Abstract
The problem of road congestion is becoming more and more serious in urban areas, which calls for solutions. This makes life in cities uncomfortable and costs a huge budget every year. Several resources are wasted during a bottling of fuel, weather, etc. In the ad hoc network of vehicles (VANET), useful information is exchanged between vehicles and traffic to avoid congestion and ensure easy fluidity. Vehicle-to-vehicle communication (V2V) is a means of transmitting this information in a VANET network. The immense amount of data that can be generated by a VANET network makes processing difficult for traditional tools to take advantage of its generated data. In this paper, we propose an approach based on big data tools to analyse the floating data in a VANET network and to detect the congested roads each based on occupancy rate of the roads, we detect the congested roads in the monitored area. Then, exctract more details about the congestion occurred by identifying the congestion interval and the peak instants in this interval. Simulations are done using the SUMO mobilisation generator and the NS-2 simulator.
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Cherkaoui, B., Beni-hssane, A., & Erritali, M. (2017). A clustering algorithm for detecting and handling black hole attack in vehicular ad hoc networks. In Á. Rocha, M. Serrhini, & C. Felgueiras (Eds.), Europe and MENA cooperation advances in information and communication technologies (Vol. 520)., Advances in intelligent systems and computing Cham: Springer.
Singh, A., & Kad, S. (2016). A review on the various security techniques for VANETs. Procedia Computer Science, 78, 284–290.
Boussoufa-Lahlah, S., Semchedine, F., & Bouallouche-Medjkoune, L. (2018). Geographic routing protocols for vehicular ad hoc NETworks (VANETs): A survey. Vehicular Communications, 11, 20–3.
Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: Preserving security and privacy. Journal of Big Data, 5, 1. https://doi.org/10.1186/s40537-017-0110-7.
Saraladevi, B., Pazhaniraja, N., Victer Paul, P., Saleem Basha, M. S., & Dhavachelvan, P. (2015). Big data and hadoop-a study in security perspective. Procedia Computer Science, 50, 596–601.
White, T. (2009). Hadoop: The definitive guide. Sebastopol: O’Reilly Media.
Ghazi, M. R., & Gangodkar, D. (2015). Hadoop, MapReduce and HDFS: A developers perspective. Procedia Computer Science, 48, 45–50.
Birjali, M., Beni-Hssane, A., & Erritali, M. (2018). Evaluation of high-level query languages based on MapReduce in Big Data. Journal of Big Data, 5, 36. https://doi.org/10.1186/s40537-018-0146-3.
Liroz-Gistau, M., Akbarinia, R., Agrawal, D., & Valduriez, P. (2016). FP-hadoop: Efficient processing of skewed MapReduce jobs. Information Systems, 60, 69–84.
Cárdenas-Benítez, N., Aquino-Santos, R., Magaña-Espinozan, P., Aguilar-Velazco, J., Edwards-Block, A., & Medina Cass, A. (2016). Traffic congestion detection system through connected vehicles and big data. Sensors, 16, 599.
Kimura, M., et al. (March 2013). A novel method based on VANET for alleviating traffic congestion in urban transportations. In Proceedings of the IEEE 7th international symposium on autonomous decentralized systems, Mexico City, Mexico (pp. 1–7).
Daniel, A., Paul, A., & Ahmad, A. (2015). Near real-time big data analysis on vehicular networks. In International conference on soft-computing and network security (ICSNS-2015), February 25–27, Coimbatore, India.
Al Najada, H., & Mahgoub, I. (2016). Autonomous vehicles safe-optimal trajectory selection based on big data analysis and predefined user preferences. In IEEE annual ubiquitous computing, electronics and mobile communication conference, UEMCON 2016.
Zhang, L., Gao, D., Zhao, W., & Chao, H. (2013). A multilevel information fusion approach for road congestion detection in VANETs. Mathematical and Computer Modelling, 58, 1206–1221.
Kumar, T., & Kushwaha, D. S. (2019). An approach for traffic congestion detection and traffic control system. In S. Fong, S. Akashe, & P. Mahalle (Eds.), Information and communication technology for competitive strategies (Vol. 40)., Lecture notes in networks and systems Singapore: Springer.
Yang, H., Wang, Z., Xie, K., & Dai, D. (2017). Use of ubiquitous probe vehicle data for identifying secondary crashes. Transportation Research Part C: Emerging Technologies, 82, 138–160.
Issariyakul, T., & Hossain, E. (2012). Introduction to network simulator 2 (NS2). In: Introduction to network simulator NS2. Springer, Boston, MA
Lopez, P. A., et al. (2018). Microscopic Traffic Simulation using SUMO, In 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, (pp. 2575–2582).
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Cherkaoui, B., Beni-Hssane, A., El Fissaoui, M. et al. Road State Novel Detection Approach in VANET Networks Based on Hadoop Ecosystem. Wireless Pers Commun 107, 1643–1660 (2019). https://doi.org/10.1007/s11277-019-06349-3
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DOI: https://doi.org/10.1007/s11277-019-06349-3