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Deep Learning System for Vehicular Re-Routing and Congestion Avoidance

1
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Epigmenio Gonzalez 500, Querétaro 76130, Mexico
2
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. General Ramon Corona 2514, Jalisco 45138, Mexico
3
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Lago de Guadalupe KM 3.5, Estado de México 52926, Mexico
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2717; https://doi.org/10.3390/app9132717
Received: 29 May 2019 / Revised: 27 June 2019 / Accepted: 3 July 2019 / Published: 5 July 2019
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Abstract

Delays in transportation due to congestion generated by public and private transportation are common in many urban areas of the world. To make transportation systems more efficient, intelligent transportation systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. However, most existing approaches only react once the traffic jam has occurred and, therefore, the delay has already spread to more areas of the traffic network. We propose a vehicle redirection system to avoid congestion that uses a model based on deep learning to predict the future state of the traffic network. The model uses the information obtained from the previous step to determine the zones with possible congestion, and redirects the vehicles that are about to cross them. Alternative routes are generated using the entropy-balanced k Shortest Path algorithm (EBkSP). The proposal uses information obtained in real time by a set of probe cars to detect non-recurrent congestion. The results obtained from simulations in various scenarios have shown that the proposal is capable of reducing the average travel time (ATT) by up to 19%, benefiting a maximum of 38% of the vehicles. View Full-Text
Keywords: traffic congestion detection; minimizing traffic congestion; traffic prediction; deep learning; urban mobility; ITS; Vehicle-to-Infrastructure traffic congestion detection; minimizing traffic congestion; traffic prediction; deep learning; urban mobility; ITS; Vehicle-to-Infrastructure
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Perez-Murueta, P.; Gómez-Espinosa, A.; Cardenas, C.; Gonzalez-Mendoza, M., Jr. Deep Learning System for Vehicular Re-Routing and Congestion Avoidance. Appl. Sci. 2019, 9, 2717.

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