A. James Clark School of Engineering

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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    Artificial Intelligence-Related Research Funding by the U.S. National Science Foundation and the National Natural Science Foundation of China
    (IEEE, 2020-10-06) Abadi, Hamidreza Habibollahi Najaf; He, Zhou; Pecht, Michael
    For the United States and China, artificial intelligence (AI) algorithms, methods, and applications are considered key to a nation's economic competitiveness and security. This paper investigates funding by the U.S. National Science Foundation and National Natural Science Foundation of China from 2010 to 2019, including the key institutions and universities that received AI awards, and the key AI disciplines and applications of focus in the research. Comparisons between the U.S. National Science Foundation and the National Natural Science Foundation of China, including the number of published papers as a result of the awards, are also presented.
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    PARAMETRIC AND NON-PARAMETRIC APPROACHES FOR THE PREDICTION OF THE DIFFUSION OF THE ELECTRIC VEHICLE
    (2020) Bas Vicente, Javier; Cirillo, Cinzia; Zofío Prieto, José Luis; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Driven by environmental awareness and new regulations for fuel efficiency, electric vehicles (EVs) have significantly evolved in the last decade, yet their market share is still much lower than expected. In addition to understanding the reasons for this slow market penetration, it is crucial to have appropriate tools to correctly predict the diffusion of this innovative product. Recent works in forecasting the EV market combine substitution and diffusion models, where discrete choice specifications are used to address the former, and Bass-type to account for the latter. However, these methodologies are not dynamic and do not consider the fact that innovation occurs through social channels among members of a social system. This research presents two advanced methodologies that make use of real data to evaluate the adoption of the EVs in the State of Maryland. The first consists of a disaggregated substitution model that considers social influence and social conformity, which is then embedded in a diffusion model to predict electric vehicle sales. The second, in contrast, relies on non-parametric machine learning techniques for the classification of potential EV purchasers. Both make use of data collected through a stated choice experiment specifically designed to capture the inclination of users towards EVs.
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    DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS
    (2018) Mishra, Saurabh; Ayyub, Bilal; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty.
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    Market Penetration of New Vehicle Technology: A Generalized Dynamic Approach for Modeling Discrete-Continuous Decisions
    (2017) LIU, YAN; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Energy consumption and greenhouse gas (GHG) emissions are at their highest levels in history. One of the largest sources of GHG emissions in the United States is from burning fossil fuels for transportation. In developing countries GHG emissions from private vehicles are growing rapidly with their wealth. Government agencies attempt to reduce dependency on fossil fuels by regulating the ownership/usage of private vehicles, promoting vehicles with higher engine efficiency, introducing new fuel types, and defining stricter emission standards. Hybrid and electric vehicles are gaining consumers’ interest and trust, and their sale shares are gradually increasing. Meanwhile, environmental awareness, taxes on conventional gasoline cars, and incentives for cars with new technologies, make small and alternative-fuel vehicles more attractive. The future of personal transportation is uncertain; in particular, car ownership, vehicle type preferences and usage behavior are likely to change in surprising ways. In this context, it is important to assess the influence of the vehicle market evolution on consumer’s vehicle demands and travel behaviors. This dissertation proposes a comprehensive modeling framework that is able to analyze different dimensions of the car purchasing and usage problem. A multi-facet approach is taken for the investigation, and different model types are proposed. The investigation starts with a mixed logit model that accounts for time-series choices, heterogeneity in preferences and correlation across alternatives. This model is estimated on Stated Preference Survey data collected in Maryland and quantifies market elasticities and willingness-to-pays for improving car characteristics. Afterward, a dynamic discrete choice model is developed to predict the diffusion of hybrid and electric cars in Maryland, with consideration of household’s forward-looking behavior and stochasticity in vehicle market evolution. This model focuses on vehicle purchase time and vehicle type choice. To further consider vehicle usage decision, an integrated discrete-continuous choice model is proposed to jointly estimate household’s discrete choices on vehicle type/ownership and continuous choice on vehicle usage. The model is applied to estimate household-level vehicle emissions in Maryland, USA and Beijing, China. The dissertation concludes with a sequential discrete-continuous choice model. The modeling framework is applied to estimate vehicle ownership and usage decisions of forward-looking agents over time in a finite time horizon. In particular, a recursive probit model is formulated to estimate a sequence of vehicle holding decisions, while a regression is used to estimate a sequence of vehicle usage decisions. The proposed model is tested and validated on simulated discrete and continuous choices in a car ownership problem setting. The dissertation contributes to the theory of dynamic models for discrete-continuous decisions. The sequential discrete-continuous choice model is the first to measure the dynamic interdependency between discrete choice and continuous choice over time. The dissertation also contributes to the understanding of critical transportation issues, including market penetration of new vehicle technology, estimation of household-level vehicle emissions, and policy evaluation for promoting green vehicles and reducing dependency on cars and emissions.
