Abstract
Having the big availability of transportation data, an emergent need to exploit these data in order to address different transportation issues become even more required. This paper introduces a Big Data solution to the road congestion problem that aim to reduce and optimize the traffic flow in urban areas. The proposed system uses real time traffic data to compute the congestion index for each road in the network and then generates recommendations to reassign the traffic flow. The computed congestion indexes are used in the system’s traffic network generation, where the cartography is represented by a weighted graph. The weights are changed dynamically according to the congestion indexes and path properties. The detailed approach adopts Hadoop framework in the data gathering and analysis, which has improved the performance of the proposed system significantly and uses the Dijkstra algorithm over Hadoop MapReduce framework to search for the shortest path in the road network.
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Berrouk, S., El Fazziki, A., Boucetta, Z. (2019). Traffic Regulation and Recommendation System Based on Measuring the Road Congestion. 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_86
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DOI: https://doi.org/10.1007/978-3-030-11196-0_86
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