Civil & Environmental Engineering
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Item DEVELOPMENT OF AN INTEGRATED RIDE-SHARED MOBILITY-ON-DEMAND (MOD) AND PUBLIC TRANSIT SYSTEM(2019) XU, LIU; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Mobility-on-Demand (MOD) services, like the ones offered by Uber and Lyft, are transforming urban transportation by providing more sustainable and convenient service that allows people to access anytime and anywhere. In most U.S. cities with sprawling suburban areas, the utilization of public transit for commuting is often low due to lack of accessibility. Thereby the MOD system can function as a first-and-last-mile solution to attract more riders to use public transit. Seamless integration of ride-shared MOD service with public transit presents enormous potential in reducing pollution, saving energy, and alleviating congestion. This research proposes a general mathematical framework for solving a multi-modal large-scale ride-sharing problem under real-time context. The framework consists of three core modules. The first module partitions the entire map into a set of more scalable zones to enhance computational efficiency. The second module encompasses a mixed-integer-programming model to concurrently find the optimal vehicle-to-request and request-to-request matches in a hybrid network. The third module forecasts the demand for each station in the near future and then generates an optimized vehicle allocation plan to best serve the incoming rider requests. To ensure its applicability, the proposed model accounts for transit frequency, MOD vehicle capacity, available fleet size, customer walk-away condition and travel time uncertainty. Extensive experimental results prove that the proposed system can bring significant vehicular emission reduction and deliver timely ride-sharing service for a large number of riders. The main contributions of this study are as follows: • Design of a general framework for planning a multi-modal ride-sharing system in cities with under-utilized public transit system; • Development of an efficient real-time algorithm that can produce solutions of desired quality and scalability and redistribute the available fleet corresponding to the future demand evolution; • Validation of the potential applicability of the proposed system and quantitatively reveal the trade-off between service quality and system efficiency.Item Seismic Resilience-based Design and Optimization: A Deep Learning and Cyber-Physical Approach(2018) Wu, Jingzhe; Phillips, Brian M.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the growing acceptance and better understanding of the importance of uncertainties in seismic design, traditional design approaches with deterministic analysis are being replaced with more reliable approaches within a risk-based context. Recently, resilience has been increasingly studied as a comprehensive metric to assess the ability of a system to withstand and recover from disturbances with large uncertainties. For civil infrastructure systems susceptible to natural hazards, especially earthquakes as considered herein, seismic resilience could provide a measurement integrating both earthquake and post-earthquake performance. For structural engineers, improving infrastructure disaster resilience starts with the design of more resilient structures. This requires a quantitative approach to explicitly guild the design towards better resilience. However, when attempting to quantify the seismic resilience of a structure, large uncertainties lead to large computational costs associated with risk-based approaches. Additionally, the accuracy of numerical simulations under wide range of design scenarios is unknown. To address these challenges, this dissertation investigates the role of seismic resilience in structural design. This dissertation starts with a novel seismic protective device to improve structural resilience and follows with the development of a quantitative and efficient design, evaluation, and optimization framework for seismic resilience. This framework proposes metamodeling through deep neural networks for improved efficiency and cyber-physical systems for improved accuracy. Feedforward neural networks are adopted for fragility metamodeling, while online learning long-short term memory neural networks are developed for structural component metamodeling. Real-time hybrid simulation is used for the construction of cyber-physical systems. The proposed framework is demonstrated to have both improved accuracy and significantly reduced computational/experimental cost when compared to existing approaches. The applicability of the framework is illustrated through the optimization of structural systems for improved seismic resilience.Item Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies(2016) Champion, Billy Ray; Gabriel, Steven A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.Item INTEGRATED DYNAMIC DEMAND MANAGEMENT AND MARKET DESIGN IN SMART GRID(2014) Asudegi, Mona; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Smart Grid is a system that accommodates different energy sources, including solar, wind, tidal, electric vehicles, and also facilitates communication between users and suppliers. This study tries to picture the interaction among all new sources of energy and market, besides managing supplies and demands in the system while meeting network's