Matching Items (10)
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- All Subjects: Transportation
- Creators: Pendyala, Ram
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
Transit-oriented developments (TODs) are a promising strategy to increase public transit use and, as a result, reduce personal car travel. By using TOD infill to increase urban population density and encourage transportation mode-shifting, the potential exists to reduce life-cycle per capita energy use and environmental impacts of the interdependent infrastructure systems. This project specifically examined the Gold Line of light rail and Orange Line of bus rapid transit in Los Angeles, CA.
ContributorsNahlik, Matthew John (Author) / Chester, Mikhail (Thesis director) / Pendyala, Ram (Committee member) / Pincetl, Stephanie (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor)
Created2013-05
Description
Arizona's transportation infrastructure is in need of an update. The American Society of Civil Engineers (ASCE) State Infrastructure 2017 Report Card scores Arizona's roads at a D+ and Arizona's bridges at a B. These grades are indicative that the serviceability levels of the roads and bridges are less than adequate. These grades may seem tolerable in light of a national bridge C+ grade and a national road D grade, but the real problem lies in Arizona's existing funding gap that is in danger of exponentially increasing in the future. With an influx of vehicles on Arizona's roads and bridges, the cost of building, repairing, and maintaining them will grow and cause a problematic funding shortage. This report explores the current state of Arizona's roads and bridges as well as the policy and funding sources behind them, using statistics from the ASCE infrastructure report card and the Federal Highway Administration. Additionally, it discusses how regular, preventative maintenance for transportation infrastructure is the economically responsible choice for the state because it decreases delays and fuel expenses, prevents possible catastrophes, and increases human safety. To prioritize preventative transportation infrastructure maintenance, the common mentality that allows it to be sidelined for more newsworthy projects needs to be changed. Along with gaining preventative maintenance revenues through increasing vehicular taxes and fees, encouraging transportation policymakers and politicians to make economic decisions in favor of maintenance rather than waiting until failure is a reliable way to encourage regular, preventative maintenance.
ContributorsBurdett, Courtney (Author) / Hjelmstad, Keith (Thesis director) / Pendyala, Ram (Committee member) / Civil, Environmental and Sustainable Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Productivity in the construction industry is an essential measure of production efficiency and economic progress, quantified by craft laborers' time spent directly adding value to a project. In order to better understand craft labor productivity as an aspect of lean construction, an activity analysis was conducted at the Arizona State University Palo Verde Main engineering dormitory construction site in December of 2016. The objective of this analysis on craft labor productivity in construction projects was to gather data regarding the efficiency of craft labor workers, make conclusions about the effects of time of day and other site-specific factors on labor productivity, as well as suggest improvements to implement in the construction process. Analysis suggests that supporting tasks, such as traveling or materials handling, constitute the majority of craft labors' efforts on the job site with the highest percentages occurring at the beginning and end of the work day. Direct work and delays were approximately equal at about 20% each hour with the highest peak occurring at lunchtime between 10:00 am and 11:00 am. The top suggestion to improve construction productivity would be to perform an extensive site utilization analysis due to the confined nature of this job site. Despite the limitations of an activity analysis to provide a complete prospective of all the factors that can affect craft labor productivity as well as the small number of days of data acquisition, this analysis provides a basic overview of the productivity at the Palo Verde Main construction site. Through this research, construction managers can more effectively generate site plans and schedules to increase labor productivity.
ContributorsFord, Emily Lucile (Author) / Grau, David (Thesis director) / Chong, Oswald (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Recurring incidents between pedestrians, bicycles, and vehicles at the intersection of Rural Road and Spence Avenue led to a team of students conducting their own investigation into the current conditions and analyzing a handful of alternatives. An extension of an industry-standard technique was used to build a control case which alternatives would be compared to. Four alternatives were identified, and the two that could be modeled in simulation software were both found to be technically feasible in the preliminary analysis.
ContributorsFellows, Christopher Lee (Author) / Lou, Yingyan (Thesis director) / Zhou, Xuesong (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The emerging multimodal mobility as a service (MaaS) and connected and automated mobility (CAM) are expected to improve individual travel experience and entire transportation system performance in various aspects, such as convenience, safety, and reliability. There have been extensive efforts in the literature devoted to enhancing existing and developing new methodologies and tools to investigate the impacts and potentials of CAM systems. Due to the hierarchical nature of CAM systems and associated intrinsic correlated human factors and physical infrastructures from various resolutions, simply considering components across different levels into a single model may be practically infeasible and computationally prohibitive in operation and decision stages. One of the greatest challenges in existing studies is to construct a theoretically sound and computationally efficient architecture such that CAM system modeling can be performed in an inherently consistent cross-resolution manner. This research aims to contribute to the modeling of CAM systems on layered transportation networks, with a special focus on the following three aspects: (1) layered CAM system architecture with a tight network and modeling consistency, in which different levels of tasks can be efficiently performed at dedicated layers; (2) cross-resolution traffic state estimation in CAM systems using heterogeneous observations; and (3) integrated city logistics operation optimization in CAM for improving system performance.
ContributorsLu, Jiawei (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Xue, Guoliang (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation investigates congestion mitigation during the ingress of a planned special event (PSE). PSEs would impact the regular operation of the transportation system within certain time periods due to increased travel demand or reduced capacities on certain road segments. For individual attendees, cruising for parking during a PSE could be a struggle given the severe congestion and scarcity of parking spaces in the network. With the development of smartphones-based ridesharing services such as Uber/Lyft, more and more attendees are turning to ridesharing rather than driving by themselves. This study explores congestion mitigation during a planned special event considering parking, ridesharing and network configuration from both attendees and planner’s perspectives.
