Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item AN INTEGRATED AGBM-DTA MODEL FOR OPTIMIZING THE TRANSPORTATION SYSTEM BENEFITS OF PERSONALIZED MONETARY AND NON-MONETARY INCENTIVES(2021) Zhao, Jun; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The employment of different types of incentives in transportation systems to form advanced transportation congestion management solutions has garnered significant attention recently. This dissertation develops an integrated and personalized incentive scheme to incentivize more system-beneficial travel and mobility options considering both monetary and non-monetary incentives. In real-world case, when offered different travel options, the users usually choose the options with higher individual benefits, while the incentive providers aim to maximize system benefits. Therefore, conflicts occur between the agents (the users) and the principals (the incentive providers/system benefit optimizers) because the agents act solely based on their own interests. Thus, the principals provide both monetary and non-monetary incentives to minimize the agents’ efforts of altering their travel behaviors. To optimize system benefits, we continue investigating the allocation of monetary and non-monetary incentives in different scenarios with different incentive budgets. Furthermore, to analyze and visualize the impact of different incentive policies, we propose to build an integrated AgBM-flashDTA model, namely an agent-based behavior model (AgBM) integrated with a dynamic traffic assignment model (DTA). The flashDTA is a newly developed DTA model with a tree-based framework to do traffic assignment. This novel assignment method can converge in seconds, much faster than other simulation tools, making the model a powerful tool for supporting real-time decision-making. Finally, through a demonstrative case study for a large-scale transportation system in the Washington D.C. and Baltimore regions, the capability of the proposed scheme is highlighted with significant system-level savings, reasonable insights on individual travel behavior responses, as well as superior computation efficiency.Item INTEGRATING ACTIVITY-BASED TRAVEL DEMAND AND DYNAMIC TRAFFIC ASSIGNMENT MODEL: A BEHAVIORAL USER EQUILIBRIUM APPROACH(2018) Yang, Di; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recently, the focus of transportation planning has evolved from accommodating long-term mobility needs to providing near-term and more efficient transportation systems management and operations (TSMO) solutions, the result of limited transportation funding and road capacity build-out. This planning-for-operation concept calls for modeling tools that are sensitive to dynamic interactions between travel behavior and network supply so that the impacts of emerging TSMO strategies (e.g., variable road pricing, ramp metering, etc.) can be accurately estimated. The integration of activity-based travel demand models (ABM) and dynamic traffic assignment (DTA) models offer a perfect solution. However, existing operational integrated ABM-DTA models suffer from several limitations, including excessively long runtime and poor convergence quality, which severely hinders large-scale implementations. This dissertation proposes to integrate operational ABM and DTA models based on an innovative behavioral foundation: behavior user equilibrium (BUE). Different from the normative behavior theory (i.e., user equilibrium, or UE), BUE is based on a positive theory of travel behavior that avoids impractical assumptions, such as complete information and perfect rationality. BUE describes what travelers actually do in the system and thus emphasizes the role of information acquisition, knowledge updating, and learning in travel decision-making. The BUE-based model saves runtime because DTA models no longer need to run iteratively to reach UE internally and fewer agents undergo behavioral adjustments through iterations. In addition to runtime savings, the BUE principle proposes an alternative way to explain the behavior adjustment process and provides improved behavioral realism. This BUE-based integration framework is applied to the Washington-Baltimore Metropolitan Area as a case study. The integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council (BMC), and DTALite, a mesoscopic DTA model. The BUE-based integrated model is then compared with a traditional, sequentially integrated benchmark regarding model convergence and performance. Lastly, to enhance the transferability of the BUE-based integration approach, this dissertation develops a calibration method that estimates parameters associated with the BUE principle using readily available local data so that this integration framework can be easily applied to operational ABM and DTA models elsewhere.Item TRAFFIC IMPACT ANALYSIS OF SEVERAL DYNAMIC LANE MANAGEMENT STRATEGIES FOR CONGESTION MITIGATION BASED ON DTA MODEL(2016) Zhang, Ke; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Persistent daily congestion has been increasing in recent years, particularly along major corridors during selected periods in the mornings and evenings. On certain segments, these roadways are often at or near capacity. However, a conventional Predefined control strategy did not fit the demands that changed over time, making it necessary to implement the various dynamical lane management strategies discussed in this thesis. Those strategies include hard shoulder running, reversible HOV lanes, dynamic tolls and variable speed limit. A mesoscopic agent-based DTA model is used to simulate different strategies and scenarios. From the analyses, all strategies aim to mitigate congestion in terms of the average speed and average density. The largest improvement can be found in hard shoulder running and reversible HOV lanes while the other two provide more stable traffic. In terms of average speed and travel time, hard shoulder running is the most congested strategy for I-270 to help relieve the traffic pressure.Item Traffic Analysis on Cumulative Land Development and Transportation Related Policy Scenarios(2014) Zhu, Zheng; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Numerous methods have been developed to evaluate the impact of land developments and transportation policies on transportation infrastructures. But traditional approaches are either limited to static performance or a lack of behavior foundation. With only a few activity-based land development models in practice, this thesis integrates dynamic traffic assignment (DTA) with agent-based positive travel behavior model as a feasible tool for land development and transportation policy analysis. The integrated model enhances the behavior realism of DTA as well as captures traffic dynamics. It provides a low-cost approach to conduct new traffic analysis which emphasis on not only regional/local system mobility, but also individual behaviors. A land development analysis and a flexible work schedule policy analysis are illustrated in this paper. Unlike traditional land development impact studies, a great deal of travel behavior shift is obtained via this integrated model, which creates a new way for land development and policy analysis.Item Optimal Scheduling of Evacuation Operations with Contraflow(2008-11-21) El-Sbayti, Hayssam; Mahmassani, Hani; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Congestion due to evacuations can be catastrophic and life threatening. The sudden increase in demand will result in excessive loads on roads not typically designed to handle them, leading to network breakdown at the worst possible time. Moreover, since building new roads is infeasible, efficient utilization of the available network resources during disasters becomes one of the few options available to facilitate the movement of residents to safety. One option is to address the demand side of the problem, through demand scheduling. By scheduling the evacuation demand over a longer period, the congestion is staved off and network degradation is delayed. Advising traffic on when to evacuate, where to evacuate, and which route to take has the potential to improve evacuation times, especially in no-notice emergency conditions. Another option is to address the supply side of the problem, through network re-design. By reversing the direction of wisely selected lanes in a process known as contraflow, a temporary increase in the operational capacity is achieved without any major infrastructure changes. Both options, if planned correctly, have the potential to greatly ease network degradation and allow evacuees to reach safety sooner. Therefore, the ability to determine the joint optimal demand scheduling and network contraflow policies is of critical nature to the success of any evacuation plan. The objective of this study is to develop a simulation-based dynamic traffic assignment model that minimizes network clearance time at a minimum cost to the travelers by jointly considering demand scheduling and contraflow strategies.