limitations. First, an appropriate energy system mechanism is proposed to motivate use of green and renewable energies while addressing current system's deficiencies. Then concepts and techniques from game theory, network optimization, and market design are borrowed to model the system as a Stackelberg game. Existence of an equilibrium solution to the problem is proved mathematically, and an algorithm is developed to solve the proposed nonlinear bi-level optimization model in real time. Then the model is converted to a mathematical program with equilibrium constraints using lower level's optimality conditions. Results from different solution techniques including MIP, SOS, and nonlinear MPEC solvers are compared with the proposed algorithm. Examples illustrate the appropriateness and usefulness of the both proposed system mechanism and heuristic algorithm in modeling the market and solving the corresponding large scale bi-level model. To the best knowledge of the writer there is no efficient algorithm in solving large scale bi-level models and any solution approach in the literature is problem specific. This research could be implemented in the future Smart Grid meters to help users communicate with the system and enables the system to accommodate different sources of energy. It prevents waste of energy by optimizing users' schedule of trades in the grid. Also recommendations to energy policy makers are made based on results in this research. This research contributes to science by combining knowledge of market structure and demand management to design an optimal trade schedule for all agents in the energy network including users and suppliers. Current studies in this area mostly focus either in market design or in demand management side. However, by combining these two areas of knowledge in this study, not only will the whole system be more efficient, but it also will be more likely to make the system operational in real world.Item Offtake Strategy Design for Wind Energy Projects under Uncertainty(2014) Zhu, Xinyuan; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Energy use from wind, solar, and other renewable sources is a public policy at the federal and state levels to address environment, energy, and sustainability concerns. As the cost of renewable energy is still relatively high compared to fossil fuels, it remains a critical challenge to make renewable energy cost competitive, without relying on public subsidies. During recent years, much advance has been made in our understanding of technology innovations and cost structure optimization of renewable energy. A knowledge gap exists on the other side of the equation - revenue generation. Considering the complexity and stochastic nature of renewable energy projects, there is great potential to optimize the revenue generation mechanisms in a systematic fashion for improved profitability and growth. This dissertation examines two primary revenue generation mechanisms, or offtake strategies, used in wind energy development projects in the U.S. While a short-term offtake strategy allows project developers to benefit from price volatility in the wholesale spot market for profit maximization, a long-term offtake strategy minimizes the market risk exposure through a long-term Power Purchase Agreement (PPA). With Conditional Value-at-Risk (CVaR) introduced as a risk measure, this dissertation first develops two stochastic programming models for optimizing offtake designs under short and long-term strategies respectively. Furthermore, this study also proposes a hybrid offtake strategy that combines both short and long-term strategies. The two-level stochastic model demonstrates the merit of the hybrid strategy, i.e. obtaining the maximized profit while maintaining the flexibility of balancing and hedging against market and resource risks efficiently. The Cape Wind project in Massachusetts has been used as an example to demonstrate the model validity and potential applications in optimizing its revenue streams. The analysis shows valuable implications on the optimal design of renewable energy project development in regard to offtake arrangements.Item Efficient People Movement through Optimal Facility Configuration and Operation(2013) Feng, Lei; Miller-Hooks, Elise; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There are a variety of circumstances in which large numbers of people gather and must disperse. These include, for example, carnivals, parades, and other situations involving entrance to or exit from complex buildings, sport stadiums, commercial malls, and other type of facilities. Under these situations, people move on foot, commonly, in groups. Other circumstances related to large crowds involve high volumes of people waiting at transportation stations, airports, and other types of high traffic generation points. In these cases, a myriad of people need to be transported by bus, train, or other vehicles. The phenomenon of moving in groups also arises in these vehicular traffic scenarios. For example, groups may travel together by carpooling or ridesharing as a cost-saving measure. The movement of significant numbers of people by automobile also occurs in emergency situations, such as transporting large numbers of carless and mobility-impaired persons from the impacted area to shelters during evacuation of an urban area. This dissertation addresses four optimization problems on the design