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.
