Traffic volume time-series analysis according to the type of road use

problems related to highway traffic operation and congestion management can be alleviated with the use of modem intelligent transportation systems (IT Ss). Advanced Traveler Information Systems (ATIS) is one of the emerging te chnologies that will help travelers plan routes and schedules of their trip s so as to redistribute the traffic over the highway network. Such redistri bution will try to maximize the rise of available highway capacity. Collect ions of real-time data and short-term predictions of traffic volumes are am ong the critical needs of an ATIS. This article studies characteristics of different traffic volume time series. In particular; time-series analysis i s applied to the prediction of daily traffic volumes. The daily traffic vol ume 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 th at this study will provide a better understanding of various issues involve d in the short-term prediction of traffic volumes on different types of hig hways.