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Abstract
Driving in stop-&-go (S&G) traffic can be a frustrating experience and can lead to an increase in fuel emissions, driver distress and safety risks. Past studies have modelled the time spent in S&G traffic as one of the factors for the disutility of a route. These studies did not account for the effect of the number of S&G waves, which has been found to bear a closer relationship with driver discomfort. This thesis tests the research hypothesis that an increase in the number of S&Gs on a route increases its disutility for a driver. As a proof of concept, a Stated Choice (SC) experiment was initially conducted on a sample of university staff and students. A D-efficient pivot design technique was used to generate the set of choice tasks. The competing routes were defined in terms of the number of S&Gs along with other travel attributes. The collected data was analysed using a Random Parameter Error Component Logit model to account for the taste heterogeneity and serial correlation among the choice tasks. The results showed that the number of S&Gs negatively impacted the utility of a route, thus validating the proposed hypothesis. Another follow-up SC study was then conducted on a sample of general commuters in Sydney. The data from this study was analysed using a Latent Class Choice Model which relaxed the distributional assumption associated with the previous statistical model. The obtained results showed a negative effect for the number of S&Gs, except for nearly one quarter of the sample which was indifferent towards this attribute. Lastly, a driving simulator experiment was conducted to further understand the role of S&G traffic characteristics on driver frustration. Participants were made to drive through virtual scenarios depicting varying S&G traffic conditions. A Structural Equation Model was estimated on the data which indicated that the number of S&Gs had a positive effect on the level of frustration and was also observed to influence route choice. The findings from this thesis not only extend the body of knowledge on intricate route choice behaviour of drivers, but also inform policies aimed at reducing traffic congestion and the resulting S&G traffic.