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Unattended Crowdsensing Method to Monitor the Quality Condition of Dirt Roads

Authors: Matías Micheletto, Rodrigo Santos, Sergio F. Ochoa


In developing countries, most roads in rural areas are dirt road. They require frequent maintenance since they are affected by erosive events, such as rain or wind, and the transit of heavy-weight trucks and machinery. Early detection of damages on the road condition is a key aspect, since it allows to reduce the maintenance time and cost, and also the limitations for other vehicles to travel through. Most proposals that help address this problem require the explicit participation of drivers, a permanent internet connection, or important instrumentation in vehicles or roads. These constraints limit the suitability of these proposals when applied into developing regions, like Latin America. This paper proposes an alternative method, based on unattended crowdsensing, to determine the quality of dirt roads in rural areas. This method involves the use of a mobile application that complements the road condition surveys carried out by organizations in charge of the road network maintenance, giving them early warnings about road areas that could be requiring maintenance. Drivers can also take advantage of the early warnings while they move through these roads. The method was evaluated using information from a public dataset. Although they are preliminary, the results indicate the proposal is potentially suitable to provide awareness about dirt roads condition to drivers, transportation authority and road maintenance companies.

Keywords: Dirt roads automatic quality assessment, collaborative system, unattended crowdsensing method, roads quality awareness provision.

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[1] Cardenas Robles, J.N.: Comparative study of fault identification methodologies in dirt roads, in Spanish. Civil engineering thesis, Facultad de Ingenier´ıa, Escuela Profesional de Ingenier´ıa Civil, Universidad Ricardo Palma, Lima, Peru (2012)
[2] Castagnino, J., Castagnino, L., Zanini, C.: Rural roads maintenance guide, in Spanish (2018). URL ar/guia de mantenimiento de caminos rurales.pdf
[3] El-Wakeel, A.S., Li, J., Noureldin, A., Hassanein, H.S., Zorba, N.: Towards a practical crowdsensing system for road surface conditions monitoring. IEEE Internet of Things Journal 5(6), 4672–4685 (2018)
[4] Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine 49(11), 32–39 (2011)
[5] Geaslin, D.: Geaslin’s inverse-square rule for deferred maintenance effort. The Geaslin Group. URL: rule.htm (2014)
[6] Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N.Y., Huang, R., Zhou, X.: Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48(1) (2015). DOI 10.1145/2794400. URL
[7] Jeng, Y., Huang, S., Lai, C.: Inspect road quality by using anomaly detection approach. In: 2018 International Conference on System Science and Engineering (ICSSE), pp. 1–4 (2018)
[8] Leguizam´on, G.I.: Logistics and accessibility in rural roads analysis of waterlogging in the southeast of buenos aires province, in Spanish. Civil engineering thesis, Unidad de Ense˜nanza Universitaria de Quequ´en, UNICEN, Argentina (2019)
[9] Li, X., Goldberg, D.W.: Toward a mobile crowdsensing system for road surface assessment. Computers, Environment and Urban Systems 69, 51 – 62 (2018). DOI 2017.12.005. URL S0198971517301333
[10] Menegazzo, J., von Wangenheim, A.: Multi-contextual and multi-aspect analysis for road surface type classification through inertial sensors and deep learning. In: 2020 X Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 1–8 (2020). DOI 10. 1109/SBESC51047.2020.9277846. URL jefmenegazzo/pvs-passive-vehicular-sensors-datasets
[11] Miranda, J., M¨akitalo, N., Garcia-Alonso, J., Berrocal, J., Mikkonen, T., Canal, C., Murillo, J.M.: From the internet of things to the internet of people. IEEE Internet Computing 19(2), 40–47 (2015)
[12] Monares, A., Ochoa, S., Herskovic, V., Santos, R., Pino, J.: Modeling interactions in human-centric wireless sensor networks. In: Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design, pp. 661–666 (2014). DOI 10.1109/ CSCWD.2014.6846923. Cited By 8
[13] Nugra, H., Abad, A., Fuertes, W., Galarraga, F., Aules, H., Villacis, C., Toulkeridis, T.: A low-cost iot application for the urban traffic of vehicles, based on wireless sensors using gsm technology. In: 2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 161–169 (2016)
[14] Ochoa, S.F., Santos, R.: Human-centric wireless sensor networks to improve information availability during urban search and rescue activities. Information Fusion 22, 71 – 84 (2015). DOI https://doi. org/10.1016/j.inffus.2013.05.009
[15] Paterson, W.: Deterioration and maintenance of unpaved roads: Models of roughness and material loss. Transportation Research Record 12, 143–156 (1991)
[16] Piao, B., Aihara, K.: Detecting the road surface condition by using mobile crowdsensing with drive recorder. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–8 (2017)
[17] Xiong, H., Zhang, D., Chen, G., Wang, L., Gauthier, V., Barnes, L.E.: icrowd: Near-optimal task allocation for piggyback crowdsensing. IEEE Transactions on Mobile Computing 15(8), 2010–2022 (2016)
[18] Yuan, Y., Che, X.: Research on road condition detection based on crowdsensing. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 804–811 (2019)