Proceedings of the International Conference on Smart Innovations in Electrical Engineering (ICSIEE 2025)

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Sustainable Parking System Prediction with Dynamic Pricing and Slot Forecasting for Smart Cities

Authors
G. M. Kavyashree1, *, K. S. Nandini Prasad2, R. S. Veena3, Yashaswi T. Hegde4, Vansh Surana4, Vaishnavi Kamath4
1Assistant Professor, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India
2Professor, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India
3Associate Professor, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India
4Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India
*Corresponding author. Email: kavyashree-ise@dsatm.edu.in
Corresponding Author
G. M. Kavyashree
Available Online 22 October 2025.
DOI
10.2991/978-94-6463-870-7_12How to use a DOI?
Keywords
Dynamic pricing; Polynomial regression; Prediction; GPS; Mango DB; React JS
Abstract

The slot forecasting system dynamic pricing model is applied for automobile parking supervision for trendy cities. Real-time data gathered from five Bengaluru city geographic area. Method: Polynomial Regression algorithm is designed using python language. The aggressive constraints are considered for training model – Geological distance, traffic data, peak hour vs non-peak hour, time of day, weekend vs weekday, slots available in parking lot, when user needs to take a slot in the given location. Based on the above-stated parameters user can take up chosen parking slot by forecasting suitable price. The above-stated was designed and provided GUI interface to users by using NodeJS. Comparison of actual price with dynamic price is performed and its outcome is observed that prediction price is very accurate and nearly match with actual price and it is verified with various time slots (during peak or normal hours to save cost and time of user. Uniqueness: data set collection- the data is gathered from five various locations of Bengaluru for both peak time and normal time, weekdays and weekends. Techniques Integrated- Technologies known as Mango DB, React JS, and GPS. To develop a model with collected values of various locations and predict dynamic pricing by using linear polynomial regression. Advanced Booking- the proposed allows the operator to take a parking space in advance with GPS based location. Fee can be made when user substantially occupy the slot. The allotted slot was simplified in file and one reduces total computation. The entire recommended idea reduces operator-waiting time for parking and energy consumption. The proposed solution offers a sustainable parking solution for smart cities.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Smart Innovations in Electrical Engineering (ICSIEE 2025)
Series
Advances in Engineering Research
Publication Date
22 October 2025
ISBN
978-94-6463-870-7
ISSN
2352-5401
DOI
10.2991/978-94-6463-870-7_12How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - G. M. Kavyashree
AU  - K. S. Nandini Prasad
AU  - R. S. Veena
AU  - Yashaswi T. Hegde
AU  - Vansh Surana
AU  - Vaishnavi Kamath
PY  - 2025
DA  - 2025/10/22
TI  - Sustainable Parking System Prediction with Dynamic Pricing and Slot Forecasting for Smart Cities
BT  - Proceedings of the International Conference on Smart Innovations in Electrical Engineering (ICSIEE 2025)
PB  - Atlantis Press
SP  - 111
EP  - 122
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-870-7_12
DO  - 10.2991/978-94-6463-870-7_12
ID  - Kavyashree2025
ER  -