Matching Items (20)
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- All Subjects: Civil Engineering
- Creators: Zhou, Xuesong
- Member of: Theses and Dissertations
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
Urban water systems face sustainability challenges ranging from water quality, leaks, over-use, energy consumption, and long-term supply concerns. Resiliency challenges include the capacity to respond to drought, managing pipe deterioration, responding to natural disasters, and preventing terrorism. One strategy to enhance sustainability and resiliency is the development and adoption of smart water grids. A smart water grid incorporates networked monitoring and control devices into its structure, which provides diverse, real-time information about the system, as well as enhanced control. Data provide input for modeling and analysis, which informs control decisions, allowing for improvement in sustainability and resiliency. While smart water grids hold much potential, there are also potential tradeoffs and adoption challenges. More publicly available cost-benefit analyses are needed, as well as system-level research and application, rather than the current focus on individual technologies. This thesis seeks to fill one of these gaps by analyzing the cost and environmental benefits of smart irrigation controllers. Smart irrigation controllers can save water by adapting watering schedules to climate and soil conditions. The potential benefit of smart irrigation controllers is particularly high in southwestern U.S. states, where the arid climate makes water scarcer and increases watering needs of landscapes. To inform the technology development process, a design for environment (DfE) method was developed, which overlays economic and environmental performance parameters under different operating conditions. This method is applied to characterize design goals for controller price and water savings that smart irrigation controllers must meet to yield life cycle carbon dioxide reductions and economic savings in southwestern U.S. states, accounting for regional variability in electricity and water prices and carbon overhead. Results from applying the model to smart irrigation controllers in the Southwest suggest that some areas are significantly easier to design for.
ContributorsMutchek, Michele (Author) / Allenby, Braden (Thesis advisor) / Williams, Eric (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model systems in practice today are largely tour-based model systems that simulate individual daily activity-travel patterns through the prediction of day-level and tour-level activity agendas. These tour level activity-based models adopt a discrete time representation of activities and sequence the activities within tours using rule-based heuristics. An alternate stream of activity-based model systems mostly confined to the research arena are activity scheduling systems that adopt an evolutionary continuous-time approach to model activity participation subject to time-space prism constraints. In this research, a tour characterization framework capable of simulating and sequencing activities in tours along the continuous time dimension is developed and implemented using readily available travel survey data. The proposed framework includes components for modeling the multitude of secondary activities (stops) undertaken as part of the tour, the time allocated to various activities in a tour, and the sequence in which the activities are pursued.
Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice.
Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice.
ContributorsGarikapati, Venu Madhav (Author) / Pendyala, Ram M. (Thesis advisor) / Zhou, Xuesong (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The objective of this research study was to develop a methodology for calculating a pavement condition index (PCI) based on historical distress data collected in the databases from Long-Term Pavement Performance (LTPP) program and Minnesota Road Research (Mn/ROAD) project. Excel™ templates were developed and successfully used to import distress data from both databases and directly calculate PCIs for test sections. Pavement performance master curve construction and verification based on the PCIs were also developed as part of this research effort. The analysis and results of LTPP data for several case studies indicated that the study approach is rational and yielded good to excellent statistical measures of accuracy.
It is believed that the InfoPaveTM LTPP and Mn/ROAD database can benefit from the PCI templates developed in this study, by making them available for users to compute PCIs for specific road sections of interest. In addition, the PCI-based performance model development can be also incorporated in future versions of InfoPaveTM. This study explored and analyzed asphalt pavement sections. However, the process can be also extended to Portland cement concrete test sections. State agencies are encouraged to implement similar analysis and modeling approach for their specific road distress data to validate the findings.
ContributorsWu, Gan (Author) / Kaloush, Kamil (Thesis advisor) / Zhou, Xuesong (Committee member) / Underwood, Benjamin Shane (Committee member) / Arizona State University (Publisher)
Created2015
Description
High performing and sustainable building certification bodies continue to update their requirements, leading to scope modification of certifications, and an increasing number of viable sources of environmental information for building materials. In conjunction, the Architecture, Engineering, and Construction (AEC) industry is seeing increasing demand for such environmental product information. The industry and certifications are moving from using single attribute environmental information about building materials to lifecycle based information to inform their design decisions.
