Analysis of Travel Time Distribution for Varying Length of Time Interval

This study intends to determine the most appropriate distribution for modeling travel time variability. It also aims to explore the effects of the time of day and the length of the analysis  time interval on the type of the bestfit probability distribution function. To this end, four analysis time intervals of different lengths ranging from five minutes to three hours are considered. Subsequently, for each analysis time interval, travel time data collected at different times of day are fitted to 12 common probability distribution functions. The Akaike Information Criterion is then used to evaluate the goodness of fitting and to rank the probability distribution functions. The results of this study indicate that the Gaussian mixture distributions are superior to single distributions to represent travel time distribution. In addition, single probability distribution functions can model the distribution of travel time observations when the length of the time interval is short. Among single probability distribution functions, the burr distribution provides the best fit to the travel time data. The results of this research also show that the type of the bestfit probability distribution function does not change significantly over the time of day.