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Artificial Intelligence in Highway Safety

Subasish Das
€210.80
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A01=Subasish Das
Ai Algorithm
AI-based crash severity prediction
Artificial intelligence
Author_Subasish Das
Blood Alcohol Content Test
Boltzmann Machine
Category=UYQ
Code Chunk
Connected Autonomous Vehicles (CAV)
Crash Analysis
Crash Count
Crash Data
Crash Data Analysis
Crash Prediction Models
Crash Severity Analysis
DBN
Deep learning
Deep Learning Algorithms
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Highway Safety
Highway Safety Research
Local Surrogate Models
Machine learning
Ml Algorithm
MPOs
NLP
Observed Crash Frequency
PDP
Posted Speed Limit
predictive analytics safety
R programming applications
Recursive Neural Networks
road user behavior analysis
Shapley Values
supervised classification methods
Support Vector Machine
traffic risk modeling
transportation data science
Unsupervised Learning
Variable Importance Plot

Product details

  • ISBN 9780367436704
  • Weight: 760g
  • Dimensions: 156 x 234mm
  • Publication Date: 29 Sep 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend.

Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.
About Subasish Das

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

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