This dissertation seeks to understand the current practices, and then focus on strategies to effectively utilize newer sources of environmental product information in high performance building design. The first phase of research used a survey of 119 U.S.-based AEC practitioners experienced in certified sustainable building projects to understand how the numerous sources of environmental information are currently used in the building design process. The second phase asked two focus groups of experienced AEC professionals to develop a Message Sequence Chart (MSC) that documents the conceptual design process for a recently designed building. Then, the focus group participants integrated a new sustainability requirement for building materials, Environmental Product Declarations (EPDs), into their project, and documented the adjustments to their specific design process in a second, modified MSC highlighting potential drivers for inclusion of EPDs. Finally, the author examines the broader applicability of these drivers through case studies. Specifically, 19 certified high-performance building (HPB) case studies, for reviewing the impact of three different potential drivers on the design team’s approach to considering environmental product information during conceptual design of a HPB, as well as the projects certification level.
LEED certification has changed the design of buildings, and the new information sources for building materials will inform the way the industry selects building materials. Meanwhile, these information sources will need to expand to include a growing number of products, and potentially more data as the industry’s understanding of the impacts of building materials develops. This research expands upon previous research on LEED certification to illustrates that owner engagement and commitment to the HPB process is a critical success factor for the use of environmental product information about building materials.
This dissertation seeks to understand the current practices, and then focus on strategies to effectively utilize newer sources of environmental product information in high performance building design. The first phase of research used a survey of 119 U.S.-based AEC practitioners experienced in certified sustainable building projects to understand how the numerous sources of environmental information are currently used in the building design process. The second phase asked two focus groups of experienced AEC professionals to develop a Message Sequence Chart (MSC) that documents the conceptual design process for a recently designed building. Then, the focus group participants integrated a new sustainability requirement for building materials, Environmental Product Declarations (EPDs), into their project, and documented the adjustments to their specific design process in a second, modified MSC highlighting potential drivers for inclusion of EPDs. Finally, the author examines the broader applicability of these drivers through case studies. Specifically, 19 certified high-performance building (HPB) case studies, for reviewing the impact of three different potential drivers on the design team’s approach to considering environmental product information during conceptual design of a HPB, as well as the projects certification level.
LEED certification has changed the design of buildings, and the new information sources for building materials will inform the way the industry selects building materials. Meanwhile, these information sources will need to expand to include a growing number of products, and potentially more data as the industry’s understanding of the impacts of building materials develops. This research expands upon previous research on LEED certification to illustrates that owner engagement and commitment to the HPB process is a critical success factor for the use of environmental product information about building materials.
ContributorsBurke, Rebekah (Author) / Parrish, Kristen (Thesis advisor) / Gibson, G. Edward (Committee member) / Allenby, Braden (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recently, automation, shared use, and electrification are proposed and viewed as the “three revolutions” in the future transportation sector to significantly relieve traffic congestion, reduce pollutant emissions, and increase transportation system sustainability. Motivated by the three revolutions, this research targets on the passenger-focused scheduled transportation systems, where (1) the public transit systems provide high-quality ridesharing schedules/services and (2) the upcoming optimal activity planning systems offer the best vehicle routing and assignment for household daily scheduled activities.
The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.
In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.
The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.
The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.
In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.
The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.
ContributorsLiu, Jiangtao (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Mirchandani, Pitu (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.
ContributorsMahmoudi, Monirehalsadat (Author) / Zhou, Xuesong (Thesis advisor) / Mirchandani, Pitu B. (Committee member) / Miller, Harvey J. (Committee member) / Pendyala, Ram M. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Infrastructure are increasingly being recognized as too rigid to quickly adapt to a changing climate and a non-stationary future. This rigidness poses risks to and impacts on infrastructure service delivery and public welfare. Adaptivity in infrastructure is critical for managing uncertainties to continue providing services, yet little is known about how infrastructure can be made more agile and flexible towards improved adaptive capacity. A literature review identified approximately fifty examples of novel infrastructure and technologies which support adaptivity through one or more of ten theoretical competencies of adaptive infrastructure. From these examples emerged several infrastructure forms and possible strategies for adaptivity, including smart technologies, combined centralized/decentralized organizational structures, and renewable electricity generation. With institutional and cultural support, such novel structures and systems have the potential to transform infrastructure provision and management.
