i-manager Publications

Fundamental Understanding of Mobile Phone Data for Transport Applications

Louis Mauricio*

Department of Computer Science, University of Zulia, Zulia, Venezuela.

Periodicity:July - December'2024
DOI : https://doi.org/10.26634/jmt.11.2.21646

Abstract

Mobile phone data has become a valuable resource in mobility studies, providing a rich dataset for analyzing human movement. This study presents a comprehensive review of mobile phone data sources and their applications in transportation studies. It explores the strengths and limitations of data types such as call detail records (CDR), cellular signaling data (CSD), and synthetic CDR. The study discusses methodologies for estimating travel demand, generating origin-destination (OD) matrices, and conducting mode-specific OD estimation. While CDR data has proven reliable for trip purpose estimation, challenges remain in accurately predicting travel modes. The study concludes with recommendations for enhancing synthetic CDR data and expanding its use in mobility studies.

Keywords

Mobile Phone Data, Call Detail Records (CDR), Transport Applications, Origin-Destination Matrix, Travel Demand, Synthetic CDR.

How to Cite this Article?

Mauricio, L. (2024). Fundamental Understanding of Mobile Phone Data for Transport Applications. i-manager’s Journal on Mobile Applications & Technologies, 11(2), 1-12. https://doi.org/10.26634/jmt.11.2.21646

References

[1]. Adam, A., Delvenne, J. C., & Thomas, I. (2017). Cartography of interaction fields in and around Brussels: Commuting, moves and telephone calls. Brussels Studies.

[2]. Akin, D., & Sisiopiku, V. (2002). Estimating origin- destination matrices using location information from cellular phones. In Presented at the 49th Annual North American Meetings of the Regional Science Association International, 2002, Puerto Rico (pp. 1-7).

[3]. Alexander, L., Jiang, S., Murga, M., & González, M. C. (2015). Origin–destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies, 58, 240-250.

[4]. Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., & Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734, 1-74.

[5]. Behara, K. N., Bhaskar, A., & Chung, E. (2020). A novel approach for the structural comparison of origin- destination matrices: Levenshtein distance. Transportation Research Part C: Emerging Technologies, 111, 513-530.

[6]. Blondel, V. D., Decuyper, A., & Krings, G. (2015). A survey of results on mobile phone datasets analysis. EPJ Data Science, 4, 1-55.

[7]. Caceres, N., Wideberg, J. P., & Benitez, F. G. (2008). Review of traffic data estimations extracted from cellular networks. IET Intelligent Transport Systems, 2(3), 179-192.

[8]. Calabrese, F., Di Lorenzo, G., Liu, L., & Ratti, C. (2011). Estimating Origin-Destination Flows Using Opportunistically Collected Mobile Phone Location Data from One Million Users in Boston Metropolitan Area. MIT Libraries.

[9]. Cerruto, F., Cirillo, S., Desiato, D., Gambardella, S. M., & Polese, G. (2022). Social network data analysis to highlight privacy threats in sharing data. Journal of Big Data, 9(1), 19.

[10]. Çolak, S., Lima, A., & González, M. C. (2016). Understanding congested travel in urban areas. Nature Communications, 7(1), 10793.

[11]. Huang, H., Cheng, Y., & Weibel, R. (2019). Transport mode detection based on mobile phone network data: A systematic review. Transportation Research Part C: Emerging Technologies, 101, 297-312.

[12]. Iqbal, M. S., Choudhury, C. F., Wang, P., & González, M. C. (2014). Development of origin–destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 40, 63-74.

[13]. Jiang, S., Fiore, G. A., Yang, Y., Ferreira Jr, J., Frazzoli, E., & González, M. C. (2013, August). A review of urban computing for mobile phone traces: Current methods, challenges and opportunities. In Proceedings of the 2nd ACM SIGKDD international workshop on Urban Computing (pp. 1-9).

[14]. Kao, C. H., Hsiao, R. S., Chen, T. X., Chen, P. S., & Pan, M. J. (2017, June). A hybrid indoor positioning for asset tracking using Bluetooth low energy and Wi-Fi. In 2017 IEEE international conference on consumer electronics- Taiwan (ICCE-TW) (pp. 63-64). IEEE.

[15]. Luca, M., Barlacchi, G., Lepri, B., & Pappalardo, L. (2020). Deep learning for human mobility: A survey on data and models. arXiv preprint arXiv:2012.02825.

[16]. Pei, T., Sobolevsky, S., Ratti, C., Shaw, S. L., Li, T., & Zhou, C. (2014). A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28(9), 1988-2007.

[17]. Ratti, C., Frenchman, D., Pulselli, R. M., & Williams, S. (2006). Mobile landscapes: Using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33(5), 727-748.

[18]. Schlaich, J. (2010). Analyzing route choice behavior with mobile phone trajectories. Transportation Research Record, 2157(1), 78-85.

[19]. Simini, F., González, M. C., Maritan, A., & Barabási, A. L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 96-100.

[20]. Wang, X., Zheng, X., Chen, W., & Wang, F. Y. (2020). Visual human–computer interactions for intelligent vehicles and intelligent transportation systems: The state of the art and future directions. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 253-265.