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 …

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 …

Application Of Artificial Intelligence And Geographic Information System For Developing Automated Walkability Score, Md Mehedi Hasan

Dissertations

Walking is considered as one of the major modes of active transportation, which contributes to the livability of cities. It is highly important to ensure walk friendly sidewalks to promote human physical activities along roads. Over the last two decades, different walk scores were estimated in respect to walkability measures by applying different methods and approaches. However, in the era of big data and machine learning revolution, there is still a gap to measure the composite walkability score in an automated way by applying and quantifying the activityfriendliness of walkable streets. In this study, a street-level automated walkability score was …