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    JOINT EVALUATION OF TRANSPORTATION REVENUE GENERATION AND INFRASTRUCTURE INVESTMENT POLICIES WITH BENEFITS REDISTRIBUTION CONSIDERATIONS
    (2017) Kastrouni, Eirini; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The purpose of this dissertation is to guide economic policymaking by providing a comprehensive estimation of the effects that transportation revenue generation and infrastructure investment policies have on users. A 10-level integration framework is proposed to capture the complex research question of revenue generation-investment-benefits redistribution, via the use of activity-based modeling and innovative data integration techniques. Revenue policies are not evaluated based on their first-level impacts on payers alone. On the contrary, they are combined with transportation investment outlooks, and their performance is assessed based on how users eventually benefit from the revenues being invested in transportation projects that facilitate their travel experience. The revenue policies explored include the status quo (fuel tax), a fuel tax increase, a flat VMT fee, an income-based VMT fee, a transportation-dedicated property tax, and a transportation-dedicated sales tax. Subsequently, three alternative transportation investment outlooks are explored; these outlooks may be adopted by Maryland in the future, in an effort to redefine the state’s purpose, perspective and vision with respect to transportation. The selected outlooks capture some of the most popular and widely discussed future transportation vision directions for the U.S. transportation agencies, and include: (i) network-wide bottleneck removal projects funded by the state fuel tax increase of 2015 in Maryland, (ii) development of a bus-only network funded by a transportation-dedicated property tax that is invested locally, and (iii) infrastructure retrofitting projects to accommodate connected and autonomous vehicles, funded by an income-based VMT fee. The policies’ performance is evaluated on the basis of tax incidence, travel behavior and revenue generation metrics, while changes in welfare measures are estimated to assess the benefits redistribution due to the proposed revenue-investment dyads. The redistribution analysis shows that investing in bottleneck removal or CAVs will partially alleviate the burden that users will experience due to the fuel tax increase and variable VMT fee policies. However, in a situation where transportation funding shifts from the status quo to a transportation-dedicated property tax, lower income HHs will bear greater burden, and none of the income groups or counties will be able to recuperate part of their losses via the transit-oriented investment.
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    DECISION ANALYSIS IN CONSTRUCTION CLAIMS
    (2016) Lessani, Arian; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Claims in construction projects are inevitable and can result in costly litigation. Construction contract ambiguity, overly restrictive terms, and unfairly allocated risks are among the factors increasing the likelihood of conflict between parties in construction claims. The source of conflict is a gap between parties’ beliefs over specifics of a claim. This research introduces a settlement negotiation model that provides methods for disagreeing parties to understand the gaps in their beliefs and possibly to come to an agreement before litigation. The quantitative decision analysis approach identifies a range for the optimal settlement amount in the claim process. Each party holds private information regarding its belief over the specifics of a claim. The specifics of a claim are classified into Liability, the likelihood of the defendant being found liable at a trial, and Damages, unanticipated expenditures plaintiff incurred due to the defendant’s alleged fault. A Bayesian Network model quantifies parties’ beliefs over Liability and Damages. This model represents parties’ legal arguments and their respective strengths and credibility. These beliefs become inputs to a non-cooperative game theory model. Non-cooperative game theory analyzes interactions between the claim parties at each stage of the claim. The asymmetric information game considers each party’s actions and strategy based on its belief over the expected outcome from litigation, and its belief over the opponent’s expected outcome from litigation. The analysis results in equilibriums that help parties decide how to resolve the claim and avoid costly and timely litigation. The resulting approach reveals predictive outcomes in construction claims using economic theory to analyze construction disputes.
