. 23, 561–566 (2005)

    Article  Google Scholar 

  • Kim, J., Mahmassani, H.S.: Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. Trans. Res. C 59, 375–390 (2015)

    Article  Google Scholar 

  • Liang, X., Zheng, X., Lv, W., Zhu, T., Xu, K.: The scaling of human mobility by taxis is exponential. Physica A 391, 2135–2144 (2012)

    Article  Google Scholar 

  • Liang, X., Zhao, J., Dong, L., Xu, K.: Unraveling the origin of exponential law in intra-urban human mobility. Sci. Rep. 3, 2983 (2013)

    Article  Google Scholar 

  • Lynch, K.: The image of the city. MIT Press, Cambridge (1960)

    Google Scholar 

  • Mahmassani, H.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Net. Spat. Econ. 1(3–44), 267–292 (2001)

    Article  Google Scholar 

  • Mahmassani, H., Saberi, M., Zockaie, A.: Urban network gridlock: theory, characteristics, and dynamics. Trans. Res. C 36, 480–497 (2013)

    Article  Google Scholar 

  • Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Nat. Acad. Sci. USA 98(2), 404–409 (2001)

    Article  Google Scholar 

  • Newman, M.: Networks: an introduction. Oxford University Press, Oxford (2010)

    Book  Google Scholar 

  • Newman, M., Park, J.: Why social networks are different from other types of networks. Phys. Rev. E 68(3), 036122 (2003)

    Article  Google Scholar 

  • Nguyen, T., Szymanski, B.: Using location-based social networks to validate human mobility and relationships models. Proc. IEEE/ACM Int. Conf. Adv. Soc. Netw. Anal. Min. (2012). doi:10.1109/ASONAM.2012.210

    Google Scholar 

  • Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., Mascolo, C.: A tale of many cities: universal patterns in human urban mobility. Plos One 7(5), e37027 (2012). doi:10.1371/journal.pone.0037027

    Article  Google Scholar 

  • Pearson, D., Ellis, P., Farnsworth, S.: Calibration of a past year travel demand model for model evaluation. Texas Trans. Inst. (2002). doi:10.1016/j.procs.2012.08.001. (Report No. FHWA/TX-03/4198-2)

    Google Scholar 

  • Pendalaya, R., Bhat, C.: Validation and assessment of activity-based travel demand model systems. Proc. Innov. Travel Demand Model. 2, 157–158 (2006)

    Google Scholar 

  • Peng, C., Jin, X., Wong, K.-C., Shi, M., Liò, P.: Collective human mobility pattern from taxi trips in urban area. Plos One 7, e34487 (2012)

    Article  Google Scholar 

  • Rai, R.K., Balmer, M., Rieser, M., Vaze, V.S., Schönfelder, S., Axhausen, K.W.: Capturing human activity spaces: new geometries. Trans. Res. Rec. 2021, 70–80 (2007)

    Article  Google Scholar 

  • Ratti, C., Frenchman, D., Pulselli, R.M., Williams, S.: Mobile landscapes: using location data from cell phones for urban analysis. Environ. Plan. 33, 727–748 (2006)

    Article  Google Scholar 

  • Riccardo, G., Armando, B., Sanro, R.: Towards a statistical physics of human mobility. Int. J. Mod. Phys. C 23(9), 1250061 (2012)

    Article  Google Scholar 

  • Roth, C., Kang, S.M., Batty, M., Barthélemy, M.: Structure of urban movements: polycentric activity and entangled hierarchical flows. Plos One 6, e15923 (2011)

    Article  Google Scholar 

  • Rual, J.F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G.F., Gibbons, F.D., Dreze, M., Ayivi-Guedehoussou, N., Klitgord, N., Simon, C., Boxem, M., Milstein, S., Rosenberg, J., Goldberg, D.S., Zhang, L.V., Wong, S.L., Franklin, G., Li, S., Albala, J.S., Lim, J., Fraughton, C., Llamosas, E., Cevik, S., Bex, C., Lamesch, P., Sikorski, R.S., Vandenhaute, J., Zoghbi, H.Y., Smolyar, A., Bosak, S., Sequerra, R., Doucette-Stamm, L., Cusick, M.E., Hill, D.E., Roth, F.P., Vidal, M.: Towards a proteome-scale map of the human protein–protein interaction network. Nature 437(7062), 1173–1178 (2005)

    Article  Google Scholar 

  • Saberi, M., Mahmassani, H., Hou, T., Zockaie, A.: Estimating network fundamental diagram using three-dimensional vehicle trajectories: extending Edie’s definitions of traffic flow variables to networks. Transp. Res. Rec. 2422, 12–20 (2014)

    Article  Google Scholar 

  • Schneider, C.M., Belik, V., Couronné, T., Smoreda, Z., González, M.C.: Unravelling daily human mobility motifs. J. R. Soc. Interface 10, 1–8 (2013)

    Article  Google Scholar 

  • Schonfelder, A., Axhausen, K.W.: Activity spaces: measures of social exclusion? Transp. Policy 10(4), 273–286 (2003)

    Article  Google Scholar 

  • Siganos, G., Faloutsos, M., Faloutsos, P., Faloutsos, C.: Power laws and the AS-level internet topology. IEEE/ACM Trans. Netw. 11(4), 514–524 (2003)

    Article  Google Scholar 

  • Simini, F., González, M.C., Maritan, A., Barabási, A.L.: A universal model for mobility and migration patterns. Nature 484, 96–100 (2012)

    Article  Google Scholar 

  • Song, C., Koren, T., Wang, P., Barabási, A.: Modelling the scaling properties of human mobility. Nat. Phys. 6, 818–823 (2010)

    Article  Google Scholar 

  • Theriault, M., Claramunt, C., Seguin, A.M., Villeneuve, P.: Temporal GIS and statistical modelling of personal lifelines. Adv. Spat. Data Handl. (2002). doi:10.1109/DEXA.1999.795202

    Google Scholar 

  • Thiemann, C., Theis, F., Grady, D., Brune, R., Brockmann, D.: The structure of borders in a small world. Plos One 5(11), e15422 (2010)

    Article  Google Scholar 

  • Timmermans, H.J., Zhang, J.: Modeling household activity travel behavior: examples of state of the art modeling approaches and research agenda. Transp. Res. B 43(2), 187–190 (2009)

    Article  Google Scholar 

  • Toole, J.L., Colak, S., Sturt, B., Alexandre, L., Evsukoff, A., González, M.C.: The path most travelled: travel demand estimation using big data resources. Transp. Res. C (2015). doi:10.1016/j.trc.2015.04.022

    Google Scholar 

  • Virkar, Y., Clauset, A.: Power-law distributions in binned empirical data. Ann. Appl. Stat. 8(1), 89–119 (2014)

    Article  Google Scholar 

  • Wang, P., Hunter, T., Bayen, A., Schechtner, K., González, M.C.: Understanding road usage patterns in urban areas. Sci. Rep. (2012). doi:10.1038/srep01001

    Google Scholar 

  • Watling, D., Hazelton, M.L.: The dynamics and equilibria of day-to-day assignment models. Netw. Spat. Eco. 3(3), 349–370 (2003)

    Article  Google Scholar 

  • Watts, D.J.: The science of a connected age. Six degrees. W. W. Norton & Co. Inc., New York (2003)

    Google Scholar 

  • Widhalm, P., Yang, Y., Ulm, M., Athavale, S., González, M.C.: Discovering urban activity patterns in cell phone data. Transportation 42(4), 597–623 (2015)

    Article  Google Scholar 

  • Woolley-Meza, O., Thiemann, C., Grady, D., Lee, J.J., Seebens, H., Blasius, B., Brockmann, D.: Complexity in human transportation networks: a comparative analysis of worldwide air transportation and global cargo ship movements. Eur. Phys. J. B 84, 589–600 (2011)

    Article  Google Scholar 

  • Yagi, S., Mohammadian, A.: An activity-based microsimulation model of travel demand in the jakarta metropolitan area. J. Choice Model. 3(1), 32–57 (2010)

    Article  Google Scholar 

  • Ye, Q., Wu, B., Wang, B.: Distance distribution and average shortest path length estimation in real-world networks. Adv. Data Min. Appl. 6440, 322–333 (2010)

    Article  Google Scholar 

  • Yook, S.H., Jeong, H., Barabasi, A.L.: Modeling the internet’s large-scale topology. Proc. Nat. Acad. Sci. USA 99(22), 13382–13386 (2002)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Institute of Transport Studies, Monash University, Melbourne, Australia

    Meead Saberi & Amir Hosseini

  2. Transportation Center, Northwestern University, Evanston, USA

    Hani S. Mahmassani

  3. Institute for Theoretical Biology, Humboldt Universität zu Berlin, Berlin, Germany

    Dirk Brockmann

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  1. Meead Saberi
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  2. Hani S. Mahmassani
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  3. Dirk Brockmann
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  4. Amir Hosseini
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Correspondence to Meead Saberi.

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Saberi, M., Mahmassani, H.S., Brockmann, D. et al. A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks. Transportation 44, 1383–1402 (2017). https://doi.org/10.1007/s11116-016-9706-6

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