References
- 1Ahas, R, Silm, S, Järv, O, Saluveer, E and Tiru, M. 2010. Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones. Journal of Urban Technology, 17(1): 3–27. DOI: 10.1080/10630731003597306
- 2Alexander, L, Jiang, M, Murga, M and Gonzalez, M. 2015. Origin-destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies, 58: 240–250. DOI: 10.1016/j.trc.2015.02.018
- 3Bar-Gera, H. 2007. Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel. Transportation Research Part C: Emerging Technologies, 15(6): 380–391. DOI: 10.1016/j.trc.2007.06.003
- 4Batty, M. 1976. Urban modelling: algorithms, calibrations, predictions. London: Syndics of Cambridge University Press.
- 5Bharat, PB and Larsen, O. 2011. Are intrazonal trips ignorable? Transport Policy, 18(1): 13–22. DOI: 10.1016/j.tranpol.2010.04.004
- 6BLS – Bureau of labor statistics. 2017.
American time use survey – 2017 results . U.S. Department of Labour. USDL-18-1058. Available at:https://www.bls.gov/news.release/pdf/atus.pdf [Accessed 21.11.2018]. - 7Caceres, N, Wideberg, J and Benitez, F. 2008. Review of Traffic Data Estimations Extracted from Cellular Networks. IET Intelligent Transport Systems, 2(3): 179–192. DOI: 10.1049/iet-its:20080003
- 8Calabrese, F, Colonna, M, Lovisolo, P, Parata, D and Ratti, C. 2011a. Real-time urban monitoring using cellphones: A case study in Rome. IEEE Transactions on Intelligent Transportation Systems, 12(1): 141–151. DOI: 10.1109/TITS.2010.2074196
- 9Calabrese, F, Lorenzo, G, Liu, L and Ratti, C. 2011b. Estimating origin-destination flows using mobile phone location data. Pervasive Computing, IEEE, 10(4): 36–44. DOI: 10.1109/MPRV.2011.41
- 10Cascetta, E, Pagliara, F and Papola, A. 2007. Alternative approaches to trip distribution modelling: a retrospective review and suggestions for combining different approaches. Regional Science, 86(4): 597–620. DOI: 10.1111/j.1435-5957.2007.00135.x
- 11Çolak, L, Alexander, B, Alvim, S, Mehndiretta, M and Gonzalez, M. 2015.
Analyzing Cell Phone Location Data for Urban Travel: Current Methods, Limitations and Opportunities . Transportation Research Board Annual meeting. Washington, D.C. - 12Csáji, B, Browet, A, Traag, V, Delvenne, J, Huens, E, Dooren, P, Smoreda, Z and Blondel, V. 2013. Exploring the mobility of mobile phone users. Physica A Statistical Mechanics and its Applications, 392(6): 1459–1473. DOI: 10.1016/j.physa.2012.11.040
- 13Demissie, MD. 2014.
Combining datasets from multiple sources for urban and transportation planning: Emphasis on cellular network data , Ph.D. dissertation, Dept. Civil Eng., Coimbra Univ., Coimbra, Portugal. - 14Demissie, MG, Correia, G and Bento, C. 2013a. Intelligent road traffic status detection system through cellular networks handover information: An exploratory study. Transp. Res. C, Emerging Technol, 32: 76–78. DOI: 10.1016/j.trc.2013.03.010
- 15Demissie, MG, Correia, GH and Bento, C. 2013b. Traffic volume estimation through cellular networks handover information. 13th World Conference on Transportation Research, Rio de Janeiro, Brazil.
- 16Demissie, MG, Correia, GH and Bento, C. 2013c. Exploring cellular network handover information for urban mobility analysis. Journal of Transport Geography, 31: 164–170. DOI: 10.1016/j.jtrangeo.2013.06.016
- 17Demissie, MG, Correia, GH and Bento, C. 2015. Analysis of the pattern and intensity of urban activities through aggregate cellphone usage. Transportmetrica A: Transport Science, 11(6): 502–524. DOI: 10.1080/23249935.2015.1019591
- 18Demissie, MG, Phithakkitnukoon, S and Kattan, L. 2018. Trip Distribution Modeling Using Mobile Phone Data: Emphasis on Intra-Zonal Trips. IEEE Trans. Intell. Transp. Syst. Oct. 2018. DOI: 10.1109/TITS.2018.2868468
- 19Demissie, MG, Phithakkitnukoon, S, Sukhvibul, T, Antunes, F and Bento, C. 2016a. Inferring origin-destination flows using mobile phone data: A case study of Senegal. International conference on electrical engineering/electronics, computer, telecommunications and information technology. Chiang Mai, Thailand. DOI: 10.1109/ECTICon.2016.7561328
- 20Demissie, MG, Phithakkitnukoon, S, Sukhvibul, T, Antunes, F, Gomes, R and Bento, C. 2016b. Inferring passenger travel demand to improve urban mobility in developing countries Using Cell Phone Data: A Case Study of Senegal. IEEE Transactions on Intelligent Transportation Systems, 17(9): 2466–2478. DOI: 10.1109/TITS.2016.2521830
- 21Dennett. 2012.
