Laser Scanning Systems in Highway and Safety Assessment: Analysis of Highway Geometry and Safety Using Lidar

ISBN-13: 9783030103736 / Angielski / Twarda / 2019 / 157 str.

Biswajeet Pradhan

Spis treści

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.

O autorze

Prof. Dr. Biswajeet Pradhan

Distinguished 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 habilitation in “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 his mindful but competitive approach. He has published over 19 journal articles indexed in Web of Science, attended 9 conferences, and won three awards. 

Blurb

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.