The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Development of origin–destination matrices using mobile phone call data
Open accessAbstract
Abstract In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.
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González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,059
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Groves, R. M. (2006) · Public Opinion Quarterly · doi:10.1093/poq/nfl033 · Cited by 2,420
The International Telecommunication Union
Jones, R. W. (2002) · IEEE Antennas and Propagation Society International Symposium 1997. Digest · doi:10.1109/aps.1997.631791 · Cited by 1,909
A universal model for mobility and migration patterns
Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012) · Nature · doi:10.1038/nature10856 · Cited by 1,502
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Box, M. J. (1965) · The Computer Journal · doi:10.1093/comjnl/8.1.42 · Cited by 1,302
Modelling the scaling properties of human mobility
Song, C., Koren, T., Wang, P., & Barabási, A.-L. (2010) · Nature Physics · doi:10.1038/nphys1760 · Cited by 1,284
Self-citationHerrera, J. C., Work, D. B., Herring, R., Ban, X. (., Jacobson, Q., & Bayen, A. M. (2010) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2009.10.006 · Cited by 915
Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data
Vardi, Y. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476697 · Cited by 794
A Microscopic Traffic Simulator for evaluation of dynamic traffic management systems
Yang, Q., & Koutsopoulos, H. N. (1996) · Transportation Research Part C: Emerging Technologies · doi:10.1016/s0968-090x(96)00006-x · Cited by 746
Understanding individual mobility patterns from urban sensing data: A mobile phone trace example
Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira, J., & Ratti, C. (2013) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2012.09.009 · Cited by 643
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Toole, J. L., Colak, S., Sturt, B., Alexander, L. P., Evsukoff, A., & González, M. C. (2015) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2015.04.022 · Cited by 417
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Wang, Z., He, S. Y., & Leung, Y. (2018) · Travel Behaviour and Society · doi:10.1016/j.tbs.2017.02.005 · Cited by 367
Demissie, M. G., Phithakkitnukoon, S., Sukhvibul, T., Antunes, F., Gomes, R., & Bento, C. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2521830 · Cited by 90
Iliopoulou, C., & Kepaptsoglou, K. (2019) · European Transport Research Review · doi:10.1186/s12544-019-0365-5 · Cited by 59
Metadata
- Authors
- Iqbal, M. S., Choudhury, C. F., Wang, P., & González, M. C.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 40
- Pages
- 63-74
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2020281294
- Cited by
- 507
- References
- 29
- FWCI
- 74.30
- Type
- journal article
- Language
- en
- Open access
- green
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