Laser Scanning Systems in Highway and Safety Assessment

Analysis of Highway Geometry and Safety Using LiDAR

Nonfiction, Science & Nature, Technology, Environmental, Engineering, Civil
Cover of the book Laser Scanning Systems in Highway and Safety Assessment by Biswajeet Pradhan, Maher Ibrahim Sameen, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Biswajeet Pradhan, Maher Ibrahim Sameen ISBN: 9783030103743
Publisher: Springer International Publishing Publication: April 2, 2019
Imprint: Springer Language: English
Author: Biswajeet Pradhan, Maher Ibrahim Sameen
ISBN: 9783030103743
Publisher: Springer International Publishing
Publication: April 2, 2019
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book The Scottish Book by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Talking Bodies by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Body Sensors and Electrocardiography by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Advancing Big Data Benchmarks by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Noncontact Atomic Force Microscopy by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Composing Fisher Kernels from Deep Neural Models by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Artificial Neural Networks and Machine Learning – ICANN 2017 by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Processes and Pathways of Family-School Partnerships Across Development by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Extremophilic Microbial Processing of Lignocellulosic Feedstocks to Biofuels, Value-Added Products, and Usable Power by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Stochastic Dynamics and Irreversibility by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Formal Techniques for Distributed Objects, Components, and Systems by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Advances in Brain Inspired Cognitive Systems by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Re-Evaluating Women's Page Journalism in the Post-World War II Era by Biswajeet Pradhan, Maher Ibrahim Sameen
Cover of the book Italy’s Top Products in World Trade by Biswajeet Pradhan, Maher Ibrahim Sameen
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy