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Mobile phone data in transportation research: methods for benchmarking against other data sources

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Abstract

The ubiquity of personal cellular phones in society has led to a surging interest in using Big Data generated by mobile phones in transport research. Studies have suggested that the vast amount of data could be used to estimate origin–destination (OD) matrices, thereby potentially replacing traditional data sources such as travel surveys. However, constructing OD matrices from mobile phone data (MPD) entails multiple challenges, and the lack of ground truth hampers the evaluation and validation of the estimated matrices. Furthermore, national laws may prohibit the distribution of MPD for research purposes, compelling researchers to work with pre-compiled OD matrices with no insight into the methods used. In this paper, we analyse a set of such pre-compiled OD matrices from the greater Oslo area and perform validation procedures against several sources to assess the quality and robustness of the OD matrices as well as their usefulness in transportation planning applications. We find that while the OD matrices correlate well with other sources at a low resolution, the reliability decreases when a finer level of detail is chosen, particularly when comparing shorter trips between neighbouring areas. Our results suggest that coarseness of data and privacy concerns restrict the usefulness of MPD in transport research in the case where OD matrices are pre-compiled by the operator.

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References

  • Aguiléra, V., Allio, S., Benezech, V., Combes, F., Milion, C.: Using cell phone data to measure quality of service and passenger flows of Paris transit system. Transp. Res. Part C Emerg. Technol. 43, 198–211 (2014)

    Article  Google Scholar 

  • Ahas, R., Aasa, A., Silm, S., Tiru, M.: Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: case study with mobile positioning data. Transp. Res. Part C Emerg. Technol. 18, 45–54 (2010)

    Article  Google Scholar 

  • Aksehirli, E., Li, Y.: Predicting MRT trips in Singapore by creating a mobility behavior model based on GSM data. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 632–639. IEEE (2018)

  • 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. Part C Emerg. Technol. 58, 240–250 (2015)

    Article  Google Scholar 

  • Asgari, F.: Inferring User Multimodal Trajectories from Cellular Network Metadata in Metropolitan Areas. Institut National des Télécommunications, Évry (2016)

    Google Scholar 

  • Bachir, D., Khodabandelou, G., Gauthier, V., El Yacoubi, M., Puchinger, J.: Inferring dynamic origin–destination flows by transport mode using mobile phone data. Transp. Res. Part C Emerg. Technol. 101, 254–275 (2019)

    Article  Google Scholar 

  • Bassolas, A., Ramasco, J.J., Herranz, R., Cantú-Ros, O.G.: Mobile phone records to feed activity-based travel demand models: MATSim for studying a cordon toll policy in Barcelona. Transp. Res. Part A Policy Pract. 121, 56–74 (2019)

    Article  Google Scholar 

  • Becker, R., Cáceres, R., Hanson, K., Isaacman, S., Loh, J.M., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A., Volinsky, C.: Human mobility characterization from cellular network data. Commun. ACM 56, 74–82 (2013)

    Article  Google Scholar 

  • Calabrese, F., Di Lorenzo, G., Liu, L., Ratti, C.: Estimating Origin–Destination Flows Using Opportunistically Collected Mobile Phone Location Data from One Million Users in Boston Metropolitan Area (2011a)

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chatzikokolakis, K., Elsalamouny, E., Palamidessi, C., Pazii, A.: Methods for Location Privacy: A comparative overview. In: Foundations and Trends® in Privacy and Security, vol. 1, no. 4, pp. 199–257. Now publishers inc. https://doi.org/10.1561/3300000017 (2017)

  • Chen, C., Ma, J., Susilo, Y., Liu, Y., Wang, M.: The promises of big data and small data for travel behavior (aka human mobility) analysis. Transp. Res. Part C Emerg. Technol. 68, 285–299 (2016)

    Article  Google Scholar 

  • Danafar, S., Piorkowski, M., Krysczcuk, K.: Bayesian framework for mobility pattern discovery using mobile network events. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp. 1070–1074. IEEE (2017)

  • Di Lorenzo, G., Sbodio, M., Calabrese, F., Berlingerio, M., Pinelli, F., Nair, R.: Allaboard: visual exploration of cellphone mobility data to optimise public transport. IEEE Trans. Visual Comput. Graph. 22, 1036–1050 (2015)

    Article  Google Scholar 

  • Doyle, J., Hung, P., Kelly, D., Mcloone, S.F., Farrell, R.: Utilising Mobile Phone Billing Records for Travel Mode Discovery (2011)

  • Drageide, V.: Towards Privacy Management of Information Systems. The University of Bergen, Bergen (2009)

    Google Scholar 

  • Forrest, T.L., Pearson, D.F.: Comparison of trip determination methods in household travel surveys enhanced by a global positioning system. Transp. Res. Rec. 1917, 63–71 (2005)

