Engineering Commons^™

A Deep Machine Learning Approach For Predicting Freeway Work Zone Delay Using Big Data, Abdullah Shabarek

Dissertations

The introduction of deep learning and big data analytics may significantly elevate the performance of traffic speed prediction. Work zones become one of the most critical factors causing congestion impact, which reduces the mobility as well as traffic safety. A comprehensive literature review on existing work zone delay prediction models (i.e., parametric, simulation and non-parametric models) is conducted in this research. The research shows the limitations of each model. Moreover, most previous modeling approaches did not consider user delay for connected freeways when predicting traffic speed under work zone conditions. This research proposes Deep Artificial Neural Network (Deep ANN) and …

High-Speed Rail Safety Analysis Based On Dual-Weighted Complex Network, Liu Lv

Dissertations

This study uses a complex network model to analyze the causes of accidents in high-speed railway operations. By identifying the key factors that led to high-speed railway accidents, hidden safety hazards were discovered. This will help improve the operational safety of the U.S. high-speed rail line under construction.

The analysis uses the regional high-speed railway network in Guangzhou, China as a case study, including the railway (including high-speed railway) accidents that occurred in the company's jurisdiction from 2013 to 2017. With comparative analysis between general railways and high-speed railways, the changes of high-speed railway safety factors are explored. Data analysis …

Incident Duration Time Prediction Using A Supervised Topic Modeling Method, Jihyun Park

Theses

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 …

Development And Evaluation Of Low Cost 2-D Lidar Based Traffic Data Collection Methods, Ravi Jagirdar

Dissertations

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is …