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Hace 4 días · The accurate prediction of traffic conditions is essential for effective and efficient traffic management and control. The dynamic and complex nature of traffic data, characterized by intricate temporal and spatial features, presents significant challenges to accurate traffic forecasting. While previous studies have developed various models with advanced algorithms, they often fail to fully ...
Hace 4 días · Many authors have used a predictive approach to congestion control. They have used Machine Learning to analyze and predict congestion [].There are two primary types of traffic prediction methods: parametric (such as stochastic and temporal approaches) and non-parametric (such as machine-learning (ML) models, which have been used recently to solve complex traffic problems).
6 de may. de 2024 · In this section, smart traffic vehicle management using Faster R-CNN based deep learning based ensemble method is highlighted. The research problem revolves around traffic management which ...
Hace 4 días · Due to the ability to adaptively model the dependencies between source sequences and targets, the attention mechanism has become a common method for modeling the spatial dynamics of traffic networks. Several GCN-based studies utilized the attention mechanism to improve the traditional Laplace matrix by enabling it to dynamically represent spatial correlations [ 36 - 40 ].
Hace 5 días · The research employed a model that utilizes the macroscopic fundamental diagram to examine parking and traffic flow more efficiently and with reduced computational expenses.
Hace 4 días · 1 That is, the traffic state that the driver will experience (Cascetta Citation 2009; Cantarella et al. Citation 2019). 2 The proposed model was preliminary calibrated using the data collected on 400 meters of the 5 I80 highway in the USA (NGSIM I80-1, 2021), during 15 min.
10 de may. de 2024 · 5 Conclusion. This study introduces an innovative approach to traffic prediction that significantly enhances the accuracy of forecasting, particularly under atypical conditions such as extreme weather or special events, by integrating textual and numerical data through the use of Large Language Models.
