Mobility Pattern of Taxi Passengers at Intra-Urban Scale: Empirical Study of Three Cities

References

[1] Hufnagel L, Brockmann D, Geisel T. Forecast and control of epidemics in a globalized world. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(42): 15124–15129.10.1073/pnas.0308344101Suche in Google Scholar PubMed PubMed Central

[2] Ahas R, Aasa A, Markü, et al. Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Management, 2007, 28(3): 898–910.10.1016/j.tourman.2006.05.010Suche in Google Scholar

[3] Kaltenbrunner A, Meza R, Grivolla J, et al. Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 2010, 6(4): 455–466.10.1016/j.pmcj.2010.07.002Suche in Google Scholar

[4] Lu X, Bengtsson L, Holme P. Predictability of population displacement after the 2010 Haiti earthquake. Proceedings of the National Academy of Sciences, 2012, 109(29): 11576–11581.10.1073/pnas.1203882109Suche in Google Scholar PubMed PubMed Central

[5] Liu X, Gong L, Gong Y, et al. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 2015, 43: 78–90.10.1016/j.jtrangeo.2015.01.016Suche in Google Scholar

[6] Lazer D, Pentland A S, Adamic L, et al. Life in the network: The coming age of computational social science. Science, 2007, 323(5915): 721.10.1126/science.1167742Suche in Google Scholar PubMed PubMed Central

[7] Brockmann D, Hufnagel L, Geisel T. The scaling laws of human travel. Nature, 2006, 439(7075): 462–465.10.1038/nature04292Suche in Google Scholar PubMed

[8] González M C, Hidalgo C A, Barabasi A L. Understanding individual human mobility patterns. Nature, 2008, 453(7196): 779–782.10.1038/nature06958Suche in Google Scholar PubMed

[9] Song C, Koren T, Wang P, et al. Modelling the scaling properties of human mobility. Nature Physics, 2010, 6(10): 818–823.10.1038/nphys1760Suche in Google Scholar

[10] Han X P, Hao Q, Wang B H, et al. Origin of the scaling law in human mobility: Hierarchy of traffic systems. Physical Review E, 2011, 83(3): 036117.10.1103/PhysRevE.83.036117Suche in Google Scholar PubMed

[11] Jiang B, Yin J, Zhao S. Characterizing the human mobility pattern in a large street network. Physical Review E, 2009, 80(2): 021136.10.1103/PhysRevE.80.021136Suche in Google Scholar PubMed

[12] Yao C Z, Lin J N. A study of human mobility behavior dynamics: A perspective of a single vehicle with taxi. Transportation Research Part A: Policy and Practice, 2016, 87: 51–58.10.1016/j.tra.2016.03.005Suche in Google Scholar

[13] Bazzani A, Giorgini B, Rambaldi S, et al. Statistical laws in urban mobility from microscopic GPS data in the area of Florence. Journal of Statistical Mechanics: Theory and Experiment, 2010, 5: 05001.10.1088/1742-5468/2010/05/P05001Suche in Google Scholar

[14] Liang X, Zheng X, Lü W, et al. The scaling of human mobility by taxis is exponential. Physica A: Statistical Mechanics and Its Applications, 2012, 391(5): 2135–2144.10.1016/j.physa.2011.11.035Suche in Google Scholar

[15] Liang X, Zhao J, Dong L, et al. Unraveling the origin of exponential law in intra-urban human mobility. arXiv preprint arXiv, 2013, 1305: 6364.10.1038/srep02983Suche in Google Scholar PubMed PubMed Central

[16] Wang W, Pan L, Yuan N, et al. A comparative analysis of intra-city human mobility by taxi. Physica A: Statistical Mechanics and Its Applications, 2015, 420: 134–14710.1016/j.physa.2014.10.085Suche in Google Scholar

