Traffic Volume Time‐Series Analysis According to the Type of Road Use

Abstract

Problems related to highway traffic operation and congestion management can be alleviated with the use of modern intelligent transportation systems (ITSs). Advanced Traveler Information Systems (ATIS) is one of the emerging technologies that will help travelers plan routes and schedules of their trips so as to redistribute the traffic over the highway network. Such redistribution will try to maximize the use of available highway capacity. Collections of real‐time data and short‐term predictions of traffic volumes are among the critical needs of an ATIS. This article 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 13 daily traffic volumes. The study involves a comparison of statistical and neural network techniques for time series analysis. The analysis is 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 on different types of highways.