Traffic state prediction by machine learning algorithms for short-term and mid-term prediction time horizons

Predicting traffic variables and informing the passengers and the transportation network operators is one way to manage the travel demand. By informing the future state of traffic through intelligent transportation systems, there is more readiness to avoid congestion. In this study, three machine learning models, including support vector machine (SVM), random forest (RF), and long short-term memory (LSTM), were used to predict the hourly traffic state, consist of light, semi-heavy and heavy states, for Karaj to Chalus rural road in the north of Iran. Predictor variables of mid-term models are calendar information, weather, and road blockage policies. In contrast, in short-term models, in addition to mentioned variables, the observed traffic states in the past three to eight hours have been used, and these models can only predict the future of one and two hours of future. The results show that short-term LSTM is the most accurate traffic state predictor model with accuracy equal to 90.11%. Among the mid-term models, the LSTM model has predicted traffic state more accurately than SVM and Rf, and its accuracy is equal to 82.07%. Also, LSTM has the highest values of f1 to predict light, semi-heavy, and heavy, which are equivalent to 0.86, 0.93, and 0.81, respectively. Also, the hour, holiday, and type of holiday variables and traffic state observed in 3 to 8 hours later variables have the greatest effect on increasing the accuracy of mid-term and short-term models, respectively.