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Laser Scanning Systems in Highway and Safety Assessment

Analysis of Highway Geometry and Safety Using LiDAR

  • Biswajeet Pradhan
  • Maher Ibrahim Sameen
Textbook

Part of the Advances in Science, Technology & Innovation book series (ASTI)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Road Geometry Modelling

    1. Front Matter
      Pages 1-1
    2. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 3-13
    3. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 15-31
    4. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 33-46
    5. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 77-86
  3. Modeling Road Traffic Accidents

    1. Front Matter
      Pages 95-95
    2. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 97-109
    3. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 111-117
    4. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 129-139
    5. Biswajeet Pradhan, Maher Ibrahim Sameen
      Pages 141-157

About this book

Introduction

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.

Keywords

Traffic Accidents Prediction Model Deep Learning Recurrent Neural Network LiDAR Road Geometry Neural Networks versus statistical methods Geometric modeling of road networks Traffic Safety and Transportation

Authors and affiliations

  • Biswajeet Pradhan
    • 1
  • Maher Ibrahim Sameen
    • 2
  1. 1.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)University of Technology SydneySydneyAustralia
  2. 2.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)University of Technology SydneySydneyAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-10374-3
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-3-030-10373-6
  • Online ISBN 978-3-030-10374-3
  • Series Print ISSN 2522-8714
  • Series Online ISSN 2522-8722
  • Buy this book on publisher's site
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