Evaluation of parametric and non-parametric models in predicting rare traffic events based on average speed and traffic volume

One of the information needed for all planning problems and specifically transportation planning is to have accurate prediction about the future. Traffic variables prediction is one of the efficient tools in travel demand management. Using this tool and advanced traveler information systems (ATIS), the predicted traffic variables are informed to the users and transportation system operators to make plans and set policies. In this study, the average speed and traffic volume of the Karaj to Chalus suburban road with the high variation of traffic variables in the north of Iran is predicted. The Karaj to Chalous road is part of the route from Tehran as the capital of Iran to the country's northern coast. Along the Karaj to Chalous road, three parallel roads, with different lengths, connect Tehran with the cities of the north. In general, finding the pattern of non-mandatory trips is more complicated than mandatory trips. Generally, the predictive methods are divided into three groups, naïve, parametric and non-parametric methods. Among the various predictive models, the SARIMA as a parametric model and the artificial neural network and the support vector machine as nonparametric models are employed. In the data pre-processing step, the variables affecting the average speed and traffic volume are extracted and added to the dataset as predictor variables. These variables are related to time, calendar, holidays, weather, and roads blockage. Also, because of the importance of the maximum and minimum values of traffic speed and volume, as critical values and rare events, models are evaluated with emphasis on the prediction of rare events compared to normal values. The results show that, for the test data, the lowest root mean square error of predicting the average traffic speed and traffic volume are obtained using artificial neural network and support vector machine models equals 139 vehicles per hour and 5 kilometers per hour, respectively. In terms of R2 of prediction-observation plot, the performance of SARIMA for predicting the average speed and traffic volume is the same for the test dataset. In contrast the R2 of hourly traffic volume prediction is higher for the training data. The R2 of artificial neural network model and the support vector machine for traffic volume prediction is higher than traffic speed prediction. The lowest root mean square error of predicting the first and fourth quartile of the observed average traffic speed values is obtained by support vector machine models and artificial neural network, respectively. Also, predicting the first quartile and fourth quartile of the observed traffic volume values by the support vector machine model is more accurate than two other models. Using predicted traffic parameters and providing them to travelers and transportation agencies by intelligent transportation systems leads to make a balance between travel demand and travel supply in the near future which is the main aim of this study. Travelers can have a better personal plan for their future trips based on these predictions. Also, the transportation agencies are more prepared to deal with critical traffic situations and can prevent traffic congestion.