Predicting Traveling Distances and Unveiling Mobility and Activity Patterns of Individuals from Multisource Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 5
Abstract
This work investigates whether the user-generated data from multiple sources, such as smart cards and social media, can be used to identify main mobility/activity patterns based solely on geo-tagged information. To perform such an analysis, automated models are developed to (1) retrieve user mobility patterns from historical, user-generated data logs, (2) categorize users based on the similarity of their observed mobility patterns, and (3) predict the travel distances of users for participating in future activities. For testing purposes, user-generated data sets from smart card logs and Twitter profiles collected between November 2013 and February 2015 in London are used. User-generated data from 200 smart card and 32 active Twitter users are collected and 6 main clusters are identified based on the mobility/activity pattern similarities of users. Results show that it is possible to integrate data logs from multiple sources to capture the main mobility/activity patterns observed in an area. Results also reveal that the accuracy of the predicted travel distance of one user’s trip can be significantly improved if the user’s previous activities are considered in the prediction process.
Get full access to this article
View all available purchase options and get full access to this article.
References
Alexander, L., S. Jiang, M. Murga, and M. C. González. 2015. “Origin–destination trips by purpose and time of day inferred from mobile phone data.” Transp. Res. Part C: Emerging Technol. 58 (Sep): 240–250. https://doi.org/10.1016/j.trc.2015.02.018.
Arentze, T. A., and H. J. Timmermans. 2004. “A learning-based transportation oriented simulation system.” Transp. Res. Part B: Methodol. 38 (7): 613–633. https://doi.org/10.1016/j.trb.2002.10.001.
Axhausen, K. W., and T. Gärling. 1992. “Activity-based approaches to travel analysis: Conceptual frameworks, models, and research problems.” Transp. Rev. 12 (4): 323–341. https://doi.org/10.1080/01441649208716826.
Baek, S., Y. Lim, S. Rhee, and K. Choi. 2010. “Method for estimating population OD matrix based on probe vehicles.” KSCE J. Civ. Eng. 14 (2): 231–235. https://doi.org/10.1007/s12205-010-0231-4.
Becker, R., R. Cáceres, K. Hanson, S. Isaacman, J. M. Loh, M. Martonosi, J. Rowland, S. Urbanek, A. Varshavsky, and C. Volinsky. 2013. “Human mobility characterization from cellular network data.” Commun. ACM 56 (1): 74–82. https://doi.org/10.1145/2398356.2398375.
Briët, J., and P. Harremoës. 2009. “Properties of classical and quantum Jensen-Shannon divergence.” Phys. Rev. A 79 (5): 052311. https://doi.org/10.1103/PhysRevA.79.052311.
Calabrese, F., G. Di Lorenzo, L. Liu, and C. Ratti. 2011. “Estimating origin-destination flows using opportunistically collected mobile phone location data from one million users in Boston metropolitan area.” IEEE Pervasive Comput. 10 (4): 36–44. https://doi.org/10.1109/MPRV.2011.41.
Cervero, R. 1989. “Jobs-housing balancing and regional mobility.” J. Am. Plann. Assoc. 55 (2): 136–150. https://doi.org/10.1080/01944368908976014.
Chaniotakis, E., C. Antoniou, J. M. S. Grau, and L. Dimitriou. 2016a. “Can social media data augment travel demand survey data?” In Proc., 19th Int. Conf. on Intelligent Transportation Systems (ITSC) 2016, 1642–1647. New York: IEEE.
Chaniotakis, E., C. Antoniou, and F. Pereira. 2016b. “Mapping social media for transportation studies.” IEEE Intell. Syst. 31 (6): 64–70. https://doi.org/10.1109/MIS.2016.98.
Chen, C., J. Ma, Y. Susilo, Y. Liu, and M. Wang. 2016. “The promises of big data and small data for travel behavior (aka human mobility) analysis.” Transp. Res. Part C: Emerging Technol. 68: 285–299. https://doi.org/10.1016/j.trc.2016.04.005.
De Dios Ortuzar, J., and L. G. Willumsen. 2011. Modelling transport. Chichester, UK: Wiley.
Ester, M., H.-P. Kriegel, J. Sander, and X. Xu. 1996. “A density-based algorithm for discovering clusters in large spatial databases with noise.” In Vol. 96 of Proc., KDD, 226–231. Portland, OR: AAAI Press.
Ettema, D., H. Timmermans, and L. van Veghel. 1996. Effects of data collection methods in travel and activity research. Eindhoven, Netherlands: European Institute of Retailing and Services Studies, Eindhoven Univ. of Technology.
Fan, R.-E., K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. 2008. “Liblinear: A library for large linear classification.” J. Mach. Learn. Res. 9 (Aug): 1871–1874.
Flyvbjerg, B. 2005. “Measuring inaccuracy in travel demand forecasting: Methodological considerations regarding ramp up and sampling.” Transp. Res. Part A: Policy Pract. 39 (6): 522–530. https://doi.org/10.1016/j.tra.2005.02.003.
