Context aware parking occupancy forecasting in urban environment for sustainable smart parking system

DOI

10.2991/978-94-6463-620-8_9How to use a DOI?

Keywords

smart parking; parking occupancy forecasting; context-aware forecasting; urban mobility

Abstract

The increasing urbanization and car ownership rates are placing a significant strain on urban parking infrastructure, leading to congestion, pollution, and driver frustration. While smart parking systems, leveraging sensors, communication networks, and data analytics, offer a promising solution, existing systems face challenges such as limited accuracy, coverage, and integration. This paper examines the potential of context-aware parking occupancy forecasting to overcome these limitations. By incorporating external factors like traffic flow, weather, and events into forecasting models, this approach aims to improve prediction accuracy and optimize parking resource management. We discuss the current state of smart parking, its challenges, and the benefits of context aware forecasting. This research contributes to the development of more effective and efficient smart parking solutions for creating sustainable and liveable urban environments. The study leverages context-aware forecasting models such as LSTM and ARIMA to address challenges in parking occupancy prediction.

Copyright

© 2024 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.

Download article (PDF)

TY  - CONF
AU  - Miratul Khusna Mufida
AU  - Ahmed Snoun
AU  - Abdessamad Ait El Cadi
AU  - Thierry Delot
AU  - Martin Trepanier
AU  - Nelmiawati
AU  - Andy Triwinarko
AU  - Nur Cahyono Kushardianto
AU  - Wenang Anurogo
AU  - Zaenuddin Lubis
AU  - Agung Riyadi
PY  - 2024
DA  - 2024/12/25
TI  - Context aware parking occupancy forecasting in urban environment for sustainable smart parking system
BT  - Proceedings of the  7th International Conference on Applied Engineering (ICAE 2024)
PB  - Atlantis Press
SP  - 107
EP  - 125
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-620-8_9
DO  - 10.2991/978-94-6463-620-8_9
ID  - Mufida2024
ER  -