Parking availability (occupancy of parking facility) information is the fundamental building block for both travelers and planners to make parking-related decisions. It is highly valued by travelers and is one of the most important inputs to many parking models. This dissertation proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility.
From an attendee’s perspective, the probability of finding parking at a particular parking facility is more treasured than occupancy information for parking search. However, it is hard to estimate parking probabilities even with accurate occupancy data in a dynamic environment. In the second part of this dissertation, taking one step further, the idea of introducing learning algorithms into parking guidance and information systems that employ a central server is investigated, in order to provide estimated optimal parking searching strategies to travelers. With the help of the Markov Decision Process (MDP), the parking searching process on a network with uncertain parking availabilities can be modeled and analyzed.
Finally, from a planner’s perspective, a bi-level model is proposed to generate a comprehensive PSE traffic management plan considering parking, ridesharing and route recommendations at the same time. The upper level is an optimization model aiming to minimize total travel time experienced by travelers. In the lower level, a link transmission model incorporating parking and ridesharing is used to evaluate decisions from and provide feedback to the upper level. A congestion relief algorithm is proposed and tested on a real-world network.
Parking availability (occupancy of parking facility) information is the fundamental building block for both travelers and planners to make parking-related decisions. It is highly valued by travelers and is one of the most important inputs to many parking models. This dissertation proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility.
From an attendee’s perspective, the probability of finding parking at a particular parking facility is more treasured than occupancy information for parking search. However, it is hard to estimate parking probabilities even with accurate occupancy data in a dynamic environment. In the second part of this dissertation, taking one step further, the idea of introducing learning algorithms into parking guidance and information systems that employ a central server is investigated, in order to provide estimated optimal parking searching strategies to travelers. With the help of the Markov Decision Process (MDP), the parking searching process on a network with uncertain parking availabilities can be modeled and analyzed.
Finally, from a planner’s perspective, a bi-level model is proposed to generate a comprehensive PSE traffic management plan considering parking, ridesharing and route recommendations at the same time. The upper level is an optimization model aiming to minimize total travel time experienced by travelers. In the lower level, a link transmission model incorporating parking and ridesharing is used to evaluate decisions from and provide feedback to the upper level. A congestion relief algorithm is proposed and tested on a real-world network.
ContributorsXiao, Jun, Ph.D (Author) / Lou, Yingyan (Thesis advisor) / Pendyala, Ram (Committee member) / Zhou, Xuesong (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2019
Description
Motorcycle fatalities have been increasing at a faster rate than the number of motorcycles being registered in the United States. There is limited analysis on the causes of fatal motorcycle crashes, specifically regarding different demographics, certain driver behavior, and various crash characteristics. It is important to be aware of how these factors relate to each other during a fatal motorcycle crash. This analysis focuses on these factors and explores potential steps to decrease motorcycle fatality rates using research and data from the Fatality Analysis Reporting System (FARS) from the National Highway Traffic Safety Administration (NHTSA), and data from the National Household Travel Survey (NHTS). Based on this data, there are noticeable trends between different genders and age groups. According to the analysis, males have a higher fatality rate than females, and their fatal crashes tend to involve multiple driver infractions such as drinking, speeding, not wearing a helmet, and driving without a license. Similarly, younger drivers have a higher fatality rate than older drivers, and their fatal crashes tend to involve multiple driver infractions. Although older drivers involved in fatal crashes usually drive more cautiously, they tend to be involved in single-vehicle crashes more often than younger drivers. Moving forward, implementing certain training programs directed towards particular demographics has the potential to decrease motorcycle rider fatalities.
ContributorsMoran, Sarah Elizabeth (Co-author) / Santilli, Amy (Co-author) / Pendyala, Ram (Thesis director) / Khoeini, Sara (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Approximately 1% of the total working population within the United States bikes as their primary mode of commute. Due to recent increased in bicycle facilities as well as a focus on alternative modes of transport, understanding the motivations and type of people who bike to work is important in order to encourage new users.
In this project, a literature review was completed as well as data analysis of the National Household Travel Survey (NHTS) in order to find specific populations to target. Using these target populations, it is suggested that advertising and workplace encouragement occur to persuade more people to bike to work. Through data analysis it was found that the most impactful variables were the region of the country, gender, population density, and commute distance. Bicycle commuters statistically had fewer vehicles in their households and drove less miles annually.
There were five main target groups found through this analysis; people who bike for other reasons besides work and live in a city with more than 4,000 people per square mile, young professionals between 19-39, women in regions with separated bicycle facilities, those with low vehicle availability, and environmentally conscious individuals. Working to target these groups through advertising campaigns to encourage new users, as well as increasing and improving bicycle facilities, will help create more new bicyclists.
In this project, a literature review was completed as well as data analysis of the National Household Travel Survey (NHTS) in order to find specific populations to target. Using these target populations, it is suggested that advertising and workplace encouragement occur to persuade more people to bike to work. Through data analysis it was found that the most impactful variables were the region of the country, gender, population density, and commute distance. Bicycle commuters statistically had fewer vehicles in their households and drove less miles annually.
There were five main target groups found through this analysis; people who bike for other reasons besides work and live in a city with more than 4,000 people per square mile, young professionals between 19-39, women in regions with separated bicycle facilities, those with low vehicle availability, and environmentally conscious individuals. Working to target these groups through advertising campaigns to encourage new users, as well as increasing and improving bicycle facilities, will help create more new bicyclists.
ContributorsImbus, Eileen Elizabeth (Author) / Khoeini, Sara (Thesis director) / Pendyala, Ram (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05