Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

This paper proposes a framework for short-term traffic breakdown probability calculation using a Variational LSTM neural network model. Considering that traffic breakdown is a stochastic event, this forecast framework was devised to produce distributions as outputs, which cannot be achieved using standard deterministic recurrent neural networks. Therefore, the model counts on the robustness of neural networks but also includes the stochastic characteristics of highway capacity. The framework consists of three main steps: (i) build and train a probabilistic speed forecasting neural network, (ii) forecast speed distributions with the trained model using Monte Carlo (MC) dropout, and therefore perform Bayesian approximation, and (iii) establish a speed threshold that characterizes breakdown occurrence and calculate breakdown probabilities based on the speed distributions. The proposed framework produced an efficient control over traffic breakdown occurrence, can deal with many independent variables or features, and can be combined with traffic management strategies.

• Record URL:
  https://doi.org/10.1080/21680566.2022.2164529
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21680566
• Supplemental Notes:
  • © 2023 Hong Kong Society for Transportation Studies Limited. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Zechin, Douglas
  • Cybis, Helena Beatriz Bettella
• Publication Date: 2023-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 1026-1044
• Serial:
  • Transportmetrica B: Transport Dynamics
  • Volume: 11
  • Issue Number: 1
  • Publisher: Taylor & Francis
  • ISSN: 2168-0566
  • EISSN: 2168-0582
  • Serial URL: https://www.tandfonline.com/toc/ttrb20/current

Subject/Index Terms

• TRT Terms: Highway capacity; Highway traffic control; Machine learning; Random variables; Traffic flow; Traffic forecasting
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01901014
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 30 2023 10:45AM