The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Incident duration time prediction using a supervised topic modeling method
Author:	Park, Jihyun
View Online:	njit-etd2020-074 (xi, 53 pages ~ 1.6 MB pdf)
Department:	Department of Civil and Environmental Engineering
Degree:	Master of Science
Program:	Transportation
Document Type:	Thesis
Advisory Committee:	Lee, Joyoung (Committee chair) Wang, Guiling (Committee member) Dimitrijevic, Branislav (Committee member)
Date:	2020-12
Keywords:	Tarffic congestion Incident duration time prediction Semantic textg analysis Natural language processing
Availability:	Unrestricted
Abstract:	Precisely predicting the duration time of an incident is one of the most prominent components to implement proactive management strategies for traffic congestions caused by an incident. This thesis 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 thesis 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 2016-2019 by applying a Labeled Latent Dirichlet Allocation(L-LDA) approach. For the training, this thesis 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 74% and 82% for the incidents shorter and longer than 2-hour, respectively.