No description available.

No description available.

No description available.

No description available.

No description available.

Past political regimes and socio-economic imbalances have led to the formation of a transport system in the Republic of South Africa (RSA) that is unique to the developing world. Affluent communities in metropolitan cities are situated close to economic activity, whereas the people in need of public transport are situated on the periphery of the cities. This demographic structure is opposite to that of developed countries and complicates both the provision of transport services and the planning process thereof. Multi-Agent Transport Simulation (MATSim) has been identified as an Agent-Based Simulation (ABS) approach that models individual travellers as autonomous entities to create large scale traffic simulations. The initial implementation of MATSim in the RSA successfully simulated private vehicle trips between home and work in the province of Gauteng, proving that there is enough data available to create a realistic multi-agent transport model. The initial implementation can be expanded to further enhance the simulation accuracy, but this requires the incorporation of additional primary and secondary activities into the initial transport demand. This study created a methodology to expand the initial implementation in the midst of limited data, and implemented this process for Gauteng. The first phase constructed a 10% synthetic population that represents the demographic structure of the actual population and identified various socio-demographic attributes that can influence an individual's travel behaviour. These attributes were assigned to the synthetic agents by following an approach that combines probabilistic sampling and rule-based models. The second phase used agents' individual attributes, and census, National Household Travel Survey (NHTS) and geospatial data to transform the synthetic population into a set of daily activity plans - one for every agent. All the agents' daily plans were combined into a plans.xml file that was used as input to MATSim, where the individuals' activity plans were executed simultaneously to model the transport decisions and behaviour of agents. Data deficiencies were overcome by contemplating various scenarios and comparing the macroscopic transport demand patterns thereof to the results of the initial implementation and to actual counting station statistics. This study successfully expanded the initial home-work-home implementation of MATSim by including additional non-work activities in the transport demand. The addition of non-work activities improved the simulation accuracy during both peak and off-peak periods, and the initial demand therefore provides an improved representation of the travel behaviour of individuals in Gauteng. / Dissertation (MEng)--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted

We consider the Lighthill-Whitman-Richards traffic model on a network consisting of a highway with an off ramp, connected by a junction. We compare the known coupling conditions for the evolution of traffic at the junction and suggest a novel improvement to the existing conditions. That is, we resolve the spurious effects that arise in standard models, namely clogging of the main highway and vehicle destination changes. We achieve this by tracking vehicle density buildup in the form of a queue, which is modeled by an ODE. We define the solution to the Riemann problem at the junction using the supply and demand functions. The numerical approximation is carried out using a modified Godunov scheme, adjusted to take into account the effects of an emptying queue. Exact and numerical comparisons of the model with existing models verify that the number of vehicles who wish to exit are preserved and the nonphysical clogging of the main highway does not occur. / Mathematics

No description available.

The realization that the U.S. infrastructure is deteriorating and that there is a need to establish a strategy to prevent an infrastructure catastrophe have propelled the development of various infrastructure management systems. Often, the expansion of transportation facilities is regarded as a means to the improvement of the condition of transportation infrastructure. However, building more infrastructure than can be properly maintained causes serious deterioration of the existing infrastructure. Sustainable development from a highway management perspective can be equated with qualitative development, which improves the current condition of the highway system, rather than expanding its physical resources. The objective of this research is to develop a highway management strategy to help achieve sustainable development for the Commonwealth of Virginia. This research is performed by developing a transportation planning model for state highway management (TPMSHM) within the framework of a decision support system (DSS). The planning model consists of ten subsystems, including pavement and bridge management subsystems. These subsystems encompass various socioeconomic parameters that influence the physical status of highways. In the dynamic simulation model, these parameters are expressed in causal relationships using a system dynamics methodology. The types of trajectories for highway conditions that lead to sustainable development are provided. This research proposes a state-dependent prioritization strategy for calculating efficient budget shares by hierarchical levels of highway conditions. In this strategy, the proportions of the highway budget allocated to each level of management activity are determined by the physical conditions of the highways. Highways in the worst condition are given the first priority to receive the budget allocations. The model also addresses the policy of raising fuels tax to increase the state's transportation revenue. The adverse impact of a fuels tax increase is discussed in terms of revenue, the physical sufficiency of highways, and user benefits. The TPMSHM constitutes a leading component of the DSS and governs the building processes of other two components, which include a Data Base and a Display Base. A Data Base is constructed by listing all the parameters needed by the TPMSHM within a frame designed in terms of the records and fields of the parameters. A Display Base is demonstrated in a possible form using system dynamics' Powersim software. The graphical capability of representing the simulation results and the interactive user interface inherent in the software are examined. The emphasis of this research is placed on the development of the TPMSHM, which strives to manage the physical condition of the state highway system at an acceptable level through a state-dependent prioritization strategy to achieve sustainable development. / Ph. D.

