Enhanced Parking Occupancy Prediction Using Multi-Factor Analysis and Stacked GRU-LSTM for Real-Time Smart Parking Solutions

DOI

10.2991/978-94-6463-718-2_155How to use a DOI?

Keywords

Parking occupancy prediction; multi-factor analysis; GRU-LSTM; real-time smart parking; IoT-driven intelligence; spatiotemporal dynamics; smart city; dynamic pricing; scalability; urban mobility

Abstract

Smart parking systems have become a cornerstone of urban mobility, addressing the increasing demand for efficient parking management. This study proposes an enhanced parking occupancy prediction framework leveraging multi-factor analysis and a hybrid Stacked GRU-LSTM model to deliver accurate real-time predictions. By integrating spatiotemporal dynamics and IoT-driven intelligence, the model captures complex parking patterns influenced by external factors such as weather, traffic, and special events. The scalable architecture ensures adaptability to diverse urban settings, making it a future-ready solution for evolving smart city initiatives. Extensive testing demonstrates the model's robustness, computational efficiency, and ability to minimize overfitting, ensuring reliability in both structured and unstructured parking environments. This solution facilitates dynamic pricing integration, enhances operational efficiency, and promotes a seamless user experience. The proposed framework represents a significant advancement in smart parking solutions, aligning with global sustainability goals.

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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TY  - CONF
AU  - K. Nithya
AU  - E. Baby Anitha
AU  - U. Kasthuri
AU  - V. B. Mohan Raj
AU  - M. Mouleesh
AU  - K. Sathish
PY  - 2025
DA  - 2025/05/23
TI  - Enhanced Parking Occupancy Prediction Using Multi-Factor Analysis and Stacked GRU-LSTM for Real-Time Smart Parking Solutions
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1877
EP  - 1892
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_155
DO  - 10.2991/978-94-6463-718-2_155
ID  - Nithya2025
ER  -