Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Abstract

Summary The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.

Improved Grey System Models for Predicting Traffic Parameters

“…The proposed model performed better in predicting AADTs. Badhrudeen et al (2016) compared the performance of the GM(1,1) model against neural network models (NNs). Using about 100 minutes of observational data to train the GM(1,1) model, the results indicated that the GM(1,1) model outperformed the NNs by 1 to 3% in mean absolute percent error (MAPE).…”

An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment

“…Hence, aiming at reliability prediction and evaluation, the method based on artificial neural network would be better. Among these artificial neural networks, backpropagation (BP) neural network is the most commonly used, especially in practical application [17,18]. Moreover, BP network has apparent advantages in self-learning, selforganizing, good fault tolerance, and excellent nonlinear approximation ability [19].…”