Scholarlink

<p>Traffic flow forecasting is a task that is becoming increasingly important in this modern world. People use various navigation systems, including a prediction model that provides the estimated time for reaching a destination and its optimal path. The core of those systems is the traffic flow forecasting model. Although there are many forecasting models currently in use, their performance has proven to be insufficient in many situations. In this experiment, we used Temporal Fusion Transformers (TFT) to predict the average occupancy of real-life traffic from some roadside units in California. Extensive hyperparameter tuning is performed to ensure optimal results for the model. TFT outperformed all the state-of-the-art models with a precision of around 95%. Our implementation of quantile probabilistic forecasting on the data adds another layer of forecasting information and has demonstrated that the actual values mostly lie within the confidence interval range, which makes the model more useful for making navigation decisions in a traffic forecasting system.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Time-series Multivariate Multistep Traffic Flow Forecasting using Temporal Fusion Transformers

  • 期刊论文

  • 发表日期：

    2025年2月25日

查看全文
  • Saadman Sakif Arnob,
  • Ali Abir Shuvro,
  • Saadman Rahman,
  • Md. Moniruzzaman,
  • Md. Sakhawat Hossen

摘要

Traffic flow forecasting is a task that is becoming increasingly important in this modern world. People use various navigation systems, including a prediction model that provides the estimated time for reaching a destination and its optimal path. The core of those systems is the traffic flow forecasting model. Although there are many forecasting models currently in use, their performance has proven to be insufficient in many situations. In this experiment, we used Temporal Fusion Transformers (TFT) to predict the average occupancy of real-life traffic from some roadside units in California. Extensive hyperparameter tuning is performed to ensure optimal results for the model. TFT outperformed all the state-of-the-art models with a precision of around 95%. Our implementation of quantile probabilistic forecasting on the data adds another layer of forecasting information and has demonstrated that the actual values mostly lie within the confidence interval range, which makes the model more useful for making navigation decisions in a traffic forecasting system.