CFP last date
20 January 2025
Reseach Article

Smart Urban Parking Solution in Sri Lanka

by M.D.S.M. Antany, M.R.M. Aadil, L.V. Ferreira, Priyankara A.D.D., M.G.N.M. Pemadasa, Thamali Dassanayake
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 38
Year of Publication: 2021
Authors: M.D.S.M. Antany, M.R.M. Aadil, L.V. Ferreira, Priyankara A.D.D., M.G.N.M. Pemadasa, Thamali Dassanayake
10.5120/ijca2021921782

M.D.S.M. Antany, M.R.M. Aadil, L.V. Ferreira, Priyankara A.D.D., M.G.N.M. Pemadasa, Thamali Dassanayake . Smart Urban Parking Solution in Sri Lanka. International Journal of Computer Applications. 183, 38 ( Nov 2021), 26-32. DOI=10.5120/ijca2021921782

@article{ 10.5120/ijca2021921782,
author = { M.D.S.M. Antany, M.R.M. Aadil, L.V. Ferreira, Priyankara A.D.D., M.G.N.M. Pemadasa, Thamali Dassanayake },
title = { Smart Urban Parking Solution in Sri Lanka },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2021 },
volume = { 183 },
number = { 38 },
month = { Nov },
year = { 2021 },
issn = { 0975-8887 },
pages = { 26-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number38/32180-2021921782/ },
doi = { 10.5120/ijca2021921782 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:01.501054+05:30
%A M.D.S.M. Antany
%A M.R.M. Aadil
%A L.V. Ferreira
%A Priyankara A.D.D.
%A M.G.N.M. Pemadasa
%A Thamali Dassanayake
%T Smart Urban Parking Solution in Sri Lanka
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 38
%P 26-32
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A sustainable, feasible, and affordable mobile application solution to automate the vehicle parking of a driver with features like finding available parking slots, finding the nearest parking area, and navigating to the available parking slot inside a parking area would be beneficial for the drivers since it would save valuable time and money and for the owners, owners would allow utilizing their limited spaces in urban areas to the maximum efficiency increasing their profit rate and saving environmental pollution by limiting the toxic gases produced to the environment. 'Pay as You Park' Smart Parking Solution empowers its users to use above mentioned advantages with its mobile application for drivers and, a web application for car park owners. The main uniqueness is that instead of traditional hourly charged parking fees, introduced system charges only for the exact time been parked. This system uses on-premises CCTV cameras to identify the availability of parking spaces and it allows anyone with a space to park a vehicle to register with the system and contribute the space to the system while earning extra money once the system validated proper conditions of a parking area. With the current growth of the number of vehicles on Sri Lankan roads, traffic has become a huge problem. 'Pay as You Park' Smart Parking Solution saves the time of the user since it enables the user to skip the time-intensive process of finding the perfect parking slot for their vehicle with the use of a Machine Learning-based algorithm which is used to find the suitable parking slot for a particular vehicle.

References
  1. Bhavani, D. S., & Ghalib, M. R. “Internet of Things Based Smart Car Parking System Using K-Nearest Neighbour Algorithm to Find the Nearest Slot. Journal of Computational and Theoretical Nanoscience,”2018, 15(6), 2040–2045. doi:10.1166/jctn.2018.7403
  2. Gunasekara, G. G. Y. U., Gunasekara, A. D. A. I., & Kathriarachchi, R. P. S. (2015). A Smart Vehicle Parking Management Solution.
  3. Karunarathne, M. S., & Nanayakkara, L. D. J. F. (2014). A Prototype to Identify Availability of a Car in a Smart Car Park with Aid of Programmable Chip and Infrared Sensors. Journal of Emerging Trends in Computing and Information Sciences, 5(2).
  4. Nwave, (2021), Advantages and Disadvantages of Smart Parking Sensors | Nwave [Online] Available: https://www.nwave.io/news/pros- and-cons-of-smart- parking-systems/ [Accessed 20 Feb 2021]
  5. Paidi, V., Fleyeh, H., Håkansson, J., & Nyberg, R. G. (2018). Smart parking sensors, technologies and applications for open parking lots: a review. IET Intelligent Transport Systems, 12(8), 735-741.
  6. M. A. P. Chamikara, Y. P. R. D. Yapa, S. R. Kodituwakku and J. Gunathilake, “An Efficient Algorithm to Detect The Nearest Location Of A Map For A Given Theme,” 2013 International Journal of Scientific & Technology Research.
  7. Y. Dian Harja and R. Sarno, “Determine the best option for nearest medical services using Google maps API, Haversine and TOPSIS algorithm,” 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 2018, pp. 814-819, doi: 10.1109/ICOIACT.2018.8350709.
  8. Siahaan, Andysah P. U. 2017. “Haversine Method in Looking for the Nearest Masjid.” INA-Rxiv. September 22. doi:10.31227/osf.io/eb3ja
  9. M. A. Kuhail, M. Boorlu, N. Padarthi and C. Rottinghaus, "Parking Availability Forecasting Model," 2019 IEEE International Smart Cities Conference (ISC2), 2019, pp. 619-625, doi: 10.1109/ISC246665.2019.9071688.
  10. Improve Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight Approach in iBeacon Network Laial Alsmadi Faculty of Engineering and IT University of Technology Sydney Sydney, Australia Xiaoying Kong Faculty of Engineering and IT University of Technology Sydney Sydney, Australia Kumbesan Sandrasegaran Faculty of Engineering and IT University of Technology Sydney Sydney, Australi
  11. Ibrahim, Hossam El-Din, Car Parking Problem in Urban Areas, Causes and Solutions (November 25, 2017). 1st International Conference on Towards a Better Quality of Life, 2017, Available at SSRN: https://ssrn.com/abstract=3163473or http://dx.doi.org/10.2139/ssrn.3163473
  12. M. M. Forrest, Z. Chen, S. Hassan, I. O. Raymond and K. Alinani, "Cost Effective Surface Disruption Detection System for Paved and Unpaved Roads," in IEEE Access, vol. 6, pp. 48634-48644, 2018, doi: 10.1109/ACCESS.2018.2867207.
  13. Nienaber, S & Booysen, M.J. (Thinus) & Kroon, Rs. (2015). Detecting Potholes Using Simple Image Processing Techniques and Real-World Footage. 10.13140/RG.2.1.3121.8408.
  14. Taluja, Chandan & Thakur, Ritula. (2018). An Intelligent Model for Indian Soil Classification using various Machine Learning Techniques. 2250-3005.
  15. Lee, T.; Yoon, Y.; Chun, C.; Ryu, S. CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes. Electronics 2021, 10, 1402. https://doi.org/10.3390/electronics10121402
  16. Varona, B., Monteserin, A., & Teyseyre, A. (2019). A deep learning approach to automatic road surface monitoring and pothole detection. Personal and Ubiquitous Computing. doi:10.1007/s00779-019-01234- z
  17. viso.ai, (2021), Mask R-CNN: A Beginner's Guide | viso.ai [online] Available: https://viso.ai/deep-learning/mask-r-cnn/ [Accessed 29 August 2021]
  18. Smart Parking System Based on Bluetooth Low Energy Beacons with Particle Filtering Andrew Mackey, Student Member, IEEE, Petros Spachos, Senior Member, IEEE, and Konstantinos N. Plataniotis, Fellow, IEEE
Index Terms

Computer Science
Information Sciences

Keywords

ToA - Time of Arrival TDoA-Time Difference of Arrival SVM - Support Vector Machine GPS - Global Positioning System