Understanding the differences among public transit types allows for the development of more targeted policies at both local and regional levels. Examining how the built environment (BE) influences travel behavior (Delclòs-Alió et al.) and assessing data aggregation effects around different transit station types at the local level, along with identifying key predictors of ridership across transit modes at the regional level, offers valuable insights for policy efforts. Specifically, the dissertation comprises three studies that analyze BE-travel behavior associations and data aggregation effects locally, as well as variations in key predictors of rail and bus ridership at a regional scale within the United States. The findings emphasize the unique land-use and travel behavior associations for various public transit systems within transit catchment areas, the effects of data aggregation on BE-travel behavior models, and the critical predictors of rail and bus ridership at regional levels. The first study highlights nonlinear associations between BE attributes and commuting mode share within rail and Bus Rapid Transit (BRT) catchment areas, using data from approximately 2,790 transit stations across 34 U.S. metropolitan statistical areas. Applying a random forest model, this study reveals substantial differences between rail and BRT areas, with rail catchment areas showing greater sensitivity to BE factors in reducing car dependency. BRT, however, emerges as a viable alternative for sprawling areas lacking the compact development needed to support rail systems. The second study investigates how data aggregation influences the BE-mode share relationship around 2,794 rail and BRT stations, utilizing both inferential and machine learning approaches. Findings indicate that data aggregation affects BE-mode share models regardless of the analytical method. Optimal buffer sizes for capturing BE effects and minimizing sensitivity to data aggregation were identified as 800 meters for BRT stations and 1,000 meters for rail stations. Key BE features such as employment density, jobs per household, intersection density, residential density, distance from the central business district, job accessibility (active modes) demonstrated robustness against data aggregation for both rail and BRT stations. The third study examines changes in transit ridership predictors before and after the COVID-19 pandemic across 35 U.S. metropolitan areas. Using extreme gradient boosting on data spanning January 2019 to June 2023, the study identifies a shift from internal to external factors as key drivers of ridership post-pandemic. Socioeconomic factors, gasoline prices, telecommuting, population density, employment density and polycentric development emerged as influential for bus ridership post-pandemic, while traditional factors like vehicle revenue miles, fare, transit coverage, and service areas are more important for rail ridership. Additionally, the study uncovers unique threshold and interaction effects in the post-pandemic period, including positive interactions between African American population proportions and poverty rates for bus ridership, carless households and gasoline prices for bus ridership, and between VRM and polycentricity for rail ridership. This dissertation provides insights into the complex dynamics between BE, transit types, and travel behavior, offering valuable implications for urban transportation planning and policy development at multiple levels. / Doctor of Philosophy / This research explores how different aspects of urban design impact public transit use and helps identify what drives ridership on different types of transit. By studying connections between urban layouts and travel habits around transit stations, the findings offer guidance for creating tailored local and regional transit policies. Specifically, the research looks at three key areas: how built environments relate to travel choices locally, how data processing methods influence results, and what factors influence bus and rail ridership across U.S. cities. The first part reveals that the design of areas around rail and Bus Rapid Transit (BRT) stations affects travel patterns in unique ways. Rail stations tend to decrease car use in well-developed areas, while BRT stations work better in sprawling urban settings, where compact rail development isn't feasible. The second part shows that the way data is organized can change how we understand the link between urban form and transit use. For example, analyzing a broader area (up to 1,000 meters) around rail stations captures the effect of local design better, while an 800-meter radius is optimal for BRT. Certain features, like job density and proximity to the city center, consistently predict transit use, regardless of the data scale. Finally, the third part examines changes in what drives transit ridership since the COVID-19 pandemic. While pre-pandemic ridership was mostly influenced by operational factors like service coverage, post-pandemic ridership is more affected by external factors like gas prices and remote work trends. Unique patterns also emerge, such as links between certain demographics and bus ridership and between economic factors and rail use. Overall, this study helps planners and policymakers understand the unique needs of rail and bus systems, supporting strategies to make public transit more effective and responsive to community needs.