    Article  Google Scholar 

  • García, P., Herranz, R., Javier, J.: Big data analytics for a passenger-centric air traffic management system. Presented at the 6th SESAR Innovation Days, Delft, Netherlands (2016)

  • García-Albertos, P., Picornell, M., Salas-Olmedo, M.H., Gutiérrez, J.: Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis. Transp. Res. Part A Policy Pract. 125, 294–307 (2019)

    Article  Google Scholar 

  • Gundlegård, D., Rydergren, C., Breyer, N., Rajna, B.: Travel demand estimation and network assignment based on cellular network data. Comput. Commun. 95, 29–42 (2016)

    Article  Google Scholar 

  • Hjorthol, R., Engebretsen, Ø., Uteng, T.P.: Den nasjonale Reisevaneundersøkelsen 2013/14: Nøkkelrapport. Transportøkonomisk institutt, Oslo (2014)

    Google Scholar 

  • Holleczek, T., Yin, S., Jin, Y., Antonatos, S., Goh, H.L., Low, S., Shi-Nash, A.: Traffic measurement and route recommendation system for mass rapid transit (MRT). In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1859–1868. ACM (2015)

  • Horn, C., Kern, R.: Deriving public transportation timetables with large-scale cell phone data. Procedia Comput. Sci. 52, 67–74 (2015)

    Article  Google Scholar 

  • Huang, Z., Ling, X., Wang, P., Zhang, F., Mao, Y., Lin, T., Wang, F.-Y.: Modeling real-time human mobility based on mobile phone and transportation data fusion. Transp. Res. Part C Emerg. Technol. 96, 251–269 (2018)

    Article  Google Scholar 

  • Huang, H., Cheng, Y., Weibel, R.: Transport mode detection based on mobile phone network data: a systematic review. Transp. Res. Part C Emerg. Technol. 101, 297–312 (2019)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Martonosi, M., Rowland, J., Varshavsky, A.: Ranges of human mobility in Los Angeles and New York. In 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 88–93. IEEE (2011)

  • Kalatian, A., Shafahi, Y.: Travel mode detection exploiting cellular network data. In: MATEC Web of Conferences, pp. 03008. EDP Sciences (2016)

  • Larijani, A.N., Olteanu-Raimond, A.-M., Perret, J., Brédif, M., Ziemlicki, C.: Investigating the mobile phone data to estimate the origin destination flow and analysis; case study: Paris region. Transp. Res. Procedia 6, 64–78 (2015)

    Article  Google Scholar 

  • Li, G., Chen, C.-J., Peng, W.-C., YI, C.-W.: Estimating crowd flow and crowd density from cellular data for mass rapid transit. In: Proceedings of the 6th International Workshop on Urban Computing (in Conjunction with ACM KDD 2017) (2017)

  • Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., Vilhuber, L.: Privacy: theory meets practice on the map. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 277–286. IEEE (2008)

  • Mamei, M., Bicocchi, N., Lippi, M., Mariani, S., Zambonelli, F.: Evaluating origin–destination matrices obtained from CDR data. Sensors (Basel) 19, 4470 (2019)

    Article  Google Scholar 

  • Montero, L., Ros-Roca, X., Herranz, R., Barceló, J.: Fusing mobile phone data with other data sources to generate input OD matrices for transport models. Transp. Res. Procedia 37, 417–424 (2019)

    Article  Google Scholar 

  • Ni, L., Wang, X.C., Chen, X.M.: A spatial econometric model for travel flow analysis and real-world applications with massive mobile phone data. Transp. Res. Part C Emerg. Technol. 86, 510–526 (2018)

    Article  Google Scholar 

  • Phithakkitnukoon, S., Sukhvibul, T., Demissie, M., Smoreda, Z., Natwichai, J., Bento, C.: Inferring social influence in transport mode choice using mobile phone data. EPJ Data Sci. 6, 11 (2017)

    Article  Google Scholar 

  • Poonawala, H., Kolar, V., Blandin, S., Wynter, L., Sahu, S.: Singapore in motion: Insights on public transport service level through farecard and mobile data analytics. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and data mining, pp. 589–598. ACM (2016)

  • Qu, Y., Gong, H., Wang, P.: Transportation mode split with mobile phone data. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 285–289. IEEE (2015)

  • Schjalm, A.: Kvalitetsundersøkelsen for Folke- og boligtelling 1990. In: Norway, S. (ed.) Oslo—Kongsvinger: Statistics Norway (1996)

  • Schlaich, J., Otterstätter, T., Friedrich, M.: Generating trajectories from mobile phone data. In: Proceedings of the 89th Annual Meeting Compendium of Papers. Transportation Research Board of the National Academies (2010)

