Prediction of Next Day Traffic Volume Using Artificial Neural Networks 

Pawan Lingras, Saint Mary’s University, Canada
Satish C. Sharma, University of Regina, Canada
Phil Osborne, University of Regina, Canada
Iftekhar Kalyar, University of Regina, Canada

Abstract 
The modern concepts and techniques of the Intelligent Transportation Systems (ITS) provide opportunities for traffic engineers and decision-makers to deal with problems related to highway traffic operation and congestion management. Collection of real-time data and short-term prediction of traffic volume are among the critical needs of an ITS. This paper studies characteristics of different traffic volume time series. In particular, time series analysis is applied to the prediction of daily traffic volumes. The daily traffic volume is estimated by using the previous thirteen days traffic volumes. The study involved comparison of statistical and neural network techniques for time series analysis. The analysis was applied to different types of road groups according to the trip purpose and trip length distribution. It is hoped that this study will provide a better understanding of various issues involved in the short-term prediction of traffic volumes.

Keywords: Time series analysis, Time delay Neural Networks, Autoregression, Daily Traffic Volumes, Intelligent Transportation Systems.