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  1. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel.

  2. 19 de nov. de 2022 · A traffic flow prediction model is established. An intelligent decision-making method is given, and a coordinated optimization method for regional traffic is also given.

  3. 1 de sept. de 2022 · Various authors have explored a number of techniques to forecast traffic flow with high precision, taking into account different aspects and related characteristics. In general, three large categories of traffic flow prediction models can be found: (i) parametric techniques, (ii) machine learning techniques and (iii) deep learning ...

  4. 9 de mar. de 2023 · The suggested models predict traffic velocity under work zone conditions based on the volume of traffic approaching the work area, speed during normal conditions, work area capacity, distance from the work area, the vertical gradient of the road, downstream traffic volume, and type of highway section.

  5. 1 de dic. de 2022 · Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling.

  6. 15 de may. de 2023 · 1. Introduction. Traffic flow prediction, as one of the key technologies of intelligent transportation systems (ITS), can assist decision makers manage traffic, improve road utilization, and reduce traffic congestion. Furthermore, forward-looking traffic information can help people to make reasonable travel plans and shorten travel time [1].

  7. 9 de sept. de 2014 · In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion.