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Laser scanning systems in highway an...

Pradhan, Biswajeet.

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Laser scanning systems in highway and safety assessment = analysis of highway geometry and safety using LiDAR /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Laser scanning systems in highway and safety assessment/ by Biswajeet Pradhan, Maher Ibrahim Sameen.
Reminder of title:	analysis of highway geometry and safety using LiDAR /
Author:	Pradhan, Biswajeet.
other author:	Ibrahim Sameen, Maher.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xv, 157 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction to Laser Scanning Technology -- Road Geometric Modeling Using Laser-Scanning Data -- Optimizing support vector machine and ensemble trees using the Taguchi method for automatic road network extraction -- Road Geometric Modeling Using a Novel Hierarchical Approach -- Introduction to Neural Networks -- Traffic Accidents Predictions with Neural Networks: A Review -- Applications of Deep Learning in Severity Prediction of Traffic Accidents -- Accident Modelling Using Feedforward Neural Networks -- Accident Severity Prediction with Convolutional Neural Networks -- Injury Severity Prediction Using Recurrent Neural Networks -- Improving Traffic Accident Prediction Models with Transfer Learning -- A Comparative Study between Neural Networks, Support Vector Machine, and Logistic Regression for Accident Predictions -- Estimation of Accident Factor Importance in Neural Network Models.
Contained By:	Springer eBooks
Subject:	Roads - Remote sensing. -
Online resource:	https://doi.org/10.1007/978-3-030-10374-3
ISBN:	9783030103743

Laser scanning systems in highway and safety assessment = analysis of highway geometry and safety using LiDAR /
Pradhan, Biswajeet.

Laser scanning systems in highway and safety assessmentanalysis of highway geometry and safety using LiDAR /[electronic resource] :by Biswajeet Pradhan, Maher Ibrahim Sameen. - Cham :Springer International Publishing :2020. - xv, 157 p. :ill., digital ;24 cm. - Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development,2522-8714. - Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development..

Introduction to Laser Scanning Technology -- Road Geometric Modeling Using Laser-Scanning Data -- Optimizing support vector machine and ensemble trees using the Taguchi method for automatic road network extraction -- Road Geometric Modeling Using a Novel Hierarchical Approach -- Introduction to Neural Networks -- Traffic Accidents Predictions with Neural Networks: A Review -- Applications of Deep Learning in Severity Prediction of Traffic Accidents -- Accident Modelling Using Feedforward Neural Networks -- Accident Severity Prediction with Convolutional Neural Networks -- Injury Severity Prediction Using Recurrent Neural Networks -- Improving Traffic Accident Prediction Models with Transfer Learning -- A Comparative Study between Neural Networks, Support Vector Machine, and Logistic Regression for Accident Predictions -- Estimation of Accident Factor Importance in Neural Network Models.

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.

ISBN: 9783030103743

Standard No.: 10.1007/978-3-030-10374-3doiSubjects--Topical Terms:

3442515
Roads
--Remote sensing.

LC Class. No.: TE153

Dewey Class. No.: 388.10285

Laser scanning systems in highway and safety assessment = analysis of highway geometry and safety using LiDAR /
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245 1 0 $a Laser scanning systems in highway and safety assessment $h [electronic resource] : $b analysis of highway geometry and safety using LiDAR / $c by Biswajeet Pradhan, Maher Ibrahim Sameen.
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300 $a xv, 157 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development, $x 2522-8714
505 0 $a Introduction to Laser Scanning Technology -- Road Geometric Modeling Using Laser-Scanning Data -- Optimizing support vector machine and ensemble trees using the Taguchi method for automatic road network extraction -- Road Geometric Modeling Using a Novel Hierarchical Approach -- Introduction to Neural Networks -- Traffic Accidents Predictions with Neural Networks: A Review -- Applications of Deep Learning in Severity Prediction of Traffic Accidents -- Accident Modelling Using Feedforward Neural Networks -- Accident Severity Prediction with Convolutional Neural Networks -- Injury Severity Prediction Using Recurrent Neural Networks -- Improving Traffic Accident Prediction Models with Transfer Learning -- A Comparative Study between Neural Networks, Support Vector Machine, and Logistic Regression for Accident Predictions -- Estimation of Accident Factor Importance in Neural Network Models.
520 $a 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.
650 0 $a Roads $x Remote sensing. $3 3442515
650 0 $a Optical radar. $3 637944
650 1 4 $a Transportation Technology and Traffic Engineering. $3 2153276
650 2 4 $a Environmental Management. $3 893809
650 2 4 $a Remote Sensing/Photogrammetry. $3 890882
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
650 2 4 $a Data-driven Science, Modeling and Theory Building. $3 2210495
700 1 $a Ibrahim Sameen, Maher. $3 3442514
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830 0 $a Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development. $3 3299550
856 4 0 $u https://doi.org/10.1007/978-3-030-10374-3
950 $a Earth and Environmental Science (Springer-11646)