Estimation of shift parameter of headway distributions using crosscorrelation function method

The headway distributions are key building blocks for microscopic traffic flow characteristics which involves in the safety, level of service, driver behavior, and capacity of a transportation system. Usually, theoretical headway distributions show shift patterns compared to measured headway distributions. Therefore, an evaluation of the magnitude of shift is important in microscopic traffic flow. A crosscorrelation function method is used to estimate shift parameter that matches measured relative frequency and theoretical relative frequency (density function) in an optimal manner. In a worked example, both simple shift model and complicated shift model are examined. In the simple shift model, shift value does not affect the estimated parameters of density function. The complicated shift model considers the effect of shift value on the estimated parameters of density function. In the worked example, using crosscorrelation function method, 0.4 second is selected as shift value for density function. It concludes that crosscorrelation method demonstrates simple and intuitive advantages over random selection of shift parameter