Browsing by Subject "Toll roads"
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Item Assessing the environmental justice impacts of toll road projects(2010-12) Carroll, Lindsey Elizabeth; Bhat, Chandra R. (Chandrasekhar R.), 1964-; Prozzi, JolandaInadequate and uncertain transportation funding have in recent years resulted in a renewed emphasis on using investments that can be recovered by toll charges to finance new roads and modernize existing roads. This has raised questions about environmental justice (EJ) and how it pertains to tolling. In 2004, TxDOT Project 0-5208 was funded to propose an approach for the identification, measurement, and mitigation of disproportionately high or adverse impacts imposed on minority and low-income (EJ) communities by toll roads relative to non-tolled facilities. The methodology proposed had two equally important components: an analysis/quantitative component and an effective EJ participation component. However, the research raised concerns about the ability of various available analytical tools and analysis techniques to measure the potential impacts imposed on EJ communities by toll roads relative to non-toll roads. The objective of this thesis study was to extend the work that was conducted under TxDOT Research Project 0-5208 by (a) reviewing the ability of available tools and analysis techniques to quantify and qualitatively describe the EJ impacts associated with toll road projects and toll road systems through an evaluation of state-of-the-practice applications, and (b) recommending a suitable approach to assess the EJ impacts of toll roads and toll road systems on EJ communities. The research conducted to meet the study objectives has culminated in this thesis.Item A dual approximation framework for dynamic network analysis: congestion pricing, traffic assignment calibration and network design problem(2009-05) Lin, Dung-Ying; Waller, S. TravisDynamic Traffic Assignment (DTA) is gaining wider acceptance among agencies and practitioners because it serves as a more realistic representation of real-world traffic phenomena than static traffic assignment. Many metropolitan planning organizations and transportation departments are beginning to utilize DTA to predict traffic flows within their networks when conducting traffic analysis or evaluating management measures. To analyze DTA-based optimization applications, it is critical to obtain the dual (or gradient) information as dual information can typically be employed as a search direction in algorithmic design. However, very limited number of approaches can be used to estimate network-wide dual information while maintaining the potential to scale. This dissertation investigates the theoretical/practical aspects of DTA-based dual approximation techniques and explores DTA applications in the context of various transportation models, such as transportation network design, off-line DTA capacity calibration and dynamic congestion pricing. Each of the later entities is formulated as bi-level programs. Transportation Network Design Problem (NDP) aims to determine the optimal network expansion policy under a given budget constraint. NDP is bi-level by nature and can be considered a static case of a Stackelberg game, in which transportation planners (leaders) attempt to optimize the overall transportation system while road users (followers) attempt to achieve their own maximal benefit. The first part of this dissertation attempts to study NDP by combining a decomposition-based algorithmic structure with dual variable approximation techniques derived from linear programming theory. One of the critical elements in considering any real-time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. It is therefore imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies. Satisfactory calibration of the DTA model is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. In this dissertation, the off-line DTA capacity calibration problem is studied in an attempt to devise a systematic approach for effective model calibration. Congestion pricing has increasingly been seen as a powerful tool for both managing congestion and generating revenue for infrastructure maintenance and sustainable development. By carefully levying tolls on roadways, a more efficient and optimal network flow pattern can be generated. Furthermore, congestion pricing acts as an effective travel demand management strategy that reduces peak period vehicle trips by encouraging people to shift to more efficient modes such as transit. Recently, with the increase in the number of highway Build-Operate-Transfer (B-O-T) projects, tolling has been interpreted as an effective way to generate revenue to offset the construction and maintenance costs of infrastructure. To maximize the benefits of congestion pricing, a careful analysis based on dynamic traffic conditions has to be conducted before determining tolls, since sub-optimal tolls can significantly worsen the system performance. Combining a network-wide time-varying toll analysis together with an efficient solution-building approach will be one of the main contributions of this dissertation. The problems mentioned above are typically framed as bi-level programs, which pose considerable challenges in theory and as well as in application. Due to the non-convex solution space and inherent NP-complete complexity, a majority of recent research efforts have focused on tackling bi-level programs using meta-heuristics. These approaches allow for the efficient exploration of complex solution spaces and the identification of potential global optima. Accordingly, this dissertation also attempts to present and compare several meta-heuristics through extensive numerical experiments to determine the most effective and efficient meta-heuristic, as a means of better investigating realistic network scenarios.Item Implications of uncertain future network performance on satisfying environmental justice and tolling(2008-08) Duthie, Jennifer Clare, 1981-; Waller, S. TravisThis 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.Item A methodology for the environmental justice assessment of toll road projects(2006) Victoria-Jaramillo, Isabel Cristina; Walton, C. Michael