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Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data

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Abstract

The travel behavior of passengers from the transportation hub within the city area is critical for travel demand analysis, security monitoring, and supporting traffic facilities designing. However, the traditional methods used to study the travel behavior of the passengers inside the city are time and labor consuming. The records of the cellular communication provide a potential huge data source for this study to follow the movement of passengers. This study focuses on the passengers’ travel behavior of the Hongqiao transportation hub in Shanghai, China, utilizing the mobile phone data. First, a systematic and novel method is presented to extract the trip information from the mobile phone data. Several key travel characteristics of passengers, including passengers traveling inside the city and between cities, are analyzed and compared. The results show that the proposed method is effective to obtain the travel trajectories of mobile phone users. Besides, the travel behavior of incity passengers and external passengers are quite different. Then, the correlation analysis of the passengers’ travel trajectories is provided to research the availability of the comprehensive area. Moreover, the results of the correlation analysis further indicate that the comprehensive area of the Hongqiao hub plays a relatively important role in passengers’ daily travel.

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References

  • Alexander, L., Jiang, S., Murga, M., González, M.C.: Origin–destination trips by purpose and time of day inferred from mobile phone data. Transp. Res. C 58, 240–250 (2015)

    Article  Google Scholar 

  • Asakura, Y., Hato, E.: Tracking survey for individual travel behaviour using mobile communication instruments. Transp. Res. C 12(3), 273–291 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira, J., Ratti, C.: Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp. Res. C 26, 301–313 (2013)

    Article  Google Scholar 

  • Chen, C., Bian, L., Ma, J.: From traces to trajectories: How well can we guess activity locations from mobile phone traces? Transp. Res. C 46, 326–337 (2014)

    Article  Google Scholar 

  • Cheung, C.Y., Lam, W.H.: Pedestrian route choices between escalator and stairway in MTR stations. J. Transp. Eng. 124(3), 277–285 (1998)

    Article  Google Scholar 

  • De Berg, M., Van Kreveld, M., Overmars, M., Schwarzkopf, O., Overmars, M.H.: Computational Geometry: Algorithms and Applications, 3rd edn, pp. 86–89. Springer, Berlin (2008)

    Book  Google Scholar 

  • Fang, J., Xue, M., Qiu, T.Z. (2014): Anonymous cellphone-based large-scale origin–destination data collection: case studies in China. In: Proceedings of Transportation Research Board 93rd Annual Meeting, Washington, D.C., No. 14-1567 (2014)

  • Frias-Martinez, V., Soguero, C., Frias-Martinez, E.: Estimation of urban commuting patterns using cellphone network data. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing, Beijing, China, pp. 9–16 (2012)

  • Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)

    Article  Google Scholar 

  • He, S., Cheng, Y., Ding, F., Zhang, J., Ran, B.: Extended Kalman filter-based freeway traffic state estimation using cellphone activity data. In: Proceedings of Transportation Research Board 95th Annual Meeting, Washington, D.C., No. 16-3728 (2016)

  • Jiang, S., Ferreira, J., González, M.C.: Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans. Big Data 3(2), 208–219 (2017)

    Article  Google Scholar 

  • King, D., Srikukenthiran, S., Shalaby, A.: Using simulation to analyze crowd congestion and mitigation at Canadian subway interchanges: case of Bloor-Yonge Station, Toronto, Ontario. Transp. Res. Rec. 2417, 27–36 (2014)

    Article  Google Scholar 

  • Li, H.: Using mobile phone data to analyze origin–destination travel flow dynamics for city of Pasadena, CA and surrounding area. In: Proceedings of Transportation Research Board 94th Annual Meeting, Washington, D.C., No. 15-0804 (2015)

  • Pan, C., Lu, J., Di, S., Ran, B.: Cellular-based data-extracting method for trip distribution. Transp. Res. Rec. 1945, 33–39 (2006)

    Article  Google Scholar 

  • Phithakkitnukoon, S., Horanont, T., Di Lorenzo, G., Shibasaki, R., Ratti, C.: Activity-aware map: identifying human daily activity pattern using mobile phone data. In: International Workshop on Human Behavior Understanding, Istanbul, Turkey, pp. 14–25 (2010)

  • Rokib, S.A., Karim, M.A., Qiu, T.Z., Kim, A.: Origin–destination trip estimation from anonymous cell phone and foursquare data. In: Proceedings of Transportation Research Board 94th Annual Meeting, Washington, D.C., No. 15-2379 (2015)

  • Sagl, G., Delmelle, E., Delmelle, E.: Mapping collective human activity in an urban environment based on mobile phone data. Cartogr. Geogr. Inform. Sci. 41(3), 272–285 (2014)

    Article  Google Scholar 

  • Shanghai City Comprehensive Transportation Planning Institute. The fourth travel survey of residents in Shanghai. Shanghai (2010)

  • Shanghai Hongqiao Central Business District. Passenger flow information of Hongqiao hub in 2013. http://www.shhqcbd.gov.cn/html/shhq/shhq_2013/Info/Detail_6403.htm (2013). Accessed 15 May 2015

  • Shen, Y., Kwan, M.P., Chai, Y.: Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China. J. Transp. Geogr. 32, 1–11 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Srikukenthiran, S., Fisher, D., Shalaby, A., King, D.: Pedestrian route choice of vertical facilities in subway stations. Transp. Res. Rec. 2351, 115–123 (2013)

    Article  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 

  • Xu, S., Freund, R.M., Sun, J.: Solution methodologies for the smallest enclosing circle problem. Comput. Optim. Appl. 25, 283–292 (2003)

    Article  Google Scholar 

  • Zhang, Q., Han, B., Li, D.: Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations. Transp. Res. C 16(5), 635–649 (2008)

    Article  Google Scholar 

  • Zhang, Y., Qin, X., Dong, S., Ran, B. (2010): Daily OD matrix estimation using cellular probe data. In: Proceedings of Transportation Research Board 89rd Annual Meeting, Washington, D.C., No. 10-2472 (2010)

  • Zhang, J., He, S., Wang, W., Zhan, F.: Accuracy analysis of freeway traffic speed estimation based on the integration of cellular probe system and loop detectors. J. Intell. Transp. Syst. 19(4), 411–426 (2015)

    Article  Google Scholar 

  • Zhong, G., Wan, X., Zhang, J., Yin, T., Ran, B.: Characterizing passenger flow for a transportation hub based on mobile phone data. IEEE T. Intell. Transp. 18(6), 1507–1518 (2017)

    Google Scholar 

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Acknowledgements

This study is partially supported by the Information Technology Research Project of Ministry of Transport of China (No. 2015364X16030) and the National Natural Science Foundation of China (No. 61620106002). The support provided by China Scholarship Council (CSC) during a visit of G. Zhong to UW-Madison is acknowledged.

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Correspondence to Gang Zhong.

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Zhong, G., Yin, T., Zhang, J. et al. Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data. Transportation 46, 1713–1736 (2019). https://doi.org/10.1007/s11116-018-9876-5

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  • DOI: https://doi.org/10.1007/s11116-018-9876-5

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