Artificial Intelligence in Highway Safety

Subasish Das is an associate research scientist at the Texas A&M Transportation Institute (TTI) of the Texas A&M University System. He received his M.S. and Ph.D. in Civil Engineering from the University of Louisiana at Lafayette in 2012 and 2015 respectively. His primary fields of research interest are roadway safety, roadway design, and associated operational issues. He is a systems engineer by training with hands-on experience on Six Sigma and Lean Engineering. His major areas of expertise include database management, statistical analysis and machine learning with emphasis in safety and transportation operations, spatial analysis with modern web GIS tools, interactive data visualization, and deep learning tools for CV/AV technologies. Dr. Das is the author or co-author of over 110 technical papers or research reports. The AASHTO Research Advisory Committee (RAC) awarded one of his research reports as 2014 AASHTO Sweet Sixteen High Value Research Project. He is an active member of ITE, APBP, and ASCE. He is an Eno Fellow. His other awards include 2018 TTI Young Researcher Awards, 2017 Urban Street Symposium Best Paper Award, 2014 and 2015 Gulf Region ITS Best Paper Award

1. Introduction 2. Highway Safety Basics 3. Artificial Intelligence Basics 4. Matrix Algebra and Probability 5. Supervised Learning 6. Unsupervised Learning 7. Deep Learning 8. Natural Language Processing 9. Explainable AI 10. Disruptive and Emerging Technologies in Highway Safety 11. Conclusions and Future Needs