Using License Plate Recognition Data to Gain Insight into Urban Travel Time Distributions

Travel time distribution is an essential tool for measuring urban traffic performance, a subject that has been studied for decades. This paper conducts a comprehensive investigation of two types of travel time distributions using extensive license plate recognition data from Automatic Number Plate Recognition techniques on four signalized arterials in Guiyang, China. The travel time plane distributions presented in the overlay charts of observed travel times usually exhibit significant stratified data strips. When considering signal schemes, we observe that the cycle times of the first upstream and last downstream intersections are the determining factors for the data patterns of travel time plane distributions. We also investigate the characteristics of single or multiple peaks within various departure time windows. The results indicate that travel time statistical distributions are more likely to exhibit multiple states under short time windows. As for the shapes of travel time statistical distributions, skewness and kurtosis are used as descriptive statistics. The results show that the majority of statistical distributions are positively skewed and leptokurtic, and the skewness is highly correlated with the kurtosis. A stable skewness and kurtosis at a relatively lower level may be caused by lower travel time reliabilities.