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indispensable part of smart city, and traffic prediction is an important component of ITS. Accurate traffic prediction is essential to many real-world applications. For example, traffic flow prediction can help city alleviate congestion; car-hailing demand prediction can prompt car-sharing companies pre-allocate cars to high demand regions.
9 de mar. de 2023 · Kashyap AA, Raviraj S, Devarakonda A, Nayak KSR, Santhosh KV, Bhat SJ (2022) Traffic flow prediction models—a review of deep learning techniques. Cogent Eng 9(1):2010510. Article Google Scholar Smith BL, Demetsky MJ (1994) Short-term traffic flow prediction: neural network approach. Transp Res Rec 98–104
23 de ene. de 2021 · Shi Y, Feng H, Geng X, Tang X, Wang Y (2019) A survey of hybrid deep learning methods for traffic flow prediction. In: Proceedings of the 2019 3rd international conference on advances in image processing, pp 133–138. Wang S, Cao J, Yu PS (2019) Deep learning for spatio-temporal data mining: a survey. arXiv:1906.04928
15 de may. de 2023 · 1. Introduction. Traffic flow prediction is one of the core issues in an Intelligent transportation system (ITS) (Humayun, Almufareh & Jhanjhi, 2022).According to the limited historical flow information of the links, accurately and efficiently predicting the flow of the road section in the future can alleviate traffic congestion and improve traffic efficiency and safety.
10 de abr. de 2014 · Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask ...
3 de sept. de 2020 · Koesdwiady et al. incorporated deep belief networks and data fusion techniques to derive more accurate traffic flow prediction with historical traffic data and weather data. Moreover, Soua et al. ( 2016 ) proposed a deep belief network-based approach to predict traffic flow using multi-stream data (i.e., historical traffic data, weather data, and event-based data).
1 de oct. de 2019 · In this paper, we mainly focus on the short-term air traffic flow prediction, in which the model predicts the air traffic flow in the near future, typically 0 to 30 minutes [3]. Since air traffic is a complicated time-varying system, any current traffic state may have significant influence on future traffic flow, that is why air traffic flow prediction is important to the ATC research.