Estimating flows between geographical locations: “Get me started in” spatial interaction modelling . Centre for Advanced Spatial Analysis, University College, London. - 22FHWA. 2010.
Travel model validation and reasonableness checking manual second edition . Cambridge Systematics, Inc. - 23Flowerdew, R and Lovett, A. 1988. Fitting constrained poisson regression models to interurban migration flows. Geographical Analysis, 20(4). DOI: 10.1111/j.1538-4632.1988.tb00184.x
- 24Geohive. 2014.
http://www.geohive.com/cntry/senegal%20ext.aspx . Accessed Jun. 01, 2017. - 25González, M, Hidalgo, C and Barabási, A. 2008. Understanding individual human mobility patterns. Nature, 453: 779–782. DOI: 10.1038/nature06958
- 26Gundlegård, D, Rydergren, C, Breyer, N and Rajna, B. 2016. Travel demand estimation and network assignment based on cellular network data. Computer Communications, 95: 29–42. DOI: 10.1016/j.comcom.2016.04.015
- 27Hariharan, R and Toyama, K. 2004. Project lachesis: parsing and modeling location histories. Geogr. Inform. Sci, 106–124.
- 28Hoteit, S, Secci, S, Sobolevsky, C, Ratti, C and Pujolle, G. 2014. Estimating human trajectories and hotspots through mobile phone data. Computer Networks, 64: 296–307. DOI: 10.1016/j.comnet.2014.02.011
- 29Kordi, M, Kaiser, C and Fotheringham, A. 2012. A possible solution for the centroid-to-centroid and intra-zonal trip length problems. AGILE’2012 International Conference on Geographic Information Science. Avignon.
- 30Liu, HX, Danczyk, A, Brewer, R and Starr, R. 2008. Evaluation of cellphone traffic data in Minnesota. Transportation Research Record: Journal of the Transportation Research Board, 2086: 1–7. DOI: 10.3141/2086-01.
- 31Ortuzar, JD and Willumsen, LG. 2011.
Modelling transport . Wiley. DOI: 10.1002/9781119993308 - 32Phithakkitnukoon, S, Smoreda, Z and Olivier, P. 2012. Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data. PLoS ONE, 7(6). DOI: 10.1371/journal.pone.0039253
- 33Phithakkitnukoon, S, Sukhvibul, T, Demissie, M, Smoreda, Z, Natwichai, J and Bento, C. 2017. Inferring social influence in transport mode choice using mobile phone data. EPJ Data Science, 6(11). DOI: 10.1140/epjds/s13688-017-0108-6
- 34Qin, S, Verkasalo, H, Mohtaschemi, M, Hartonen, T and Alava, M. 2012. Patterns, Entropy, and Predictability of Human Mobility and Life. PloS one, 7(12). DOI: 10.1371/journal.pone.0051353
- 35Ratti, C, Sevtsuk, A, Huang, S and Pailer, R. 2005. Mobile landscapes: Graz in real time. Symposium on LBS & TeleCartography.
November 28–30, 2005 . - 36R Core Team. 2013.
R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria. - 37Schneider, C, Belik, V, Couronné, T, Smoreda, Z and González, M. 2013. Unraveling daily human mobility motifs. J. Roy. Soc. Interface, 10.
- 38Song, C, Qu, Z, Blumm, N and Barabási, A. 2010. Limits of Predictability in Human Mobility. Science, 327(5968): 1018–1021. DOI: 10.1126/science.1177170
- 39Toole, J, Colak, S, Sturt, B, Alexander, L, Evsukoff, A and Gonzalez, M. 2015. The path most traveled: Travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies, 58: 161–428. DOI: 10.1016/j.trc.2015.04.022
- 40Toole, J, Ulm, M, González, M and Bauer, D. 2012. Inferring land use from mobile phone activity. ACM SIGKDD international workshop on urban computing. Beijing, China. DOI: 10.1145/2346496.2346498
- 41Wang, Y, Correia, G, Romph, E and Santos, BF. 2017. Road network design in a developing country using mobile phone data: An application to Senegal. IEEE Intelligent Transportation Systems Magazine. DOI: 10.1109/MITS.2018.2879168
- 42White, J and Wells, I. 2002. Extracting origin destination information from mobile phone data. International Conference on Road Transportation and Control, 30–34. London. DOI: 10.1049/cp:20020200
- 43Yan, X, Zhao, C, Fan, Y, Di, Z and Wang, W. 2014. Universal predictability of mobility pattern in cities. J R Soc Interface, 11(100). DOI: 10.1098/rsif.2014.0834
- 44Yang, Y, Herrera, C, Eagle, N and Gonzalez, M. 2014. Limits of Predictability in Commuting Flows in the Absence of Data for Calibration. Scientific reports, 4(5662).
- 45Zheng, Y, Zhang, L, Xie, X and Ma, W. 2009. Mining interesting locations and travel sequences from GPS trajectories. Proceedings of the 18th international conference on World wide web.
ACM . DOI: 10.1145/1526709.1526816
