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Predictability is important in decision making in many fields, including transport. The fluctuations of time-varying and short-term traffic processes pose critical challenges for real-time traffic predictions, which are the backbone of the intelligent transportation systems (ITS). The success of different prediction techniques depends on the structure of the phenomenon, in particular whether it is easily predictable or complex. Therefore, understanding the various aspects leading to high/low complexity in traffic time series data is critical in evaluating the performance of urban traffic systems.This thesis presents the application of the Hurst exponent metric from Fractal theory in quantifying fluctuations in different micro and macroscopic parameters of urban traffic. Data from three distinct sources and geographical locations are used for the analysis. i) Vehicle trajectory data collected every 0.5sec from an arterial road in India and a freeway in the USA are utilised to evaluate fluctuations in lateral movements and speeds of thousands of vehicles. The effect of the average lateral position and vehicle type on fluctuations is also examined.ii) Loop detector data collected every 30sec from monitor sites on an urban motorway in Australia are used to quantify fluctuations in speed and flow. The effect of the time of the day, weekend, proximity to ramps, and the presence of a horizontal curve, on fluctuations is explored using the analysis of variances. Further, latent class and random parameters Tobit models are estimated to predict the effect of the Hurst exponent (long-range dependence) of speed on crash rates at motorway sites.iii) Traffic count data collected every 5min at signalised intersections in Sydney, Australia are studied to understand the patterns of predictability. The effect of the day of the week, public holidays, special events, weather, etc. on predictability is discussed using a random effects linear regression model. This thesis is data-driven

The dominant challenge in one-way carsharing systems is the vehicle stock imbalance. Previous studies have proposed relocation approaches to handle it using optimization and simulation models. However, these models do not consider the interdependence between supply and demand in carsharing systems. In this paper, we develop an integrated optimization model to link supply and demand together. A discrete choice model that includes vehicle availability as a parameter directly affecting user’s mode choice is introduced and incorporated within the optimization formulation. In this framework, carsharing travel demand is influenced by vehicle supply. The reaction of the demand further changes vehicle availability in the system. The incorporation of a discrete choice model with the Integer Linear Programming formulation leads to a nonlinear model. We propose a linearization scheme to reformulate it. We test the model in realistic case studies representative of an Australian carsharing operator. A sensitivity analysis on total travel demand, system capacity, one-way trip price, and vehicle availability coefficient is undertaken to evaluate their impacts on system profit. The results reveal that the pattern of profit over trip price varies across scenarios with different vehicle availability coefficients and travel demand. The profit-efficiency of enlarging carsharing network is also dependent on travel demand. We conclude that the interdependence between demand and supply should be considered when setting network development plans and pricing strategies in one-way carsharing systems. If there is a strong interaction between demand and supply, the supply of carsharing vehicles has a critical impact on system profit. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

Not-for-profit food rescue organisations play a vital role in alleviating hunger in many developing and developed countries. They rescue surplus food from the business sector and re-distribute it to welfare agencies supporting different forms of food relief. A food rescue organisation’s potential to balance both collection and delivery requirements is challenging, particularly when the network is diverse, resources are limited, and characteristics of the rescued food items influence the collection and delivery process. This thesis presents various statistical and mathematical models to facilitate the sustainable, efficient, fair and cost-effective flow of rescued surplus food products from point of donation to point of delivery. Various statistical modelling techniques including multiple linear regression, structural equation modelling and neural networks are used to explore the patterns and dynamics of food donation and the distribution process. Results suggest that structural equation modelling and neural networks provide better estimations for each food type when compared to conventional multiple linear regression. In addition, this thesis aims at exploring the potential of vehicle routing, scheduling and resource allocation models to address the concerns of food rescue and delivery operations. Specifically, in incorporating the perishability of products, level of fairness in the distribution of limited rescued food, uncertainty in supply and the number of food providers, etc., in addition to the traditional cost-effective operation, which are highly relevant to the operational policies of food relief logistics. The models are formulated by extending the unpaired pickup and delivery vehicle routing problem, variants of the classical vehicle routing problem. Application of these models in real case scenarios suggests a reduction in the overall operational cost along with a reduction in waste and improvement in fairness.The core contribution of the research is the

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 findin