Thesis (M. Sc.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (p. 150-164). Also available via the World Wide Web.

Effects of route choice by Advanced Traveller Information Service (ATIS) were investigated in a study. To support the route choice behavior, the possibility of using collective traffic management systems which are mounted in public road space. To investigate what factors and what information must be communicated to the driver so that they change their route. In this work ATIS be examined in city traffic. The empirical study will verify what the role of ATIS is and which characteristics of the Information Service influence the decision strat-egy. Variable Message Signs will inform the driver while driving on possible alternatives and issues that have a major impact on the driver\\\'s decision. Here, the driver does not always make a decision within the meaning of homo oeconomicus, which increases the subjective benefits. But other determinants such as socio-demographic variables interacts with decision.

The overall goal of this research is to measure drivers' attitudes towards uncertain and unreliable routes. The route choice modeling is done within the discrete choice modeling framework and involved use of stated preference data. The first set of analysis elicits travelers' attitudes towards unreliable routes. The results of the analysis provide useful information in relation to how commuters value the occurrence/chances of experiencing delay days on their routes. The frequency of days with unexpected delays also measures the travel time reliability in a way that is easy to understand by day-to-day commuters. As such, behaviorally more realistic values are obtained from this analysis in order to capture travelers' attitudes towards reliability. Then, we model attitudes toward travel time uncertainty using non-expected utility theories within the random utility framework. Unlike previous studies that only include risk attitudes, we incorporate attitudes toward ambiguity too, where drivers are assumed to have imperfect knowledge of travel times. To this end, we formulated non-linear logit models capable of embedding probability weighting, and risk/ambiguity attitudes. A more realistic willingness to pay structure is then derived which takes into account travel time uncertainty and behavioral attitudes. Finally, we present a conceptual framework to use a descriptive utility theory, i.e. cumulative prospect theory in forecasting the demand for a variable tolled lane. We have highlighted the issues that arise when a prescriptive model of behavior is applied to forecast demand for a tolled lane.

In many countries across the world, fossil fuels, especially petroleum, are the largest energy source for powering the socio-economic system and the transportation sector dominates the consumption of petroleum in these societies. As the petroleum price continuously climbs and the threat of global climate changes becomes more evident, the world is now facing critical challenges in reducing petroleum consumption and exploiting alternative energy sources. A massive adoption of plug-in electric vehicles (PEVs), especially battery electric vehicles (BEVs), offers a very promising approach to change the current energy consumption structure and diminish greenhouse gas emissions and other pollutants. Understanding how individual electric vehicle drivers behave subject to the technological restrictions and infrastructure availability and estimating the resulting aggregate supply-demand effects on urban transportation systems is not only critical to transportation infrastructure development, but also has determinant implications in environment and energy policy enactment. Driving PEVs inevitably changes individual’s travel and activity behaviors and calls for fundamental changes to the existing transportation network and travel demand modeling paradigms to accommodate changing cost structures, technological restrictions, and supply infrastructures. A prominent phenomenon is that all PEV drivers face a distance constraint on their driving range, given the unsatisfactory battery-charging efficiency and scarce battery-charging infrastructures in a long period of the foreseeable future. Incorporating this distance constraint and the resulting behavioral changes into transportation network equilibrium and travel demand models (static and/or dynamic) raises a series of important research questions. This dissertation focuses on analyzing the impact of a massive adoption of BEVs on urban transportation network flows. BEVs are entirely dependent on electricity and cannot go further once the battery is depleted. As a modeling requirement in its simplest form, a distance constraint should be imposed when analyzing and modeling individual behaviors and network congestions. With adding this simple constraint, this research work conceptualizes, formulates and solves mathematical programming models for a set of new BEV-based network routing and equilibrium problems. It is anticipated that the developed models and methods can be extensively used in a systematic way to analyze and evaluate a variety of system planning and policy scenarios in decision-making circumstances of BEV-related technology adoption and infrastructure development. / text

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Thesis (Ph.D.)--Case Western Reserve University, 2003. / Includes bibliographical references (leaves 215-219).

Thesis(M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (p.116-119).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis(M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (p.116-119).

Thesis(M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (p.116-119).