-Abedi, N., Bhaskar, A. and Chung, E. (2014) “Tracking spatio-temporal movement of human in terms of space utilization using Media-Access-Control address data”, Applied Geography, Vol. 51, pp.72-81.
-Araghi, B. N., Pedersen, K. S., Christensen, L.T., Krishnan, R. and Lahrmann, H. (2015) “Accuracy of travel time estimation using Bluetooth technology: Case study Limfjord tunnel Aalborg”, International Journal of Intelligent Transportation Systems Research, Vol. 13, No. 3, pp.166-191
- Bellini, Pierfrancesco, Cenni, Daniele, Nesi, Paolo and Paoli, Irene (2017) “Wi-Fi based city users’ behaviour analysis for smart city”, Journal of Visual Languages and Computing, Vol. 42, pp.31-45
-Bhaskar, A., Tsubota, T. and Chung, E., (2014) “Urban traffic state estimation: Fusing point and zone based data”, Transportation Research Part C: Emerging Technologies, Vol. 48, pp.120-142.
-Cisco Company (2018) “White paper: Cisco visual networking index: Global mobile data traffic forecast update, 2015-2020”, February 2016.[Online].Available:
https://www.cisco.com/c/en/us/solutions /collateral/service-provider/ visual-networking-index-vni/mobile-white-paper-c11-520862.html
-Danalet, A., Farooq, B. and Bierlaire, M. (2014) “A Bayesian approach to detect pedestrian destination-sequences from Wi-Fi signatures”, Transportation Research Part C: Emerging Technologies, No. 44, pp.146-170.
-Du, Y., Yue, J., Ji, Y. and Sun, L. (2017) “Exploration of optimal Wi-Fi probes layout and estimation model of real-time pedestrian volume detection”, International Journal of Distributed Sensor Networks, Vol. 13, No. 11, pp. 1-10
-Engelbrecht, J., Booysen, M. J., van Rooyen, G. J., and Bruwer, F. J. (2015) “Survey of smartphone-based sensing in vehicles for intelligent transportation system applications”, IET Intelligent Transport Systems, Vol. 9, Issue 10, pp.924-935.
-Fukuzaki, Y., Mochizuki, M., Murao, K., and Nishio, N. (2014, September) “A pedestrian flow analysis system using Wi-Fi packet sensors to a real environment”, In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication pp. 721-730 ACM.
-Handte, M., Iqbal, M. U., Wagner, S., Apolinarski, W., Marrón, P. J., Navarro, E. M. M. and Fernández, M. G. (2014, March) “Crowd Density Estimation for Public Transport Vehicles”, EDBT/ICDT Workshops , pp. 315-322.
-Iqbal, M.S., Choudhury, C. F., Wang, P. and González, M. C. (2014) “Development of origin–destination matrices using mobile phone call data”, Transportation Research Part C: Emerging Technologies, Vol. 40, pp.63-74.
-Kurkcu, A. and Ozbay, K. (2017) “Estimating pedestrian densities, wait times, and flows with Wi-Fi and Bluetooth sensors”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2644, pp. 72-82.
-Mikkelsen, L., Buchakchiev, R., Madsen, T. and Schwefel, H. P. (2016) “Public transport occupancy estimation using WLAN probing”, In Resilient Networks Design and Modeling (RNDM), 2016 8th International Workshop on (pp. 302-308). IEEE
-Min Y. Mun, Deborah Estrin, Jeff Burke, Mark Hansen (2007) “Parsimonious mobility classification using GSM and WiFi traces”, In Proceedings of the Fifth Workshop on Embedded Networked Sensors (HotEmNets). Sydney: 4 November,2007
-Moertini, V. (2002) “Introduction to five data clustering algorithm”, Integral, Vol. 7, No. 2, pp.87-96.
-Musa, A. B. M. and Eriksson, J. (2012). “Tracking unmodified smartphones using wi-fi monitors”, In Proceedings of the 10th ACM conference on embedded network sensor systems, pp. 281-294, (ACM)
-Wind, D. K., Sapiezynski, P., Furman, M. A. and Lehmann, S. (2016) “Inferring stop-locations from Wi-Fi”, PloS one, Vol. 11, No. 2, p.e0149105
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