Recent developments in laser scanning technologies have provided innovative solutions for
acquiring three-dimensional (3D) point clouds about road corridors and its environments.
Unlike traditional field surveying, satellite imagery, and aerial photography, laser scanning
systems offer unique solutions for collecting dense point clouds with millimeter accuracy and
in a reasonable time. The data acquired by laser scanning systems empower modeling road
geometry and delineating road design parameters such as slope, superelevation, and vertical
and horizontal alignments. These geometric parameters have several geospatial applications
such as road safety management.
The purpose of this book is to promote the core understanding of suitable geospatial tools
and techniques for modeling of road traffic accidents by the state-of-the-art artificial intelligence
(AI) approaches such as neural networks (NNs) and deep learning (DL) using traffic
information and road geometry delineated from laser scanning data.
Data collection and management in databases play a major role in modeling and developing
predictive tools. Therefore, the first two chapters of this book introduce laser scanning technology
with creative explanation and graphical illustrations and review the recent methods of
extracting geometric road parameters. The third and fourth chapters present an optimization of
support vector machine and ensemble tree methods as well as novel hierarchical object-based
methods for extracting road geometry from laser scanning point clouds.
Information about historical traffic accidents and their circumstances, traffic (volume, type of
vehicles), road features (grade, superelevation, curve radius, lane width, speed limit, etc.) pertains
to what is observed to exist on road segments or road intersections. Soft computing models
such as neural networks are advanced modeling methods that can be related to traffic and road
features to the historical accidents and generates regression equations that can be used in various
phases of road safety management cycle. The regression equations produced by NN can identify
unsafe road segments, estimate how much safety has changed following a change in design, and
quantify the effects of road geometric features and traffic information on road safety.
This book aims to help graduate students, professionals, decision makers, and road planners
in developing better traffic accident prediction models using advanced neural networks.
