Incident Duration Time Prediction Using A Supervised Topic Modeling Method

Precisely predicting the duration time of an incident is one of the most prominent components to implement proactive congestion management strategies caused by an incident. This paper presents a novel method to predict incident duration time in a timely manner by using an emerging supervised topic modeling method. Based on Natural Language Processing (NLP) techniques,  this paper performs semantic text analyses with text-based incident dataset to train the model. The model is trained with actual 1,466 incident records collected by Korea Expressway Corporation from 2016 to 2019 by applying a Labeled Latent Dirichlet Allocation(L-LDA) approach. For the training, this paper divides the incident duration times into two groups: shorter than 2-hour and longer than 2-hour, based on the MUTCD incident management guideline. The model is tested with randomly selected incident records that have not been used for the training. The results demonstrate that the overall prediction accuracies are approximately 77% and 81% for the incidents shorter and longer than 2-hour, respectively.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p

Subject/Index Terms

Filing Info

  • Accession Number: 01764331
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03632
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:26AM