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Call number: LD2668 .T4 1978 B624 / Master of Regional and Community Planning

published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

Traffic assignment is the last step in the conventional four-step transportation planning model, following trip generation, trip distribution, and mode choice. It concerns selection of routes between origins and destinations on the traffic network. Traditional traffic assignment methods do not consider trip chaining behavior. Since commuters always make daily trips in the form of trip chains, meaning a traveler’s trips are sequentially made with spatial correlation, it makes sense to develop models to feature this trip chaining behavior. Network performance in congested areas depends not only on the total daily traffic volume but also on the trip distribution over the course of a day. Therefore, this research makes an effort to propose a network traffic assignment framework featuring commuters’ trip chaining behavior. Travelers make decisions on their departure time and route choices under a capacity-constrained network. The modeling framework sequentially consists of an activity origin-destination (OD) choice model and a dynamic user equilibrium (DUE) traffic assignment model. A heuristic algorithm in an iterative process is proposed. A solution tells commuters’ daily travel patterns and departure distributions. Finally, a numerical test on a simple transportation network with simulation data is provided. In the numerical test, sensitivity analysis is additionally conducted on modeling parameters.

Traffic congestion continues to be a growing problem for cities of all sizes in the United States. Transportation agencies in urban areas are facing the difficult challenges of providing an efficient and reliable transportation system for residents and businesses despite ever-diminishing resources. Agencies in these areas need the capability of determining the future benefits of transportation investments so they can communicate this information to the public. This capability is difficult for many agencies, especially some of the smaller ones, who may not have the resources to make these analyses without turning to expensive long-range models. This research uses readily available socio-economic, land use, and traffic congestion data from many of the Texas urban areas to create prediction models to estimate future traffic congestion levels. Many of the transportation agencies that could utilize this tool do not have the resources to deal with large complex databases. Thus, basic information such as income, employment, single family residences, or commercial properties, to name a few, is used to create the predictions models. Results from this research show that traffic congestion prediction models can be created from socio-economic and land use data. These models were created for eighteen individual Texas urban areas and several combinations of areas. Transportation agencies could use the results of this research to estimate future congestion in their respective areas.

Thesis (M. Soc. Sc.)--University of Hong Kong, 1984.

This dissertation is concerned with developing new methods for exploring the pressing problems of uncertainty, Environmental Justice, and tolling as they relate to long-range transportation planning. While these topics are seemingly disparate, much of the work in this dissertation is motivated by the increasing number of roadway projects concessioned to the private sector, and the lack of tools available for evaluating the impact of such agreements on the public given high levels of uncertainty over the length of the contracts and concern for the welfare of traditionally underserved population groups. These issues will be considered separately and together, offering insights into how transportation investment decisions can be improved. To this end, the impacts of considering long-range uncertainty in the traffic assignment model as well as in an integrated transportation and land use model (ITLUM) are assessed in terms of the effects on network performance measures and roadway improvement decisions. A new method for accounting for correlations between the future travel demands of origin-destination zone pairs is developed for the traffic assignment problem that can more effectively model the effects of potential economic changes. Results showed that neglecting correlations can lead to measures of variance of future total system travel time that range from underestimating the actual measure by seventy-five percent to overestimating it by one hundred percent, and to different selections for a network improvement project in up to fifty percent of all scenarios. Uncertainty in a basic ITLUM is considered more broadly, incorporating probability distributions for population and employment inputs as well as several travel demand model parameters, and examining how the choice of performance measure impacts the effect of uncertainty on the decision of where to increase system capacity. Comparing the network improvement projects selected when uncertainty is considered to a deterministic analysis, showed differences in up to 25% of scenarios. Challenges of considering Environmental Justice, a type of group-based equity that is required for metropolitan transportation plan compliance in the United States, are explored, particularly with regard to appropriately defining the term equity for the analysis. Several of these potential definitions are then transformed into objective functions for use in a new formulation of the user equilibrium-based discrete network design problem. A multi-objective genetic-algorithm solution method is developed to solve the problem efficiently, and insights are revealed into how different definitions of equity can lead to different decisions. The following objectives, both commonly used in practice, were found to be conflicting: 1) minimizing the difference in post-improvement performance across populations and 2) minimizing the difference across populations in the change in performance due to improvements. The problem of roadway tolling is first examined from the perspective of a private sector toll road operator seeking to maximize the asset's value by exercising flexibility. A stochastic recourse model is developed to account for the first stage investment decision and the second stage decisions to alter network capacity and toll rates. The flexibility to engage in non-compete clauses whereby the public sector cannot improve competing roadways, and also to improve feeder links in the surrounding network were found to play important roles in asset valuation. The value of having these options was found to increase with an increase in uncertainty of future demand, complexity of network structure, and the consequence of failure to meet debt obligations. The three original issues of uncertainty, Environmental Justice, and tolling are woven together into the development of a new method for determining the maximum toll rate that can be applied in a private sector operation scenario (first option) such that each group within the population, as defined for analysis of Environmental Justice, is no worse off than if the road had been constructed by the public sector without tolling (second option). Three stochastic dominance criteria are implemented to find the toll rate at which the first option dominates the second given uncertainty about the future travel demand. Findings suggest that there may be many toll rates that equate the benefits resulting from the two options, so the minimum rate is considered the optimal one. The difference in benefits to the groups was found to increase with increasing value of time, and the differences in optimal toll rates using each of the three dominance criteria increased similarly. The analytical tools developed in this dissertation, and the resulting insights obtained should offer significant contributions to several areas of long-range transportation planning, particularly informing the process of concessioning roadways to private entities, developing a transportation system that is robust to future uncertainty, and ensuring that Environmental Justice criteria is met by considering the transportation needs of each group within the population. / text