Urban traffic flow prediction techniques: A review
Sep 1, 2022 · The evaluation metrics most used by the authors to evaluate the performance of the traffic flow prediction techniques in urban areas are the Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Square root of MSE, precision (ACC) and confusion matrix.
Road network traffic flow prediction: A personalized federated …
Dec 1, 2024 · Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc.
Traffic flow - Wikipedia
In transportation engineering, traffic flow is the study of interactions between travellers (including pedestrians, cyclists, drivers, and their vehicles) and infrastructure (including highways, signage, and traffic control devices), with the aim of understanding and developing an optimal transport network with efficient movement of traffic and ...
Gap, techniques and evaluation: traffic flow prediction using …
Dec 4, 2021 · In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML).
Safety evaluation for mixed traffic flow of CAVs with different ...
Four safety evaluation metrics are employed, and three-speed scenarios are set to investigate the influence of connection levels, automation levels, and their combined effects on mixed traffic flow safety.
Artificial intelligence-based traffic flow prediction: a …
Mar 9, 2023 · Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of artificial intelligence approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry.
Full article: Traffic flow prediction models – A review of deep ...
Dec 27, 2021 · In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE).
(PDF) Gap, techniques and evaluation: traffic flow prediction …
Dec 4, 2021 · In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of...
LSTM + Transformer Real-Time Crash Risk Evaluation Using Traffic Flow …
Aug 12, 2024 · In this study, the risky driving behavior data generated by Connected Vehicle (CV) techniques was introduced along with traffic flow data to develop the crash risk evaluation model.
Federated learning analysis for vehicular traffic flow prediction ...
Jan 14, 2024 · There are several approaches to predict traffic flow, including statistical methods, machine learning techniques, and simulation models. Statistical methods involve analyzing historical traffic data to identify patterns and trends that can be used to make predictions about future traffic conditions.
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