Flyvbjerg, B., M. K. Skamris Holm, and S. L. Buhl. 2006. “Inaccuracy in traffic forecasts.” Trans. Rev. 26 (1): 1–24. https://doi.org/10.1080/01441640500124779.
Gkiotsalitis, K., F. Alesiani, and R. Baldessari. 2014. “Educated rules for the prediction of human mobility patterns based on sparse social media and mobile phone data.” In Proc., 93rd Annual Meeting on Transportation Research Board. Washington, DC: Transportation Research Board.
Gkiotsalitis, K., and O. Cats. 2018. “Reliable frequency determination: Incorporating information on service uncertainty when setting dispatching headways.” Transp. Res. Part C: Emerging Technol. 88 (Mar): 187–207. https://doi.org/10.1016/j.trc.2018.01.026.
Gkiotsalitis, K., and A. Stathopoulos. 2015a. “Optimizing leisure travel: Is bigdata ready to improve the joint leisure activities efficiency?.” In Engineering and applied sciences optimization, 53–71. Cham, Switzerland: Springer.
Gkiotsalitis, K., and A. Stathopoulos. 2015b. “A utility-maximization model for retrieving users’ willingness to travel for participating in activities from big-data.” Transp. Res. Part C: Emerging Technol. 58 (Sep): 265–277. https://doi.org/10.1016/j.trc.2014.12.006.
Gkiotsalitis, K., and A. Stathopoulos. 2016a. “Demand-responsive public transportation re-scheduling for adjusting to the joint leisure activity demand.” Int. J. Transp. Sci. Technol. 5 (2): 68–82. https://doi.org/10.1016/j.ijtst.2016.09.004.
Gkiotsalitis, K., and A. Stathopoulos. 2016b. “Joint leisure travel optimization with user-generated data via perceived utility maximization.” Transp. Res. Part C: Emerging Technol. 68 (Jul): 532–548. https://doi.org/10.1016/j.trc.2016.05.009.
Gkiotsalitis, K., Z. Wu, and O. Cats. 2019. “A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options.” Transp. Res. Part C: Emerging Technol. 98 (Jan): 14–36. https://doi.org/10.1016/j.trc.2018.11.007.
González, M. C., C. A. Hidalgo, and A.-L. Barabási. 2008. “Understanding individual human mobility patterns.” Nature 453 (7196): 779–782. https://doi.org/10.1038/nature06958.
Guo, D., and C. Chen. 2014. “Detecting non-personal and spam users on geo-tagged twitter network.” Trans. GIS 18 (3): 370–384. https://doi.org/10.1111/tgis.12101.
Hasan, S., C. M. Schneider, S. V. Ukkusuri, and M. C. González. 2013. “Spatiotemporal patterns of urban human mobility.” J. Stat. Phys. 151 (1–2): 304–318. https://doi.org/10.1007/s10955-012-0645-0.
Hawelka, B., I. Sitko, E. Beinat, S. Sobolevsky, P. Kazakopoulos, and C. Ratti. 2014. “Geo-located Twitter as proxy for global mobility patterns.” Cartography Geogr. Inf. Sci. 41 (3): 260–271. https://doi.org/10.1080/15230406.2014.890072.
Jurdak, R., K. Zhao, J. Liu, M. AbouJaoude, M. Cameron, and D. Newth. 2015. “Understanding human mobility from Twitter.” PLOS One 10 (7): e0131469. https://doi.org/10.1371/journal.pone.0131469.
Kullback, S., and R. A. Leibler. 1951. “On information and sufficiency.” Ann. Math. Stat. 22 (1): 79–86. https://doi.org/10.1214/aoms/1177729694.
Lee, J. H., S. Gao, and K. G. Goulias. 2015. “Can Twitter data be used to validate travel demand models.” In Proc., 14th Int. Conf. on Travel Behaviour Research. Windsor, UK: International Association for Travel Behaviour Research.
Lin, D.-Y., N. Eluru, S. Waller, and C. Bhat. 2008. “Integration of activity-based modeling and dynamic traffic assignment.” Transp. Res. Rec. 2076 (1): 52–61. https://doi.org/10.3141/2076-06.
Maat, K., H. J. Timmermans, and E. Molin. 2004. “A model of spatial structure, activity participation and travel behavior.” In Proc., 10th World Conf. on Transport Research. Istanbul, Turkey: Istanbul Technical Univ.
Martínez, L. M., and J. M. Viegas. 2013. “A new approach to modelling distance-decay functions for accessibility assessment in transport studies.” J. Transp. Geogr. 26 (Jan): 87–96. https://doi.org/10.1016/j.jtrangeo.2012.08.018.
Miller, E., and M. Roorda. 2003. “Prototype model of household activity-travel scheduling.” Transp. Res. Rec. 1831 (1): 114–121. https://doi.org/10.3141/1831-13.
Munizaga, M. A., and C. Palma. 2012. “Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile.” Transp. Res. Part C: Emerging Technol. 24 (Oct): 9–18. https://doi.org/10.1016/j.trc.2012.01.007.
Noulas, A., S. Scellato, C. Mascolo, and M. Pontil. 2011. “An empirical study of geographic user activity patterns in foursquare.” In Vol. 11 of Proc., 5th Int. AAAI Conf. on Weblogs and Social Media ICwSM, 70–573. Barcelona, Spain: Association for the Advancement of Artificial Intelligence.
Pan, C., J. Lu, S. Di, and B. Ran. 2006. “Cellular-based data-extracting method for trip distribution.” Transp. Res. Rec. 1945 (1): 33–39. https://doi.org/10.1177/0361198106194500105.
Pappalardo, L., F. Simini, S. Rinzivillo, D. Pedreschi, F. Giannotti, and A.-L. Barabási. 2015. “Returners and explorers dichotomy in human mobility.” Nat. Commun. 6: 8166. https://doi.org/10.1038/ncomms9166.
Pelletier, M.-P., M. Trépanier, and C. Morency. 2011. “Smart card data use in public transit: A literature review.” Transp. Res. Part C: Emerging Technol. 19 (4): 557–568. https://doi.org/10.1016/j.trc.2010.12.003.
Pendyala, R., R. Kitamura, A. Kikuchi, T. Yamamoto, and S. Fujii. 2005. “Florida activity mobility simulator: Overview and preliminary validation results.” Transp. Res. Rec. 1921 (1): 123–130. https://doi.org/10.1177/0361198105192100114.
Pereira, F. C., F. Rodrigues, and M. Ben-Akiva. 2015. “Using data from the web to predict public transport arrivals under special events scenarios.” J. Intell. Transp. Syst. 19 (3): 273–288. https://doi.org/10.1080/15472450.2013.868284.
Schubert, E., J. Sander, M. Ester, H. P. Kriegel, and X. Xu. 2017. “DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN.” ACM Trans. Database Syst. (TODS) 42 (3): 1. https://doi.org/10.1145/3068335.
Song, C., Z. Qu, N. Blumm, and A.-L. Barabási. 2010. “Limits of predictability in human mobility.” Science 327 (5968): 1018–1021. https://doi.org/10.1126/science.1177170.
Stopher, P. R. 1992. “Use of an activity-based diary to collect household travel data.” Transportation 19 (2): 159–176. https://doi.org/10.1007/BF02132836.
Stopher, P. R., and A. Collins. 2005. “Conducting a GPS prompted recall survey over the Internet.” In Proc., 4th Annual Meeting on Transportation Research Board 2005. Washington, DC: Transportation Research Board.
Sun, L., D.-H. Lee, A. Erath, and X. Huang. 2012. “Using smart card data to extract passenger’s spatio-temporal density and train’s trajectory of MRT system.” In Proc., ACM SIGKDD Int. Workshop on Urban Computing, 142–148. New York: ACM.
Terroso-Sáenz, F., J. Cuenca-Jara, A. González-Vidal, and A. F. Skarmeta. 2016. “Human mobility prediction based on social media with complex event processing.” Int. J. Distrib. Sens. Networks 12 (9): 5836392. https://doi.org/10.1177/155014775836392.
Van Der Zijpp, N. 1997. “Dynamic origin-destination matrix estimation from traffic counts and automated vehicle identification data.” Transp. Res. Rec. 1607 (1): 87–94. https://doi.org/10.3141/1607-13.
White, J., and I. Wells. 2002. “Extracting origin destination information from mobile phone data.” In Proc., 11th Int. Conf. on Road Transport Information and Control, 30–34. London: Institution of Engineering and Technology.
Wu, L., Y. Zhi, Z. Sui, and Y. Liu. 2014. “Intra-urban human mobility and activity transition: Evidence from social media check-in data.” PLOS One 9 (5): e97010. https://doi.org/10.1371/journal.pone.0097010.
Xue, R., D. J. Sun, and S. Chen. 2015. “Short-term bus passenger demand prediction based on time series model and interactive multiple model approach.” Discrete Dyn. Nat. Soc. https://doi.org/10.1155/2015/682390.
Zhang, H. 2004. “The optimality of naive Bayes.” In FLAIRS Conf., 562–567. Miami Beach, FL: Association for the Advancement of Artificial Intelligence.
Zhang, Y., X. Qin, S. Dong, and B. Ran. 2010. “Daily OD matrix estimation using cellular probe data.” In Vol. 9 of Proc., 89th Annual Meeting Transportation Research Board. Washington, DC: Transportation Research Board.
Zhao, M., L. Mason, and W. Wang. 2008. “Empirical study on human mobility for mobile wireless networks.” In Proc., MILCOM 2008-2008 IEEE Military Communications Conf., 1–7. New York: IEEE.
Zhou, X., and H. S. Mahmassani. 2006. “Dynamic origin-destination demand estimation using automatic vehicle identification data.” IEEE Trans. Intell. Transp. Syst. 7 (1): 105–114. https://doi.org/10.1109/TITS.2006.869629.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Dec 13, 2017
Accepted: Sep 23, 2019
Published online: Feb 20, 2020
Published in print: May 1, 2020
Discussion open until: Jul 20, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.