Laser Scanning Systems in Highway and Safety Assessment

1. Front Matter

   Pages i-xv

   PDF

2. Road Geometry Modelling

   1. Front Matter

      Pages 1-1

      PDF

   2. Laser Scanning Technologies in Road Geometry Modeling

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 3-13

   3. Road Geometric Modeling Using Laser Scanning Data: A Critical Review

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 15-31

   4. Road Geometric Modeling Using a Novel Hierarchical Approach

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 33-46

   5. Optimizing Support Vector Machine and Ensemble Trees Using Taguchi Method for Road Extraction from LiDAR Data

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 47-60

   6. An Integrated Machine Learning Approach for Automatic Highway Extraction from Airborne LiDAR Data and Orthophotos

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 61-76

   7. Effect of Roadside Features on Injury Severity of Traffic Accidents

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 77-86

   8. Novel GIS-Based Model for Automatic Identification of Road Geometry in Vector Data

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 87-94

3. Modeling Road Traffic Accidents

   1. Front Matter

      Pages 95-95

      PDF

   2. Review of Traffic Accident Predictions with Neural Networks

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 97-109

   3. Modeling Traffic Accident Severity Using Neural Networks and Support Vector Machines

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 111-117

   4. Predicting Injury Severity of Road Traffic Accidents Using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 119-127

   5. Applications of Deep Learning in Severity Prediction of Traffic Accidents

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 129-139

   6. Forecasting Severity of Motorcycle Crashes Using Transfer Learning

      • Biswajeet Pradhan, Maher Ibrahim Sameen

      Pages 141-157

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.

• 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
• remote sensing/photogrammetry
• data-driven science, modeling and theory building

• Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Sydney, Australia

  Biswajeet Pradhan, Maher Ibrahim Sameen

Prof. Dr. Biswajeet PradhanDistinguished Professor Biswajeet Pradhan is an internationally established scientist in the field of Geospatial Information Systems (GIS), remote sensing and image processing, complex modelling/geo-computing, machine learning and soft-computing applications, natural hazards and environmental modelling and remote sensing of Earth observation. He is the Director of the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS) at the Faculty of Engineering and IT. He is also the distinguished professor at the University of Technology, Sydney. He is listed as the World’s most Highly Cited researcher by Clarivate Analytics Report in 2018, 2017 and 2016 as one of the world’s most influential mind. In 2018, he has been awarded as World Class Professor by the Ministry of Research, Technology and Higher Education, Indonesia. He is a recipient of Alexander von Humboldt Research Fellowship from Germany. In 2011, he received his habilitationin “Remote Sensing” from Dresden University of Technology, Germany. Since February 2015, he is serving as “Ambassador Scientist” for Alexander Humboldt Foundation, Germany. Professor Pradhan has received 55 awards since 2006 in recognition of his excellence in teaching, service and research. Out of his more than 450 articles, more than 400 have been published in science citation index (SCI/SCIE) technical journals. He has written eight books and thirteen book chapters. He is the Associate Editor and Editorial Member in more than 8 ISI journals. Professor Pradhan has widely travelled abroad visiting more than 52 countries to present his research findings.



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. 

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