ContributorsGilrein, Erica (Author) / Chester, Mikhail (Thesis advisor) / Garcia, Margaret (Committee member) / Allenby, Braden (Committee member) / Arizona State University (Publisher)
Created2018
Description
Managed Lanes (MLs) have been increasingly advocated as a way to reduce congestion. This study provides an innovative new tolling strategy for MLs called the travel time refund (TTR). The TTR is an “insurance” that ensures the ML user will arrive to their destination within a specified travel time savings, at an additional fee to the toll. If the user fails to arrive to their destination, the user is refunded the toll amount.
To gauge interest in the TTR, a stated preference survey was developed and distributed throughout the Phoenix-metropolitan area. Over 2,200 responses were gathered with about 805 being completed. Exploratory data analysis of the data included a descriptive analysis regarding individual and household demographic variables, HOV usage and satisfaction levels, HOT usage and interests, and TTR interests. Cross-tabulation analysis is further conducted to examine trends and correlations between variables, if any.
Because most survey takers were in Arizona, the majority (53%) of respondents were unfamiliar with HOT lanes and their practices. This may have had an impact on the interest in the TTR, although it was not apparent when looking at the cross-tabulation between HOT knowledge and TTR interest. The concept of the HOT lane and “paying to travel” itself may have turned people away from the TTR option. Therefore, similar surveys implementing new HOT pricing strategies should be deployed where current HOT practices are already in existence. Moreover, introducing the TTR concept to current HOT users may also receive valuable feedback in its future deployment.
Further analysis will include the weighting of data to account for sample bias, an exploration of the stated preference scenarios to determine what factors were significant in peoples’ choices, and a predictive model of those choices based on demographic information.
To gauge interest in the TTR, a stated preference survey was developed and distributed throughout the Phoenix-metropolitan area. Over 2,200 responses were gathered with about 805 being completed. Exploratory data analysis of the data included a descriptive analysis regarding individual and household demographic variables, HOV usage and satisfaction levels, HOT usage and interests, and TTR interests. Cross-tabulation analysis is further conducted to examine trends and correlations between variables, if any.
Because most survey takers were in Arizona, the majority (53%) of respondents were unfamiliar with HOT lanes and their practices. This may have had an impact on the interest in the TTR, although it was not apparent when looking at the cross-tabulation between HOT knowledge and TTR interest. The concept of the HOT lane and “paying to travel” itself may have turned people away from the TTR option. Therefore, similar surveys implementing new HOT pricing strategies should be deployed where current HOT practices are already in existence. Moreover, introducing the TTR concept to current HOT users may also receive valuable feedback in its future deployment.
Further analysis will include the weighting of data to account for sample bias, an exploration of the stated preference scenarios to determine what factors were significant in peoples’ choices, and a predictive model of those choices based on demographic information.
ContributorsArcher, Melissa (Author) / Lou, Yingyan (Thesis advisor) / Chester, Mikhail (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2015
Description
Modern intelligent transportation systems (ITS) make driving more efficient, easier, and safer. Knowledge of real-time traffic conditions is a critical input for operating ITS. Real-time freeway traffic state estimation approaches have been used to quantify traffic conditions given limited amount of data collected by traffic sensors. Currently, almost all real-time estimation methods have been developed for estimating laterally aggregated traffic conditions in a roadway segment using link-based models which assume homogeneous conditions across multiple lanes. However, with new advances and applications of ITS, knowledge of lane-based traffic conditions is becoming important, where the traffic condition differences among lanes are recognized. In addition, most of the current real-time freeway traffic estimators consider only data from loop detectors. This dissertation develops a bi-level data fusion approach using heterogeneous multi-sensor measurements to estimate real-time lane-based freeway traffic conditions, which integrates a link-level model-based estimator and a lane-level data-driven estimator.