[17] Veloso M, Phithakkitnukoon S, Bento C, et al. Exploratory study of urban flow using taxi traces. In First Workshop on Pervasive Urban Applications (PURBA) in conjunction with Pervasive Computing, 2011, San Francisco, California, USA.Suche in Google Scholar

[18] Roth C, Kang S M, Batty M, et al. Structure of urban movements: polycentric activity and entangled hierarchical flows. PloS One, 2010, 6(1): 15923.10.1371/journal.pone.0015923Suche in Google Scholar PubMed PubMed Central

[19] Yue Y, Zhuang Y, Li Q, et al. Mining time-dependent attractive areas and movement patterns from taxi trajectory data. In Geoinformatics, 2009 17th International Conference, 2009.Suche in Google Scholar

[20] Huang X, Zhao Y, Ma C, et al. TrajGraph: A graph-based visual analytics approach to studying urban network centralities using taxi trajectory data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 160–169.10.1109/TVCG.2015.2467771Suche in Google Scholar PubMed

[21] Bastani F, Huang Y, Xie X, et al. A greener transportation mode:flexible routes discovery from GPS trajectory data. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2011: 405–408.10.1145/2093973.2094034Suche in Google Scholar

[22] Yuan J, Zheng Y, Zhang L, et al. Where to find my next passenger. In Proceedings of the 13th International Conference on Ubiquitous Computing, 2011: 109–118.10.1145/2030112.2030128Suche in Google Scholar

[23] Liu L, Andris C, Ratti C. Uncovering cabdrivers’ behavior patterns from their digital traces. Computers, Environment and Urban Systems, 2010, 34(6): 541–548.10.1016/j.compenvurbsys.2010.07.004Suche in Google Scholar

[24] Li B, Zhang D, Sun L, et al. Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In Pervasive Computing and Communications Workshops, 2011: 63–68.10.1109/PERCOMW.2011.5766967Suche in Google Scholar

[25] Zhang D, Sun L, Li B, Chen C, et al. Understanding taxi service strategies from taxi GPS traces. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 123–135.10.1109/TITS.2014.2328231Suche in Google Scholar

[26] Zheng Y, Liu Y, Yuan J, et al. Urban computing with taxicabs. In Proceedings of the 13th International Conference on Ubiquitous Computing, 2011.10.1145/2030112.2030126Suche in Google Scholar

[27] Csáji B C, Browet A, Traag V A, et al. Exploring the mobility of mobile phone users. Physica A: Statistical Mechanics and Its Applications, 2013, 392(6): 1459–1473.10.1016/j.physa.2012.11.040Suche in Google Scholar

[28] Giannotti F, Nanni M, Pinelli F, et al. Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007: 330–339.10.1145/1281192.1281230Suche in Google Scholar

[29] Cullen A C, Frey H C. Probabilistic techniques in exposure assessment: A handbook for dealing with variability and uncertainty in models and inputs. Springer Science & Business Media, 1999.Suche in Google Scholar

[30] Yamaoka K, Nakagawa T, Uno T. Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. Journal of Pharmacokinetics and Biopharmaceutics, 1978, 6(2): 165–175.10.1007/BF01117450Suche in Google Scholar PubMed

[31] Chen S, Gopalakrishnan P. Speaker, environment and channel change detection and clustering via the bayesian information criterion. In Proc. DARPA Broadcast News Transcription and Understanding Workshop, 1998, 8: 127–132.Suche in Google Scholar

[32] Murshudov G N, Vagin A A, Dodson E J. Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallographica Section D: Biological Crystallography, 1997, 53(3): 240–255.10.1107/S0907444996012255Suche in Google Scholar PubMed

[33] Posada D, Buckley T R. Model selection and model averaging in phylogenetics: Advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Systematic Biology, 2004, 53(5): 793–808.10.1080/10635150490522304Suche in Google Scholar PubMed

[34] Holmgren E B. The PP plot as a method for comparing treatment effects. Journal of the American Statistical Association, 1995, 90(429): 360–365.10.1080/01621459.1995.10476520Suche in Google Scholar