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    On agent-based modeling: Multidimensional travel behavioral theory, procedural models and simulation-based applications
    (2015) Xiong, Chenfeng; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation proposes a theoretical framework to modeling multidimensional travel behavior based on artificially intelligent agents, search theory, procedural (dynamic) models, and bounded rationality. For decades, despite the number of heuristic explanations for different results, the fact that "almost no mathematical theory exists which explains the results of the simulations" remains as one of the large drawbacks of agent-based computational process approach. This is partly the side effect of its special feature that "no analytical functions are required". Among the rapidly growing literature devoted to the departure from rational behavior assumptions, this dissertation makes effort to embed a sound theoretical foundation for computational process approach and agent-based microsimulations for transportation system modeling and analyses. The theoretical contribution is three-fold: (1) It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modeled and integrated in a unified and coherent approach. (2) Procedural and dynamic agent-based decision-making is modeled. Within the model, agents make decisions. They also make decisions on how and when to make those decisions. (3) Replace conventional user equilibrium with a dynamic behavioral user equilibrium (BUE). Search start/stop criteria is defined in the way that the modeling process should eventually lead to a steady state that is structurally different to user equilibrium (UE) or dynamic user equilibrium (DUE). The theory is supported by empirical observations and the derived quantitative models are tested by agent-based simulation on a demonstration network. The model in its current form incorporates short-term behavioral dimensions: travel mode, departure time, pre-trip routing, and en-route diversion. Based on research needs and data availability, other dimensions can be added to the framework. The proposed model is successfully integrated with a dynamic traffic simulator (i.e. DTALite, a light-weight dynamic traffic assignment and simulation engine) and then applied to a mid-size study area in White Flint, Maryland. Results obtained from the integration corroborate the behavioral richness, computational efficiency, and convergence property of the proposed theoretical framework. The model is then applied to a number of applications in transportation planning, operations, and optimization, which highlights the capabilities of the proposed theory in estimating rich behavioral dynamics and the potential of large-scale implementation. Future research should experiment the integration with activity-based models, land-use development, energy consumption estimators, etc. to fully develop the potential of the agent-based model.
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    Choice Modeling Perspectives on Social Networks, Social Influence, and Social Capital in Activity and Travel Behavior
    (2015) Maness, Michael; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Understanding the determinants of activities and travel is critical for transportation policymakers, planners, and engineers to design and manage transportation systems. These systems, and their externalities, are interwoven with social systems in communities, cities, regions, and societies. But discrete choice models - the predominant modeling tool for researching travel behavior and planning transportation systems - are grounded in theories of individual decision-making. This dissertation expands knowledge about the incorporation of social interactions into activity-travel choice models in the areas of social capital and social network indicators; social influence motivations and informational conformity; and misspecification errors from social network data collection. Incorporating social capital into activity choice models involves using social capital indicators from surveys. Using a position generator question type, the role of social network occupational diversity in activity participation was explored and the performance of models using name generator and position generator data was compared. Access to the resources embedded in diverse networks (extensity) was found to positively correlate with leisure activity participation. Compared to core network indicators from name generators, position generator indicators were typically better at predicting activity participation in a cross-validation study. Current models of social influence in travel do not account for varying motivations for social influence such as for accuracy, affiliation, and self-concept. To test for an accuracy motivation, a latent class discrete choice model was formulated that places individuals into classes based on information exposure. Contrasting with existing work, this model showed that "more informed" households are more likely to own bicycles due to preference changes causing less sensitivity to smaller home footprints and limited incomes. A Bayesian prediction procedure was used to derive distributions of local-level equilibria and social influence elasticity. The effect of errors in social network data collection using name and position generators is not fully understood for choice models. In a case study, the social network occupational diversity measure was robust to varying position generator lengths. Simulation experiments tested the implications of social network structure, misspecification, and small samples on social influence choice models where sample size, social influence strength, and degree of misspecification had the greatest impact.
