Matching Items (62)
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
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
Priced Managed Lanes (MLs) have been increasingly advocated as one of the effective ways to mitigating congestion in recent years. This study explores a new and innovative pricing strategy for MLs called Travel Time Refund (TTR). The proposed TTR provides an additional option to paying drivers that insures their travel time by issuing a refund to the toll cost if they do not reach their destination within specified travel times due to accidents or other unforeseen circumstances. Perceived benefits of TTR include raised public acceptance towards priced MLs, utilization increase of HOV/HOT lanes, overall congestion mitigation, and additional funding for relevant transportation agencies. To gauge travelers’ interests of TTR and to analyse its possible impacts, a stated preference (SP) survey was performed. An exploratory and statistical analysis of the survey responses revealed negative interest towards HOT and TTR option in accordance with common wisdom and previous studies. However, it is found that travelers are less negative about TTR than HOT alone; supporting the idea, that TTR could make HOT facilities more appealing. The impact of travel time reliability and latent variables representing psychological constructs on travelers’ choices in response to this new pricing strategy was also analysed. The results indicate that along with travel time and reliability, the decision maker’s attitudes and the level of comprehension of the concept of HOT and TTR play a significant role in their choice making. While the refund option may be theoretically and analytically feasible, the practical implementation issues cannot be ignored. This study also provides a discussion of the potential implementation considerations that include information provision to connected and non-connected vehicles, distinction between toll-only and refund customers, measurement of actual travel time, refund calculation and processing and safety and human factors issues. As the market availability of Connected and Automated Vehicles (CAVs) is prognosticated by 2020, the potential impact of such technologies on effective demand management, especially on MLs is worth investigating. Simulation analysis was performed to evaluate the system performance of a hypothetical road network at varying market penetration of CAVs. The results indicate that Connected Vehicles (CVs) could potentially encourage and enhance the use of MLs.
ContributorsVadlamani, Sravani (Author) / Lou, Yingyan (Thesis advisor) / Pendyala, Ram (Committee member) / Zhou, Xuesong (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2018
Description
As a student and then an Undergraduate Teaching Assistant (UGTA), I have had the opportunity to personally witness the learning process of both myself and approximately 75 additional incoming Civil Engineering students taking the Mechanics courses after me. While watching the student learning process as an UGTA, I realized that there were consistent points of confusion amongst the students that the teaching staff could not efficiently communicate with the electronic or physical classroom materials available. As a physical learner, I am able to learn more comprehensively if I have a physical model to manipulate, and often found myself in the position of wanting to be able to physically represent and manipulate the systems being studied in class.
ContributorsCamillucci, Allyson Nicole (Co-author, Co-author) / Hjelmstad, Keith (Thesis director) / Chatziefstratiou, Efthalia (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The purpose of this project was to design a new railroad crossing for pedestrians and bicyclists in mid-block or urban areas. In order to develop a successful design, the needs of the railroad, the end-users, and the city governments were researched and converted into measurable engineering requirements. For the railroad companies, the most important need was a crossing that presents an effective barrier to users while a train is in the area. For bicyclists and pedestrians (the end-users), the most important need was for the crossing to be both reliable and easily accessible. For the city governments, the most important need was a crossing that is inexpensive yet sturdy. The approach to this project was similar to the approach used in many engineering design processes. First is the Introduction, which provides an overview of the issue and presents the full problem statement. Next is the Research of Prior Art, which details the past solutions to railroad crossings as well as the 3 E's of railroad crossing safety. After this, the customer needs are discussed in the Needs to Requirements section and the process of converting these into measurable engineering requirements is shown. Next, various conceptual design options are shown in the Conceptual Design section and a final conceptual design is chosen based on adherence to the stated requirements. This final conceptual design is then taken into the preliminary design phase and refined until it becomes the final preliminary design. After the Final Preliminary Design Description, the Project Conclusions and Recommendations are presented. Due to time and monetary constraints, this project ends after the preliminary design stage. Despite this, the conclusion of this project is that the final design presented here will be successful if additional resources are obtained to move it forward into the detailed design phase. For now, this project has come to a halt due to UP's reluctance to allow any additional railroad crossings in the Phoenix and Tempe, Arizona areas. It is recommended that city officials and bicyclist/pedestrian action groups continue talks with UP until they agree to allow additional crossings to be built that are geared towards non-motorized users.
ContributorsJones, Mitchell Drexel (Author) / Kuby, Michael (Thesis director) / Lou, Yingyan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
From 2007 to 2017, the state of California experienced two major droughts that required significant governmental action to decrease urban water demand. The purpose of this project is to isolate and explore the effects of these policy changes on water use during and after these droughts, and to see how these policies interact with hydroclimatic variability. As explanatory variables in multiple linear regression (MLR) models, water use policies were found to be significant at both the zip code and city levels. Policies that specifically target behavioral changes were significant mathematical drivers of water use in city-level models. Policy data was aggregated into a timeline and coded based on categories including user type, whether the policy was voluntary or mandatory, the targeted water use type, and whether the change in question concerns active or passive conservation. The analyzed policies include but are not limited to state drought declarations, regulatory municipal ordinances, and incentive programs for household appliances. Spatial averages of available hydroclimatic data have been computed and validated using inverse distance weighting methods. The data was aggregated at the zip code level to be comparable to the available water use data for use in MLR models. Factors already known to affect water use, such as temperature, precipitation, income, and water stress, were brought into the MLR models as explanatory variables. After controlling for these factors, the timeline policies were brought into the model as coded variables to test their effect on water demand during the years 2000-2017. Clearly identifying which policy traits are effective will inform future policymaking in cities aiming to conserve water. The findings suggest that drought-related policies impact per capita urban water use. The results of the city level MLR models indicate that implementation of mandatory policies that target water use behaviors effectively reduce water use. Temperature, income, unemployment, and the WaSSI were also observed to be mathematical drivers of water use. Interaction effects between policies and the WaSSI were statistically significant at both model scales.
ContributorsHjelmstad, Annika Margaret (Author) / Garcia, Margaret (Thesis director) / Larson, Kelli (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
With a rapidly decreasing amount of resources for construction, wood and bamboo have been suggested as renewable materials for increased use in the future to attain sustainability. Through a literature review, bamboo and wood growth, manufacturing and structural attributes were compared and then scored in a weighted matrix to determine the option that shows the higher rate of sustainability. In regards to the growth phase, which includes water usage, land usage, growth time, bamboo and wood showed similar characteristics overall, with wood scoring 1.11% higher than bamboo. Manufacturing, which captures the extraction and milling processes, is experiencing use of wood at levels four times those of bamboo, as bamboo production has not reached the efficiency of wood within the United States. Structural use proved to display bamboo’s power, as it scored 30% higher than wood. Overall, bamboo received a score 15% greater than that of wood, identifying this fast growing plant as the comparatively more sustainable construction material.
ContributorsThies, Jett Martin (Author) / Ward, Kristen (Thesis director) / Halden, Rolf (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Civil, Environmental and Sustainable Eng Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Current practice and a new technology for mitigating fugitive dust on construction sites are compared on the basis of economic, environmental and social impacts for this assessment. Fugitive dust can have serious health impacts, such as repertory illnesses and valley fever, on affected persons and is regulated by the Environmental Protection Agency and enforced by state and local agencies. Current practice consists of either relatively continuous application of potable water, a valuable resource, or application of expensive polymers, however, water application is considered the best available technology (BAT). The new technology, developed by the Center of Bio-medicated and Bio-inspired Geotechnics at Arizona State University, consists of application of Enzyme-Induced Carbonate Precipitate (EICP) to create an erosion-resistant crust. This crust is considered a "one and done" solution, until it is disturbed, however will last longer and stay more effective than quickly evaporating water. Future work will need to include how much disturbance is required to disturb the crust until ineffective towards mitigating fugitive dust. Results of the comparison show that a single EICP treatment produces 37 times less pollutants, uses 41 times less water and is 1.14 times cheaper than using water treatment to mitigate fugitive dust on a 7 acre site for 2 weeks (14 days). 14 days is the threshold at where EICP treatment becomes less expensive than water application for the purpose of mitigating fugitive dust. The EICP treatment benefits include lowering global warming inducing emissions, providing better air quality, becoming more cost effective, staying constantly effective to mitigate fugitive dust, and saving potable water.
ContributorsFabian, Aaron Jacob (Author) / Fox, Peter (Thesis director) / Kavazanjian, Edward (Thesis director) / Woolley, Miriam (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Expansive soils in the United States cause extensive damage to roadways, buildings, and various structures. There are several treatment or methods of mitigation for these expansive soils. These treatments can be physical or chemical treatments that serve to provide more suitable building qualities for foundations and roadways alike. The main issue with expansive soils, is the volumetric variations, which are known as swelling and consolidation. These behaviors of the soil are usually stabilized through the use of lime solution, Portland Cement Concrete, and a newer technology in chemical treatments, sodium silicate solutions. Although the various chemical treatments show benefits in certain areas, the most beneficial method for stabilization comes from the combination of the chemical treatments. Lime and Portland cement concrete are the most effective in terms of increasing compressive strength and reduction of swell potential. However, with the introduction of silicate into either treatment, the efficacy of the treatments increases by a large amount lending itself more as an additive for the former processes. Sodium silicate solution does not lend itself to effectively increase the compressive strength of expansive soils. The sodium silicate solution treatment needs extensive research and development to further improve the process. A proposed experiment plan has been recommended to develop trends of pH and temperature and its influence on the effectiveness of the treatment. Nonetheless, due to the high energy consumption of the other processes, sodium silicate solution may be a proper step in decreases the carbon footprint, that is currently being created by the synthesis of Portland Cement Concrete and lime.
ContributorsMeza, Magdaleno (Author) / Zapata, Claudia (Thesis director) / Kavazanjian, Edward (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
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