Macroscopic traffic flow models describe the evolution of aggregated traffic characteristics over time and space, which are required by model-based traffic estimation approaches. Since current first-order Lagrangian macroscopic traffic flow model has some unrealistic implicit assumptions (e.g., infinite acceleration), a second-order Lagrangian macroscopic traffic flow model has been developed by incorporating drivers’ anticipation and reaction delay. A multi-sensor extended Kalman filter (MEKF) algorithm has been developed to combine heterogeneous measurements from multiple sources. A MEKF-based traffic estimator, explicitly using the developed second-order traffic flow model and measurements from loop detectors as well as GPS trajectories for given fractions of vehicles, has been proposed which gives real-time link-level traffic estimates in the bi-level estimation system.
The lane-level estimation in the bi-level data fusion system uses the link-level estimates as priors and adopts a data-driven approach to obtain lane-based estimates, where now heterogeneous multi-sensor measurements are combined using parallel spatial-temporal filters.
Experimental analysis shows that the second-order model can more realistically reproduce real world traffic flow patterns (e.g., stop-and-go waves). The MEKF-based link-level estimator exhibits more accurate results than the estimator that uses only a single data source. Evaluation of the lane-level estimator demonstrates that the proposed new bi-level multi-sensor data fusion system can provide very good estimates of real-time lane-based traffic conditions.
Macroscopic traffic flow models describe the evolution of aggregated traffic characteristics over time and space, which are required by model-based traffic estimation approaches. Since current first-order Lagrangian macroscopic traffic flow model has some unrealistic implicit assumptions (e.g., infinite acceleration), a second-order Lagrangian macroscopic traffic flow model has been developed by incorporating drivers’ anticipation and reaction delay. A multi-sensor extended Kalman filter (MEKF) algorithm has been developed to combine heterogeneous measurements from multiple sources. A MEKF-based traffic estimator, explicitly using the developed second-order traffic flow model and measurements from loop detectors as well as GPS trajectories for given fractions of vehicles, has been proposed which gives real-time link-level traffic estimates in the bi-level estimation system.
The lane-level estimation in the bi-level data fusion system uses the link-level estimates as priors and adopts a data-driven approach to obtain lane-based estimates, where now heterogeneous multi-sensor measurements are combined using parallel spatial-temporal filters.
Experimental analysis shows that the second-order model can more realistically reproduce real world traffic flow patterns (e.g., stop-and-go waves). The MEKF-based link-level estimator exhibits more accurate results than the estimator that uses only a single data source. Evaluation of the lane-level estimator demonstrates that the proposed new bi-level multi-sensor data fusion system can provide very good estimates of real-time lane-based traffic conditions.
ContributorsZhou, Zhuoyang (Author) / Mirchandani, Pitu (Thesis advisor) / Askin, Ronald (Committee member) / Runger, George C. (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2015
Description
With high potential for automobiles to cause air pollution and greenhouse gas emissions, there is concern that automobiles accessing or egressing public transportation may cause emissions similar to regular automobile use. Due to limited literature and research that evaluates and discusses environmental impacts from first and last mile portions of transit trips, there is a lack of understanding on this topic. This research aims to comprehensively evaluate the life cycle impacts of first and last mile trips on multimodal transit. A case study of transit and automobile travel in the greater Los Angeles region is evaluated by using a comprehensive life cycle assessment combined with regional household travel survey data to evaluate first-last mile trip impacts in multimodal transit focusing on automobile trips accessing or egressing transit. First and last mile automobile trips were found to increase total multimodal transit trip emissions by 2 to 12 times (most extreme cases were carbon monoxide and volatile organic compounds). High amounts of coal-fired energy generation can cause electric propelled rail trips with automobile access or egress to have similar or more emissions (commonly greenhouse gases, sulfur dioxide, and mono-nitrogen oxides) than competing automobile trips, however, most criteria air pollutants occur remotely. Methods to reduce first-last mile impacts depend on the characteristics of the transit systems and may include promoting first-last mile carpooling, adjusting station parking pricing and availability, and increased emphasis on walking and biking paths in areas with low access-egress trip distances.
ContributorsHoehne, Christopher G (Author) / Chester, Mikhail V (Thesis advisor) / Salon, Deborah (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2016