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Transport modelling is an essential tool for policy makers to make informed transportplanning decisions for the wellbeing of the society. However, most strategic transportmodels do not accurately incorporate the dynamics of population and households andtheir interactions, resulting in inaccurate forecasts of travel and land use demands. Whileit is highly crucial that strategic transport models provide accurate long-term forecaststhat are vital for the appraisal of large transport infrastructure projects, short-term andmedium-term forecasts are also integral for enhancing the efficiency of transport systems.Furthermore, in the modern, fast-paced world, emerging data sources are integral in bothtransport research and practice. For instance, data collected from smartphone travel surveysand electronic ticketing systems can be used to derive deep insights into people’s travelbehaviour. Therefore, this thesis is set out to investigate how population evolution modellingcan be improved in transport models, and also how emerging data sources can be leveragedto fill existing knowledge gaps.This thesis focuses on two main themes: (1) to explore new opportunities from emergingtechnologies and data sources and (2) to develop a dynamic microsimulation model that canprovide a more accurate population input for a transport demand model and also to enhancethe model development process. Using transit smart card data, The first main chapterexplores the impacts of transit ridership and reliability on residential property values inBrisbane, Australia. Three frequently used spatial regression techniques in hedonic studies are used along with a simple linear regression model. The second main chapter presentsthe first analysis on how social media offers a cost effective means to recruit and engagesmartphone travel survey participants. On top of that, recommendations on recruitment,marketing, sample representativeness, incentivisation, and deployment of the study arediscussed. The third mai

The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents' perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.

Against the backdrop of dynamic socio-technical change, travel demand analysis strives to derive insights into travel behaviour from new and established data sources to support strategic and operational processes in the public and private sectors. A principal concern of current travel demand analysis is the representation of unobserved heterogeneity to understand the complex patterns of travel demand. Hierarchical Bayesian models promise to satisfy the desiderata of contemporary travel demand analysis. However, they are not widely used in travel demand analysis, and existing estimation methods do not suit contemporary inference problems. This thesis has two aims: i) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of unobserved heterogeneity in travel demand models and to demonstrate the practical value of these representations in empirical applications, and ii) to advance and benchmark computational methods for posterior inference in hierarchical Bayesian models of travel demand. The aims are addressed over four main chapters. First, a hierarchical Bayesian multivariate Poisson log-normal model is used to analyse intergenerational differences in transport mode use among young adults in Germany. Second, parametric and semiparametric representations of unobserved heterogeneity in mixed logit models are compared and leveraged to infer distributions of willingness to pay for features of shared automated vehicle services in New York City. Third, several variational Bayes (VB) methods for posterior inference in mixed logit models are extended to admit utility specifications with both fixed and distributed parameters. Fourth, a VB method for posterior inference in mixed logit models with unobserved inter- and intra-individual heterogeneity is derived. In both the third and fourth main chapters, simulation studies are used to benchmark the VB methods against Markov chain Monte Carlo methods and frequentist maximum simulated l