Modelling urban travel time variability with the Burr regression technique

Abstract

The need for more reliable travel time in urban areas in order to provide better transport service to the community has attracted many studies to model travel time reliability and variability. The travel time distribution is basic knowledge for this modelling, and studies to fit continuous parametric distributions to travel time distribution have been conducted since the early 1950s. Two sets of empirical travel time data collected by GPS equipped vehicles in Adelaide indicate that travel time distributions are positively skewed and have long upper tails. The Burr distribution has been found to provide a good fit to the data. Utilising the Burr distribution properties and the Burr regression technique, this paper models the Adelaide urban arterial travel time variability by considering traffic variables such as link length, congestion index and degree of saturation. This study suggests how to fill some current gaps in travel time variability modelling, especially for urban arterial roads. It should also be useful for further travel time variability studies such as the valuation of travel time variability effects.

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