Spatiotemporal Short-Term Traffic Speed Prediction Using Machine Learning Techniques with Probe and Weather Data

Short-term traffic speed predictions are a key input for operational decision support systems that help agencies proactively manage the transportation network. This research leverages probe-based traffic speeds, historical weather data, and forecasted weather data to compare the accuracy and computational efficiency of different machine learning and deep learning models on a 6.5-mile corridor in Salt Lake City, Utah. The models are architected to capture both the spatial and temporal dependencies in traffic speed on the roadway network but not require extensive computational resources so that they can be easily scaled. A scalable Partial Least Squares (PLS) Regression model is employed to predict short-term speed using local probe-based traffic data. The training process includes optimizing the number of upstream and downstream roadway links used in the speed prediction for a link, to avoid needing to train on a wider roadway network. Additionally, this study investigates deep learning models using Long Short Term Memory (LSTM) networks, which are well-suited for prediction of time dependent datasets like traffic speed. Performance of the LSTM model is evaluated using different combinations of three input datasets: (1) traffic speed data; (2) historical weather data; and (3) forecasted weather data. The LSTM model that used all three input datasets exhibited slightly better accuracy compared to the other two models, but significantly outperformed them during a major snowstorm event in the test set. The produced models exhibited higher accuracy than other classical/statistical models presented in the literature while remaining geographically scalable.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 15p

Subject/Index Terms

Filing Info

  • Accession Number: 01764177
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-01749
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:21AM