Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Context aware parking occupancy forecasting in urban environment for sustainable smart parking system
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.
Context aware parking occupancy forecasting in urban environment for sustainable smart parking system
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.
Context aware parking occupancy forecasting in urban environment for sustainable smart parking system
Advances in Engineering res
Lumombo, Liony (Herausgeber:in) / Rahmi, Anis (Herausgeber:in) / Suwarno, Suwarno (Herausgeber:in) / Ardi, Noper (Herausgeber:in) / Kurniawan, Dwi Ely (Herausgeber:in) / Mufida, Miratul Khusna (Autor:in) / Snoun, Ahmed (Autor:in) / Cadi, Abdessamad Ait El (Autor:in) / Delot, Thierry (Autor:in) / Trepanier, Martin (Autor:in)
International Conference on Applied Engineering ; 2024 ; Batam
Proceedings of the 7th International Conference on Applied Engineering (ICAE 2024) ; Kapitel: 9 ; 107-125
25.12.2024
19 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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