Abstract
An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike’s Information Criterion technique (AIC) has also shown that the proposed model has a higher performance.
Highlights
The growing size of cities and increasing population mobility have caused a rapid increase in the number of vehicles on the roads
This is due to the fact that the drivers tend to drive faster, before or after encountering congestions, in order to compensate for the experienced delay. Researchers from both industry and academia focused on making traffic management systems (TMS) more efficient to cope with the above issues
The model built may be evaluated by different criteria; it gives us a clear view of its performance and how well it correctly predicts the results
Summary
The growing size of cities and increasing population mobility have caused a rapid increase in the number of vehicles on the roads. Road traffic management faces such challenges as traffic congestion, accidents, and air pollution. Recent statistics reveal that the majority of vehicle crashes usually happen in the areas around congested roads. This is due to the fact that the drivers tend to drive faster, before or after encountering congestions, in order to compensate for the experienced delay. Traffic data are growing fast, and their analysis is a key component in developing a road network strategy. The most important question is how to analyze and benefit from this gold mine of information in order to bring out predictions of future data. An accurate traffic prediction system is one of the critical steps in the operations of an Intelligent Transportation System (ITS) and is extremely important for practitioners to perform route planning and traffic regulations
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