  • Smoreda, Z., Olteanu-Raimond, A.-M., Couronné, T.: Spatiotemporal data from mobile phones for personal mobility assessment. Transp. Surv. Methods Best Pract. Decis. Mak. 41, 745–767 (2013)

    Article  Google Scholar 

  • Sørensen, A.Ø., Bjelland, J., Bull-Berg, H., Landmark, A.D., Akhtar, M.M., Olsson, N.O.: Use of mobile phone data for analysis of number of train travellers. J. Rail Transp. Plan. Manag. 8, 123–144 (2018)

    Google Scholar 

  • Statistics Norway: Classification of Statistical Tract and Basic Statistical Unit (2019). Available: https://www.ssb.no/en/klass/klassifikasjoner/1. Accessed 29 Nov 2019

  • Stopher, P.R., Greaves, S.P.: Household travel surveys: Where are we going? Transp. Res. Part A Policy Pract. 41, 367–381 (2007)

    Article  Google Scholar 

  • Stopher, P., Fitzgerald, C., Xu, M.: Assessing the accuracy of the Sydney Household Travel Survey with GPS. Transportation 34, 723–741 (2007)

    Article  Google Scholar 

  • Toole, J.L., Colak, S., Sturt, B., Alexander, L.P., Evsukoff, A., González, M.C.: The path most traveled: travel demand estimation using big data resources. Transp. Res. Part C Emerg. Technol. 58, 162–177 (2015)

    Article  Google Scholar 

  • Vazifeh, M.M., Zhang, H., Santi, P., Ratti, C.: Optimizing the deployment of electric vehicle charging stations using pervasive mobility data. Transp. Res. Part A Policy Pract. 121, 75–91 (2019)

    Article  Google Scholar 

  • Wang, F., Chen, C.: On data processing required to derive mobility patterns from passively-generated mobile phone data. Transp. Res. Part C Emerg. Technol. 87, 58–74 (2018)

    Article  Google Scholar 

  • Wang, H., Calabrese, F., Di Lorenzo, G., Ratti, C.: Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 318–323. IEEE (2010)

  • Wang, Z., He, S.Y., Leung, Y.: Applying mobile phone data to travel behaviour research: a literature review. Travel Behav. Soc. 11, 141–155 (2018)

    Article  Google Scholar 

  • Wolf, J., Loechl, M., Thompson, M., Arce, C.: Trip rate analysis in GPS-enhanced personal travel surveys. In: Stopher, P.R., Jones, P. (eds.) Transport Survey Quality and Innovation. Emerald Group Publishing Limited, Bingley (2003)

    Google Scholar 

  • World Bank: Mobile Cellular Subscriptions (per 100 People) (2019). Available: https://data.worldbank.org/indicator/it.cel.sets.p2. Accessed 29 Nov 2019

  • Wu, W., Cheu, E.Y., Feng, Y., Le, D.N., Yap, G.E., Li, X.: Studying intercity travels and traffic using cellular network data. In: Mobile Phone Data for Development: Net Mob 2013 (2013)

  • Wu, C., Thai, J., Yadlowsky, S., Pozdnoukhov, A., Bayen, A.: Cellpath: fusion of cellular and traffic sensor data for route flow estimation via convex optimization. Transp. Res. Part C Emerg. Technol. 59, 111–128 (2015)

    Article  Google Scholar 

  • Wu, L., Yang, B., Jing, P.: Travel mode detection based on GPS raw data collected by smartphones: a systematic review of the existing methodologies. Information 7, 67 (2016)

    Article  Google Scholar 

  • Xu, C., Ji, M., Chen, W., Zhang, Z.: Identifying travel mode from GPS trajectories through fuzzy pattern recognition. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pp. 889–893. IEEE (2010)

  • Yamada, Y., Uchiyama, A., Hiromori, A., Yamaguchi, H., Higashino, T.: Travel estimation using control signal records in cellular networks and geographical information. In: 2016 9th IFIP Wireless and Mobile Networking Conference (WMNC), pp. 138–144. IEEE (2016)

  • Zheng, Y., Chen, Y., Li, Q., Xie, X., Ma, W.-Y.: Understanding transportation modes based on GPS data for web applications. ACM Trans. Web (TWEB) 4, 1 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Technology Management, SINTEF, Trondheim, Norway

    Andreas Dypvik Landmark

  2. Department of Mobility and Economics, SINTEF, Trondheim, Norway

    Petter Arnesen, Carl-Johan Södersten & Odd André Hjelkrem

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  1. Andreas Dypvik Landmark
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Dypvik Landmark, A., Arnesen, P., Södersten, CJ. et al. Mobile phone data in transportation research: methods for benchmarking against other data sources. Transportation 48, 2883–2905 (2021). https://doi.org/10.1007/s11116-020-10151-7

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