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Title	Resolving Community Parking Issues: An Iot Enabled Statistical And Deep Learning Approach For Enhanced Urban Parking Management
ID_Doc	46059
Authors	Hassan T.U.; Khurram A.B.; Iqbal S.; Malik A.W.; Fraz M.M.
Year	2024
Published	2024 International Conference on Frontiers of Information Technology, FIT 2024
DOI	http://dx.doi.org/10.1109/FIT63703.2024.10838440
Abstract	Parking challenges significantly impact urban communities, costing drivers time, fuel, and frustration while contributing to carbon dioxide emissions and congestion. Efficient parking management offers profit opportunities. With the rise of software-defined vehicles in smart cities, the Internet of Things (IoT) and Internet of Vehicles (IoV) are crucial for city navigation. Existing device-based solutions fail to assess financial impacts and scalability. We propose 1 an IoT-enabled smart parking management framework using a real-time negotiation system. It integrates software-defined vehicles and Parking Side Units (PSUs) to enhance decision-making. Unlike previous studies, our model focuses both on maximizing revenue generated by parking lots, while also minimizing congestion of traffic on the roads. Moreover, we implemented a dynamic pricing policy scheme which imposes more fees to drivers during peak hours for parking. The peak hours for a particular day are determined in advance by forecasting the revenue for a particular day in advance by Seasonal Auto-regressive moving average(SARMIA) model. SARIMA model is compared with other machine learning techniques for our revenue forecasting and peak hour detection problem, and we chose it in our model as it performed the best. Our algorithm results in much better performance across different metrics such as number of vehicles roaming in search of a parking space at a time, request acceptance duration, remaining parking slots etc. © 2024 IEEE.
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