Smart Parking System with Dynamic Pricing Model

G Raghuraman; S Kavitha; Nitish Gokulakrishnan; Santosh Radhakrishnan; Pranav Ramakrishnan

doi:10.17485/IJST/v16i18.2489

Article

Smart Parking System with Dynamic Pricing Model

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i18.2489

Year: 2023, Volume: 16, Issue: 18, Pages: 1332-1339

Original Article

Smart Parking System with Dynamic Pricing Model

G Raghuraman^1*, S Kavitha^1*, Nitish Gokulakrishnan², Santosh Radhakrishnan², Pranav Ramakrishnan

¹Associate Professor, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India
²UG Student, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India

*Corresponding Author
Email: [email protected]
[email protected]

Received Date:27 December 2022, Accepted Date:30 March 2023, Published Date:05 May 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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

Keywords: Dynamic pricing; Polynomial regression; Prediction; Parking slot; Time of day; Occupancy

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Copyright

© 2023 Raghuraman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)