PARK-EZE: An IoT based Smart Parking System using DLSTM Prediction
Abstract
In response to escalating urbanization and vehicular
congestion, our project PARKEZE a Smart Parking System
introduces an innovative solution integrating IoT and DLSTM
(Deep Long Short Term Memory) technologies. By employing IoT
sensors for realtime data collection and DLSTM for predictive
analysis, the system PARK-EZE aims to revolutionize parking
management. This exploration delves into PARK-EZE’s design,
implementation, and transformative potential, elucidating its role
in creating smarter, more sustainable urban spaces. Through
comprehensive data analytics, PARKEZE seeks to alleviate
congestion and inefficient parking allocation, fostering efficient
urban mobility patterns. PARK-EZE represents a paradigm shift
towards efficiency, accessibility, and environmental stewardship
in urban environments.
Keywords:
PARK-EZE, Prediction, Real-time parking data
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