Open AccessMachine Learning and statistic predictive modeling for road traffic flow
Author(s) -
Nadia Slimani,
Ilham Slimani,
Nawal Sbiti,
Mustapha Amghar
Publication year - 2021
Publication title -
international journal of traffic and transportation management
Language(s) - English
Resource type - Journals
ISSN - 2371-5782
DOI - 10.5383/jttm.03.01.003
Subject(s) - context (archaeology) , computer science , autoregressive integrated moving average , traffic flow (computer networking) , artificial neural network , traffic congestion , statistic , mean absolute percentage error , autoregressive model , multilayer perceptron , intelligent transportation system , machine learning , artificial intelligence , transport engineering , time series , engineering , econometrics , statistics , geography , computer security , mathematics , archaeology , economics
Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart cities. This work is in line with this perspective and aims to solve this problem. The proposed methodology plans to forecast day-by-day traffic stream using three different models: the Multilayer Perceptron of Artificial Neural Networks (ANN), the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Support Machine Regression (SMOreg). Using those three models, the forecast is realized based on a history of real traffic data recorded on a road section over 42 months. Besides, a recognized traffic manager in Morocco provides this dataset; the performance is then tested based on predefined criteria. From the experiment results, it is clear that the proposed ANN model achieves highest prediction accuracy with the lowest absolute relative error of 0.57%.