Smart Parking System with Dynamic Pricing Model

Objectives: A Smart Parking System with Dynamic Pricing Model is proposed and developed for vehicle parking management using real time data collected from five popular hotspots in the city of Chennai. Methods: The model polynomial regression algorithm Python language. vital parameters considered training a are: traffic data, geological distance, day, peak hour or not, weekend not especially, number slots available lot at user wishes to book slot that location. Using these parameters, predicts an approximate price occupying selected slot. For ease use dynamic pricing as GUI NodeJS end users. Findings: comparison between static (actual) done results it observed predicted significantly close actual price. In addition, availability can be checked ahead different (normal, peak) save amount. Novelty: (i). Dataset collection — which are locations Chennai both weekdays weekends, normal time, approximately 10000 samples. (ii). Proposed techniques Different technologies (ReactJS, MongoDB, GPS) linear technique applied creation values existing live locations. (iii). booked based on preference GPS payment when physically slot, reflected database overall count reduced by one. overall, system reduces productive fuel consumption waiting time. Keywords: pricing; Polynomial regression; Prediction; slot; Time day; Occupancy