Graduate Thesis Or Dissertation

 

Incidental Sensor Networks for Human Mobility Detection Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/jm214w76j

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  • Transportation systems need to get better by moving ever more people while consuming ever fewer resources. To build better transportation systems, planners need an accurate understanding of how people exercise mobility and tools to apply that understanding to the transportation system. Such an understanding can come through the development of existing sources of implicit and explicit mobility data and tools suitable for planners to apply the results. Transportation organizations may struggle to produce the necessary tools internally, leaving external bodies, both public and private, to pursue development. In this research, three frameworks were developed that employ data already being collected to facilitate analysis of human mobility and improve the utilization of that analysis in its application to transportation systems. First, two new metrics as potential objectives for finding solutions to a type of Urban Transit Routing Problem (UTRP) are proposed and applied. The metrics assess the social experience of transit users and can be used to produce transit routes that may improve a rider’s transit experience. In the presented case study, the improved routes increased the social metrics by 242% and 119% compared to current baseline routes. Next, the UTRP construct is again adapted to produce solutions that allow transit planners to balance the need to reduce the susceptibility of disease transmission in their transit vehicles while maintaining transit network utility for potential riders. In the presented case study, a Pareto front is produced of solutions from which a transit planner could choose what best suits their community’s needs. Both the UTRP-type frameworks use a novel source of mobility data to simulate the solutions’ impacts in a real-world environment. Finally, further exploring new uses of mobility data, an anomaly detection framework that leverages redundancies in sampling populations that will arise as additional sources of data are identified is developed. The anomaly detection framework provides increased quality assurance to planners as new sources of data are developed. In the presented case study, a previously unacknowledged anomaly in traffic data is successfully identified. The three frameworks demonstrate the potential of advancing the use of additional data in transportation planning. Future work requires additional resources to support data-driven transportation planning and adapting proven practices from elsewhere to the specific US transportation needs.
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