of facilities and/or operations to support efficient movement of large numbers of people who travel in groups. A variety of modeling approaches, including bi-level and nonlinear programming are applied to formulate the identified problems. These formulations capture the complexity and diverse characteristics that arise from, for example, grouping behavior, interactions in decisions by the system and its users, inconvenience constraints for passengers, and interdependence of strategic and operational decisions. These models aim to provide: (1) estimates of how individuals and groups distribute themselves over the network in crowd situations; (2) an optimal configuration of the physical layout to support large crowd movement; (3) an efficient fleet resource management tool for ridesharing services; and (4) tools for effective regional disaster planning. A variety of solution algorithms, including a meta-heuristic scheme seeking a pure-strategy Nash equilibrium, a multi-start tabu search with sequential quadratic programming procedure, and constraint programming based column generation are developed to solve the formulated problems. All developed models and solution methodologies were employed on real-world or carefully created fictitious examples to demonstrate their effectiveness.Item DYNAMIC RIDESHARE OPTIMIZED MATCHING PROBLEM(2012) Ghoseiri, Keivan; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation develops a Dynamic Rideshare Optimized Matching (DROM) model and solution that is aimed at identifying suitable matches between passengers requesting rideshare services with appropriate drivers available to carpool for credits and HOV lane privileges. DROM receives passengers and drivers' information and preferences continuously over time and maximizes the overall system performance subject to ride availability, capacity, rider and driver time window constraints, and detour and relocation distances while considering users' preferences. The research develops a spatial, temporal, and hierarchical decomposition solution strategy that leads to the heuristic solution procedure. Three-Spherical Heuristic Decomposition Model (TSHDM). Quality and validity tests for the TSHDM algorithm are done by comparison of results between the exact and implemented algorithm solutions and major sensitivity analyses using the technique of Regression Analysis on all of the related parameters in the model are conducted to thoroughly investigate the properties of the proposed model and solution algorithm. A case study is constructed to analyze the model and TSHDM behaviors on a road network of northwest metropolitan area of Baltimore city. The study shows that however DROM is a very complicated and challenging problem from both mathematical formulation and solution algorithm perspectives, it is possible to implement a dynamic rideshare system using appropriate technical tools and social networking media. Major sensitivity analysis conducted on several parameters and variables affecting the model shows that most influencing factors for the rate of success in the rideshare system are, in order of importance: number of participating drivers, number of stops, area size, and number of participating riders. The study also shows rate of success for the rideshare system is highly dependent to the matched routes connecting directly points of origin and destination for participating riders and also increasing the number of connections from one to two which requires two consecutive change of rides for a rider has the least impact on the rate of success.Item OPTIMIZATION OF STATION LOCATIONS AND TRACK ALIGNMENTS FOR RAIL TRANSIT LINES(2012) Lai, Xiaorong; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, right-of-way type, and various other system characteristics. The existing studies overlook the complex interactions between railway alignments and station locations in a practical design process. This study proposes a comprehensive methodology that helps transit planners to concurrently optimize station locations and track alignments for an urban rail transit line. The modeling framework resolves the essential trade-off between an economically efficient system with low initial and operation cost and an effective system that provides convenient service for the public. The proposed method accounts for various geometric requirements and real-world design constraints for track alignment and stations plans. This method integrates a genetic algorithm (GA) for optimization with comprehensive evaluation of various important measures of effectiveness based on processing Geographical Information System (GIS) data. The base model designs the track alignment through a sequence of preset stations. Detailed assumptions and formulations are presented for geometric requirements, design constraints, and evaluation criteria. Three extensions of the base model are proposed. The first extension explicitly incorporates vehicle dynamics in the design of track alignments, with the objective of better balancing the initial construction cost with the operation and user costs recurring throughout the system's life cycle. In the second extension, an integrated optimization model of rail transit station locations and track alignment is formulated for situations in which the locations of major stations are not preset. The concurrent optimization model searches through additional decision variables for