Using Automatic Vehicle Identification Data to Gain Insight into Travel Time Variability and Its Causes

Abstract

Unreliable transport systems cause stress and anxiety for travelers and create difficulties for authorities managing network operations. This study conducted a comprehensive investigation of travel time distributions in terms of various time windows based on extensive automatic vehicle identification data collected from the CityLink Tollway in Melbourne, Australia. The study also examines the components of travel time variability and explores their relationships. Given the number of factors affecting travel time variability and their interaction effects, multiple regression analysis is used to quantify the contribution of the various sources to the variability in travel time. Application of the methodology to two groups of data—namely, travel times in morning peak and afternoon peak—demonstrates that they have distinctive sources of variability. Morning peak travel times vary mostly because of driver-related factors, specifically lane choice decisions, whereas 25% of the variability of travel times in the afternoon peak corresponds to capacity-related factors.