Abstract :
Road traffic volumes in intercity roads are generally estimated by probability functions, statistical
techniques or meta-heuristic approaches such as artificial neural networks. As the road traffic
volumes depend on input variables and mainly road geometrical design, weather conditions, day
or night time, weekend or national holidays and so on, these are also estimated by pattern recognition
techniques. The main purpose of this research work is to check the using chaotic pattern of
daily traffic volume and the performance of chaos theory for estimating daily traffic. In this paper,
the existing chaotic behavior in daily traffic volume in intercity roads has been examined and also
the performance of chaos theory is discussed and compared to probability functions. The ratio
between the minimum and maximum of daily traffic volume is defined as chaos factor, and data,
gathered through installed automatic traffic counters over one year, have been used in analytical
process. Results revealed that daily traffic volumes have chaotic behavior with defined twenty-four
hour time span. They also show that the application of chaos theory is better than uniform distribution
function, while weaker than normal distribution function for estimating daily traffic volume.
