The experiment results demonstrate that the proposed model for traffic flow prediction obtains high accuracy and generalizes well compared with other models.
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What are the models of traffic flow prediction?
Abstract—Traffic flow prediction is an important part of intel- ligent transportation systems (ITS). However, the performance of.
Variants researches focus on accurate and real-time forecasting of traffic flows, since it plays an important role in the construction of intelligent ...
Deep causal inference for understanding the impact of ...
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Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic ...
Explainable predictions offer valuable insights into the factors influencing traffic patterns, which help urban planners, traffic engineers, and policymakers ...
From the comprehensive research status, LSTM plays a pivotal role in traffic flow prediction research without considering spatial modeling. Various optimization ...
Jun 6, 2024 · This paper aims to explore machine learning techniques for post-processing high-resolution Numerical Weather Prediction (NWP) products for ...
It is important to point out that the focus of this study is to show how incorporating rainfall data can improve the prediction performance of traffic ...
Jun 22, 2023 · Regarding the analysis above, a Long Short-Term Memory (LSTM) Model which consider impact of COVID-19 is developed to predict one-day DRTF.
Missing: Precipitation | Show results with:Precipitation
An empirical model was proposed that considers multiple parameters such as rainfall intensity value, road friction parameter, time of the day, visibility and ...