Browsing by Author "Atkison, Travis L."

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    Evaluating Transportation Network Mobility and Enhancing Traffic Signal Operations Performance Using Probe Data and Connected Vehicle Technology
    (University of Alabama Libraries, 2021) Talukder, MD Abu Sufian; Hainen, Alexander M.; University of Alabama Tuscaloosa
    High-quality, reliable, and robust data is key to better understanding performance and improvement needs for transportation infrastructure. Predominantly, transportation systems performance has been evaluated using infrastructure-based data, which is often limited by high costs, small sample size, and potential inaccuracy. With recent advancements in technology, previously unobtainable large high-fidelity data, such as probe data and connected vehicle (CV) data, can now be utilized to address many challenges related to transportation systems. This dissertation investigates various research and practical oriented applications for such emerging transportation data sources. The first part of this dissertation develops a novel methodology for characterizing mobility of transportation networks. Using probe vehicle travel times, a route-based travel time reliability metric is proposed for assessing and comparing transportation system’s performance from one geographic area to another. The second part of this dissertation uses CV-technology to develop methodology for improving operational efficiency at a signalized intersection. Two innovative traffic signal control algorithms are established to demonstrate real-time delay optimization for both connected and non-connected vehicles. The third part of this dissertation extends the use of CV-technology to facilitate prioritized freight movement in a signalized corridor. An estimated time of arrival (ETA)-based priority logic is developed, and the proposed priority system is deployed along US-82 in Northport and Tuscaloosa, Alabama. Finally, this dissertation explores the application of emerging transportation data collection technologies to characterize and evaluate transportation systems performance. The techniques presented in this dissertation will be helpful to transportation agencies, planners, and practitioners to assess existing performance and need for future transportation infrastructure.
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    Freeway Incident Management: Analyzing the Effectiveness of Freeway Service Patrols on Incident Clearance Times
    (University of Alabama Libraries, 2021) Islam, Naima; Hainen, Alexander M.; University of Alabama Tuscaloosa
    Traffic incidents caused by vehicular crashes, roadway construction, disabled and abandoned vehicles, extreme weather conditions, and planned special events, comprise about half of all traffic congestion. As the duration of traffic incidents increases, it increases the probability of severe congestion, secondary crashes, traveler delay, travel time variability, emissions and fuel consumption, air pollution, economic and social inadequacy, as well as reduces the roadway capacity and the reliability of the whole transportation system. Freeway service patrol (FSP) programs have been considered as an effective Traffic Incident Management (TIM) program for reducing incident duration and thereby minimizing the adverse effects of traffic incidents. The overarching goal of this dissertation is to assess the impact of Alabama Service and Assistant Patrol (ASAP) program based on a unique compiled dataset. The specific objectives are: (1) to merge and match four different datasets, including response data, crash data, traffic volume data and ASAP data; (2) to identify the explanatory variables of incident clearance times with an emphasis on the ASAP coverage area information; (3) to assess duration data using hazard-based duration models with the aim of determining which modeling method best fits the data; and (4) to verify the spatial transferability for the impact of ASAP coverage area. To achieve the research objectives, this dissertation is divided into three parts. The first part describes the Weibull distribution with gamma heterogeneity in identifying the explanatory variables of incident clearance times. The second part compares two advanced econometric modeling methods (random parameters and latent class) in identifying which modeling method best fits the data. The third part employs random parameters modeling method to verify the spatial transferability of the impact of the ASAP program across the state. Ultimately, this dissertation presents a data-driven assessment of the ASAP program in the state. The distinctive contribution of this research is to provide a better understanding of the significant variables that influenced the freeway incident clearance times. The findings of this dissertation are anticipated to assist TIM agencies in formulating and implementing strategic plans to reduce freeway incident clearance times while maximizing the advantages of the ASAP program.
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    A state-based approach to context modeling and computing
    (University of Alabama Libraries, 2019) Yue, Songhui; Smith, Randy; University of Alabama Tuscaloosa
    Context-aware computing is one of the most essential computing paradigms in pervasive computing. However, current context-aware computing is still in lack of good representation models, particularly in modeling proactive behaviors and historical context data. State diagrams have proven to be an effective modeling method for modeling system behaviors. For context-aware computing, explicitly putting forward states of high-level context can be beneficial and intrigue new angles of understanding and modeling activities. In this dissertation, I propose a state-based context model, and based on the model, I introduce Context State Machines (CSM) for simulating state changes of context attribute, situation, and context, which imply important behaviors of related to context. This research develops and demonstrates CSMs for known context-aware problems from the literature including a smart elevator control system. First of all, the smart elevator, as a context- aware application in the literature, is introduced. Secondly, I introduce the implementation of the CSM engine. Thirdly, I describe two context-aware scenarios, and show the model can help automatically capture the contexts and reason the context without the inference from the developers, and it is the first time in literature to apply state-based modeling approach and the CSM engine to a real-world context-aware system. To evaluate the CSM engine as well as the CSM modeling approach, I generate high-level contextual testing data to feed the engine. I surveyed the data quality issues regarding context- aware software and rubrics of the data quality and dimensionality are developed to address the challenges of applying context to context-aware systems. The rubrics are applied in the generation of synthetic data for feeding the CSM engine in this dissertation.