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Machine Learning Techniques for Campus Mobility Analysis

Tambolkar, Pooja
http://rave.ohiolink.edu/etdc/view?acc_num=osu1689869189114362

Abstract Details

2023, Master of Science, Ohio State University, Mechanical Engineering.
Rapid urbanization has led to an increased urban sprawl exerting a lot of pressure on the natural resources, environment, infrastructure, and the dynamics of the urban regions. Effective transportation planning and traffic management are crucial for alleviating the strain on the road networks, reducing congestion, and thus promoting sustainable mobility in cities. Advancements in big data have revolutionized traffic data by facilitating real time monitoring and data driven solutions. Various data sources have the potential to provide useful traffic information. This thesis focuses on analyzing different existing data sources on the OSU campus and develop an end-to-end approach to handle this data. The thesis explores two data sources in particular - surveillance cameras and Wi-Fi hotspots to derive relevant and usable data for traffic modelling. Object detection and tracking techniques have been implemented to extract the total counts of pedestrians and vehicles moving across campus at peak hours. A reinforcement learning approach has been developed to model the path taken by pedestrians using the Wi-Fi data. Simulation in Urban Mobility (SUMO) provides a realistic environment for obtaining the optimal path for the pedestrians. By integrating diverse data sources and employing innovative methodologies, the workings of this thesis and outcomes thereof aids in traffic management and offers valuable insights for creating smarter, more efficient, and resilient cities.
Shawn Midlam-Mohler (Advisor)
Punit Tulpule (Committee Member)
Sandra Metzler (Committee Member)
158 p.
Engineering
Traffic data, Object detection and tracking, Reinforcement learning, SUMO

Recommended Citations

Citations

  • Tambolkar, P. (2023). Machine Learning Techniques for Campus Mobility Analysis [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1689869189114362

    APA Style (7th edition)

  • Tambolkar, Pooja. Machine Learning Techniques for Campus Mobility Analysis. 2023. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1689869189114362.

    MLA Style (8th edition)

  • Tambolkar, Pooja. "Machine Learning Techniques for Campus Mobility Analysis." Master's thesis, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu1689869189114362

    Chicago Manual of Style (17th edition)

osu1689869189114362
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© 2023, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.