Abstract
The rising number of vehicles inside the cities and keeping the same road map makes managing the traffic flow very difficult, especially at peak time. The problem becomes worse when drivers use the same paths and shortcuts at the same time. Which generally results in congestion, that is an urban mobility problem that causes economic, social and environmental losses. The use of classical congestion forecasting methods generally is not efficient due to the non-linear behavior of the traffic flow. Also, the existing navigation tools offer the shortest route without taking into account the overall congestion situation, demography, development of the city, which are generally variable and dynamic. To solve this complex problem, we propose in this paper an innovative solution that consists on using instances of road graphs by time slot to calculate dynamically the shortest route according to the most optimum road costs and nodes weight. Our idea shared into two phases, (1) developing an online mobile application for real time data collection of gps position and time for a group of vehicles (15 Taxis) during 30 days, and (2) performing an efficient algorithm for parsing and analyzing all collected data intelligently, calculating parameters and navigation conditions analysis of a set of road graphs with road costs and nodes weight that will be chosen automatically by our navigation tools.
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Benmessaoud, Y., Cherrat, L., Bennouna, M., Ezziyani, M. (2022). Intelligent Live Traffic Models Decision Support for Driving Directions and Road Conditions Updates. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1417. Springer, Cham. https://doi.org/10.1007/978-3-030-90633-7_10
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