station locations and station types, estimate rail transit demand, and incorporates demand and station cost in the evaluation framework. The third extension considers the existing road network when selecting sections of the alignment. Special algorithms are developed to allow the optimized alignment to take advantage of links in an existing network for construction cost reduction, and to account for disturbances of roadway traffic at highway/rail crossings. Numerical results show that these extensions have significantly enhanced the applicability of the proposed optimization methodology in concurrently selecting rail transit station locations and generating track alignment.Item Model and algorithm for solving real time dial-a-ride problem(2011) KIM, TAEHYEONG; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research studies a static and real-time dial-a-ride problem with time varying travel times, soft time windows, and multiple depots. First, a static DARP model is formulated as a mixed integer programming and in order to validate the model, several random small network problems are solved using commercial optimization package, CPLEX. Three heuristic algorithms based on sequential insertion, parallel insertion, and clustering first-routing second are proposed to solve static DARP within a reasonable time for implementation in a real-world situation. Also, the results of three heuristic methods are compared with the results obtained from exact solution by CPLEX to validate and evaluate three heuristic algorithms. Computational results show that three heuristic algorithms are superior compared to the exact algorithm in terms of the calculation time as the problem size (in terms of the number of demands) increases. Also among the three heuristic algorithms, the heuristic algorithm based on sequential insertion is more efficient than other heuristic algorithms that are based on parallel insertion and clustering first-routing second. For the case study, Maryland Transit Administration (MTA)'s real operation of Dial-a-ride service is introduced and compared with the results of developed heuristic. The objective function values from heuristic based on clustering first- routing second are better than those from MTA's operation for all cases when waiting cost, delay cost, and excess ride cost are not included in the objective function values. Also, the algorithm for real-time DARP considering dynamic events such as customer no shows, accidents, cancellations, and new requests is developed based on static DARP. The algorithm is tested in a simulation framework. In the simulation test, we compared the results of cases according to degree of gap between expected link speeds and real link speeds. Also for competitive analysis, the results of dynamic case are compared with the results of static case, where all requests are known in advance. The simulation test shows that the heuristic method could save cost as the uncertainty in new requests increases.Item Optimization of highway work zone decisions considering Short-term and Long-term Impacts(2010) Yang, Ning; Schonfeld, Paul M; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the increase of the number, duration, and scope of maintenance projects on the national highway system, transportation agencies face great challenges in developing effective comprehensive work zone management plans which minimize the negative impacts on road users and workers. The types of maintenance operation, timing, duration, configuration, and user impact mitigation strategies are major considerations in developing work zone management plans. Some of those decisions may not only affect road users during the maintenance phase but also have significant impacts on pavement serviceability in future years. This dissertation proposes a systematic methodology for jointly optimizing critical work zone decisions, based on analytical and simulation models developed to estimate short-term impacts during the maintenance periods and long-term impacts over the pavement life cycle. The dissertation starts by modeling the effects of different work zone decisions on agency and user costs during the maintenance phase. An analytic one-time work zone cost model is then formulated based on simulation analysis results. Next, a short-term work zone decision optimization model is developed to find the best combination of lane closure and traffic control strategies which can minimize the one-time work zone cost. Considering the complex and combinatorial nature of this optimization problem, a heuristic optimization algorithm, named two-stage modified population-based simulated annealing (2PBSA), is designed to search for a near-optimal solution. For those maintenance projects that may need more detailed estimation of user delay or other impacts, a simulation-based optimization method is proposed in this study. Through a hybrid approach combining simulation and analytic methods along with parallel computing techniques, the proposed method can yield satisfactory solutions while reducing computational efforts to a more acceptable level. The last part of this study establishes a framework for jointly optimizing short-term and long-term work zone decisions with the objective of maximizing cost-effectiveness. Case studies are conducted to test the performance of the proposed methods and develop guidelines for development of work zone management plans.