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    Agent-Based Models of Highway Investment Processes: Forecasting Future Networks under Public and Private Ownership Regimes
    (2012) Yusufzyanova, Dilya; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The present highway funding system, especially fuel taxes, may become a less reliable revenue source in the future, while the transportation public agencies do not have sufficient financial resources needed to meet the increasing traffic demand. In the last two decades there has been increasing interest in utilizing private sector to develop, finance and operate new and existing roadways in the United States. While transportation privatization projects have shown signs of success, it is not always clear how to measure the true benefits associated with these projects for all stakeholders, including the public sector, the private sector and the public. "Win-win" privatization agreements are tricky to make due to conflicting nature of the various stakeholders involved. Therefore, there is a huge need to study the welfare impacts of various road privatization arrangements for the society as a whole, and the financial implications for private investors and public road authorities. In order to address these needs, first, an empirical analysis is performed to study the investment decision processes of public transportation agencies. Second, the agent-based decision-making model is developed to consider transportation investment processes at different levels of government which forecasts future transportation networks and their performance under both existing and alternative transportation planning processes. Third, various highway privatization schemes currently practiced in the U.S. are identified and an agent-based model for analyzing regulatory policies on private-sector transportation investments is developed. Fourth, the above mentioned models are demonstrated on the networks with grid and beltway topologies to study the impacts of topology configuration on the privatization arrangements. Based on the simulation results of developed models, a number of insights are provided about impacts of ownership structures on the socio-economic performance in transportation systems and transportation network changes over time. The proposed models and the approach can be used in long-run prediction of economic performance intended for describing a general methodology for transportation planning on large networks. Therefore, this research is expected to contribute significantly to the understanding and selecting proper road privatization programs on public networks.
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    Discrete Choice Models for Revenue Management
    (2012) Hetrakul, Pratt; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In the transportation field, the shift of airline and railway industries toward web-based distribution channels has provided passengers with better access to fare information. This has resulted in passengers becoming more strategic to price. Therefore, a better understanding of passenger choice behavior is required in order to support fare strategies. Methods based on discrete choice (DC) analysis have recently been introduced in revenue management (RM). However, applications of DC models in railway ticket pricing are limited and heterogeneity in choice behavior across different categories of travelers has mostly been ignored. Differences in individual taste are crucial for the RM sector. Additionally, strategic passenger behavior is significant, especially in markets with flexible refund and exchange policy, where ticket cancellation and exchange behavior has been recognized as having major impacts on revenues. This dissertation examines innovative approaches in discrete choice modeling to support RM systems for intercity passenger railway. The analysis, based on ticket reservation data, contributes to the existing literature in three main aspects. Firstly, this dissertation develops choice models of ticket purchase timing which account for heterogeneity across different categories of passengers. The methodology based on latent class (LC) and mixed logit (ML) model framework offers an alternative approach to demand segmentation without using trip purposes which are not available in the data set used for the analysis. Secondly, this dissertation develops RM optimization models which use parameters estimated from the choice models and demand functions as key inputs to represent passenger response to RM policy. The approach distinguishes between leisure and business travelers, depending on departure time and day of week. The formulated optimization problem maximizes ticket revenue by simultaneously solving for ticket pricing and seat allocation. Strategies are subjected to capacity constraints determined on the basis of the railway network characteristics. Finally, this dissertation develops ticket cancellation and exchange model using dynamic discrete choice model (DDCM) framework. The estimated model predicts the timing of ticket cancellations and exchanges in response to trip schedule uncertainty, fare, and refund/exchange policy of the railway service. The model is able to predict new departure times of the exchanged tickets and covers the full range of departure time alternatives offered by the railway company.