Overview
- Presents an overview of laser scanning technology in the context of road geometry modelling
- Includes a comprehensive review on road geometry modelling and traffic accident prediction with neural networks
- Introduces neural networks with simple theoretical backgrounds and creative illustration
- Contains special chapters on novel deep learning models developed for predicting traffic accidents
- Includes a comparative study between neural networks and statistical methods
Part of the book series: Advances in Science, Technology & Innovation (ASTI)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (12 chapters)
-
Road Geometry Modelling
-
Modeling Road Traffic Accidents
Keywords
About this book
This book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.
Authors and Affiliations
About the authors
Maher Ibrahim Sameen is a postdoctoral research fellow at the School of Information Systems and Modelling, UTS. He is fuelled by his passion for developing algorithms for remote sensing and geospatial applications. His background in surveying engineering, geomatics, and remote sensing inform hismindful but competitive approach. He has published over 19 journal articles indexed in Web of Science, attended 9 conferences, and won three awards.
Bibliographic Information
Book Title: Laser Scanning Systems in Highway and Safety Assessment
Book Subtitle: Analysis of Highway Geometry and Safety Using LiDAR
Authors: Biswajeet Pradhan, Maher Ibrahim Sameen
Series Title: Advances in Science, Technology & Innovation
DOI: https://doi.org/10.1007/978-3-030-10374-3
Publisher: Springer Cham
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-10373-6Published: 18 April 2019
eBook ISBN: 978-3-030-10374-3Published: 02 April 2019
Series ISSN: 2522-8714
Series E-ISSN: 2522-8722
Edition Number: 1
Number of Pages: XV, 157
Topics: Transportation Technology and Traffic Engineering, Environmental Management, Remote Sensing/Photogrammetry, Mathematical Models of Cognitive Processes and Neural Networks, Data-driven Science, Modeling and Theory Building