Engineering Commons^™

Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates, Momen R. Mousa, Marwa Hassan

Data

Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R² (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics …

Modeling Crash Severity And Collision Types Using Machine Learning, Amit Kumar, Hari Krishnan Melempat Kalapurayil

Data

Traffic safety analysis is the fundamental step for reducing economic, social, and environmental cost incurred due to traffic accidents. The essence of traffic safety is understanding the factors affecting crash occurrence, injury severity and collision type and their underlying relationships and predict-prevent future crash instances. Crash injury severity studies in past have utilized numerous statistical, econometric and Machine Learning (ML) and Artificial Intelligence (AI) tools to extract the underlying relationship between the crash causal factors and the consequent severity or collision type. The study aims to explore the Multi-Label Classification (MLC) tool from the domain of Artificial Intelligence (AI) for …

Maintenance And Restriping Strategies For Pavement Markings On Asphalt Pavements In Louisiana, Momen R. Mousa, Marwa Hassan

Data

In Louisiana, most districts restripe their roadways using waterborne paints every other year; this strategy is questionable in terms of efficiency and economy. Meanwhile, previous studies showed substantial variability in the paint service life throughout the United States ranging between 0.25 and 6.2 years. Shortcomings in modeling the retroreflectivity of waterborne paints appear to significantly contribute to these variations as several studies predicted these values using degradation curves with a coefficient of determination (R2) as low as 0.1. Therefore, the objective of this study was to (i) develop new cost-effective restriping strategies using 4-inch (15-mil thickness) and 6-inch (25-mil thickness) …

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Data

Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference:

"Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, …