Abstract
The widespread adoption of programmable mobile devices that have great sensing, collecting and analysing abilities, opened up multiple new paradigms such as crowdsensing. The addiction of people to their smartphones made it possible to these later to be a part of their daily life and activities, which lead to the creation of applications that require the combination of human participation and the use of the powerful new technologies embedded inside the mobile devices. These information systems help mainly in the gathering of historical and real-time data and in the analysing process. In this work, we present a framework dedicated to authorities that aims to motivate citizens to join a crowd sensing attempt to minimize and control the human and material damage that occurs when having deteriorated roads and non-responsible drivers.
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References
Campbell, A.T., Eisenman, S.B., Lane, N.D., et al.: The rise of people-centric sensing. IEEE Internet Comput. 12(4), 12–21 (2008)
Kamel Boulos, M.N., Resch, B., Crowley, D.N., et al.: Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int. J. Health Geogr. 10, 1–29 (2011)
Boulif, M.N.: Maroc: moins de morts sur les routes en 2017 (2018)
Tribune: Maroc: 80% des accidents seraient dus au facteur humain (2017)
Bajwa, R., Rajagopal, R., Varaiya, P., Kavaler, R., Street, N.: In-pavement wireless sensor network for vehicle classification. In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing Sensor Networks, Aug 2016, pp. 85–96 (2011)
Annan, A.P.: Ground penetrating radar: principles electromagnetic principles of ground penetrating radar
Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., Zeinalipour-yazti, D.: Crowdsourcing with smartphones, pp. 1–7 (2012)
Zhang, D., Wang, L., Xiong, H., Guo, B.: 4W1H in mobile crowd sensing. IEEE Commun. Mag. 52(8), 42–48 (2014)
STREET BUMP [Internet] (2017). Available from: http://www.streetbump.org/about
Brisimi, T.S., Cassandras, C.G., Osgood, C., Paschalidis, I.C., Zhang, Y.: Sensing and classifying roadway obstacles in smart cities: the street bump system. IEEE Access 4(c), 1301–1312 (2016)
Kalim, F., Jeong, J., Ilyas, M.U.: CRATER: a crowd sensing application to estimate road conditions. IEEE Access 4, 8317–8326 (2016)
Chen, K., Tan, G., Lu, M., Wu, J.: CRSM: a practical crowdsourcing-based road surface monitoring system. Wirel. Netw. 22(3), 765–779 (2016)
Xue, G., Zhu, H., Hu, Z., Yu, J., Zhu, Y., Luo, Y.: Pothole in the dark: perceiving pothole profiles with participatory urban vehicles. IEEE Trans. Mob. Comput. 16(5), 1408–1419 (2017)
Li, Z., Kolmanovsky, I.V., Kalabic, U.V., Atkins, E.M., Lu, J.: Filev DiP. Optimal state estimation for systems driven by jump-diffusion process with application to road anomaly detection. IEEE Trans. Control Syst. Technol. 25(5), 1634–1643 (2017)
Fox, A., Kumar, B.V.K.V., Chen, J., Bai, F.: Multi-lane pothole detection from crowdsourced undersampled vehicle sensor data. IEEE Trans. Mob. Comput. 16(12), 3417–3430 (2017)
Dang, V.C., Kubo, M., Sato, H., Yamaguchi, A., Namatame, A.: A simple braking model for detecting incidents locations by smartphones. In: Proceedings of the 2014 7th IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2014 (2015)
Dai, J., Teng, J., Bai, X.: Mobile phone based drunk driving detection. In: 2010 4th International … [Internet], pp. 1–8 (2010). Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5482295
Eren, H., Makinist, S., Akin, E., Yilmaz, A.: Estimating driving behavior by a smartphone. In: IEEE Intelligent Vehicles Symposium, Proceedings, pp. 234–239 (2012)
Johnson, D.A., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp. 1609–1615 (2011)
Bhoraskar, R., Vankadhara, N., Raman, B., Kulkarni, P.: Wolverine: traffic and road condition estimation using smartphone sensors. In: 2012 4th International Conference on Communication Systems and Networks, COMSNETS 2012 (2012)
Saiprasert, C., Pholprasit, T., Pattara-Atikom, W.: Detecting driving events using smartphone. In: 20th ITS World Congress [Internet], 1–12 Oct 2013. Available from: http://trid.trb.org.ezproxy.library.wisc.edu/view/2013/C/1323676%5Cn, https://drive.google.com/open?id=0B1-iNPy2dfV0dElXUWdUUEJZdzg&authuser=0
Fazeen, M., Gozick, B., Dantu, R., Bhukhiya, M., González, M.C.: Safe driving using mobile phones. IEEE Trans. Intell. Trans. Syst. Internet. 13(3), 1462–1468 (2012). Available from: http://ieeexplore.ieee.org/document/6171850/
White, J., Thompson, C., Turner, H., Dougherty, B., Schmidt, D.C.: WreckWatch: automatic traffic accident detection and notification with smartphones. Mob. Netw. Appl. 16(3), 285–303 (2011)
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Boucetta, Z., El Fazziki, A., El adnani, M. (2019). Crowdsensing Based Citizen’s Safety Service. In: Ben Ahmed, M., Boudhir, A., Younes, A. (eds) Innovations in Smart Cities Applications Edition 2. SCA 2018. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-11196-0_82
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DOI: https://doi.org/10.1007/978-3-030-11196-0_82
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