Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities

Author(s)

Rocha Filho, Geraldo P. ; Meneguette, Rodolfo, I ; Torres Neto, Jose R. ; Valejo, Alan ; Li Weigang ; Ueyama, Jo ; Pessin, Gustavo ; Villas, Leandro A.

• Document type: Journal article
• Source: Ad Hoc Networks; v. 107, p. 10-pg., 2020-10-01.

Abstract

One of the main challenges in urban development faced by large cities is related to traffic jam. Despite increasing effort s to maximize the vehicle flow in large cities, to provide greater accuracy to estimate the traffic jam and to maximize the flow of vehicles in the transport infrastructure, without increasing the overhead of information on the control-related network, still consist in issues to be investigated. Therefore, using artificial intelligence method, we propose a solution of inter-vehicle communication for estimating the congestion level to maximize the vehicle traffic flow in the transport system, called TRAFFIC. For this, we modeled an ensemble of classifiers to estimate the congestion level using TRAFFIC. Hence, the ensemble classification is used as an input to the proposed dissemination mechanism, through which information is propagated between the vehicles. By comparing TRAFFIC with other studies in the literature, our solution has advanced the state of the art with new contributions as follows: (i) increase in the success rate for estimating the traffic congestion level; (ii) reduction in travel time, fuel consumption and CO2 emission of the vehicle; and (iii) high coverage rate with higher propagation of the message, maintaining a low packet transmission rate. (c) 2020 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 18/17335-9 - Exploring DLTs and Computational Intelligence in IoT
• Grantee: Jó Ueyama
• Support Opportunities: Regular Research Grants