Abstract
In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.
Highlights
The evolution of private vehicle ownership over the years has put tremendous pressure on road transportation systems worldwide, which has led to an epidemic called the traffic congestion
The study focused on application of the LevenbergMarquardt neural network model to model the flow of vehicular traffic in the Italian transport system, with particular reference to the city of Rome, using some of the parameters obtained from the survey instruments
One of the most significant findings from this research is that the Levenberg-Marquardt neural network model (LM-ANNM) has emerged as a reliable predictive model of the vehicular traffic flow of the Italian transport system
Summary
The evolution of private vehicle ownership over the years has put tremendous pressure on road transportation systems worldwide, which has led to an epidemic called the traffic congestion. Intelligent transportation systems were introduced to find solutions to this issue, such as traffic flow information management of vehicles and pedestrians The objective of these solutions was so that the future traffic flow conditions can be adequately predicted and appropriate measures would be put in place to tackle traffic flow problems on freeways and road intersections [1]. Prediction of traffic volume is the fundamental and primary component of intelligent transportation systems; this is essential for urban planners and transportation researchers to better manage traffic control of vehicles in road transportation systems. This will enable the efficient operation of road transportation systems [2]. Statistical models are well-known by researchers for their effective mathematical theories and innovative insights [7]
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