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- All Subjects: Motor vehicles
- Creators: Mirchandani, Pitu B.
- Creators: Robinson Kurpius, Sharon E.
Description
This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
ContributorsPeng, Dening (Author) / Mirchandani, Pitu B. (Thesis advisor) / Sefair, Jorge (Committee member) / Wu, Teresa (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2017
Description
Decades of research and empirical studies support the belief that traumatic life events lead to a multitude of negative outcomes (Tedeschi & Calhoun, 1996), however, new research suggests that some survivors of trauma experience significant psychological growth, known as posttraumatic growth (PTG) (Tedeschi, Park, & Calhoun, 1998). The current study focused on the trauma of a traumatic brain injury (TBI) and its relation to the development of PTG. A TBI is both a psychological trauma and a type of acquired brain injury that occurs when physical injury causes damage to the brain (National Institutes of Health [NIH], 2013). Empirical studies examining TBIs and PTG are minimal. The current study focused on survivors who have sustained a TBI from a motor vehicle accident to help control for contextual factors of the injury that are known to affect outcomes. The aim of this study was to elucidate the physical, sociodemographic, contextual, and psychological factors that helped predict the development of PTG among a population of TBI survivors. In addition, another aim of this study was to gain a better understanding of the relationship between PTG and posttraumatic stress disorder (PTSD) symptomatology. Cross-sectional data from self-identified TBI survivors of motor vehicle accidents (n = 155) were used to construct a model of prediction of PTG. Preliminary analyses revealed a reliability issue with the measure that assessed participants’ personality, and these variables were not used in planned analyses. Results revealed that the majority of participants were female, Caucasian, highly educated, and unemployed. Overall, the sample indicated significant injury severity, disability, and lower than average mental and physical functioning. The final model accounted for approximately 15% of the variance in PTG and significant predictors included: gender, time since injury, and the interaction between PTSD symptoms and time since injury. The findings of this research can help inform treatment programs and rehabilitation services as well as funding that can aim to improve outcomes from survivors of TBI. Study limitations included the use of cross-sectional data, a homogenous and unrepresentative sample of TBI survivors, recruitment concerns, and low reliability observed in one of the integral measures of the study.
ContributorsGildar, Natalie J (Author) / Bernstein, Bianca L (Thesis advisor) / Lavoie, Michael (Committee member) / Robinson Kurpius, Sharon E. (Committee member) / Arizona State University (Publisher)
Created2016