Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. In this research, a multi-paradigm intelligent system approach and several innovative algorithms were developed for solution of the freeway traffic incident detection problem employing advanced signal processing, pattern recognition, and classification techniques. The methodology effectively integrates fuzzy, wavelet, and neural computing techniques to improve reliability and robustness.

This book is a collection of original papers produced by the members of the Euro Working Group on Transportation (EWGT) in the last several years (2015–2017). The respective chapters present the results of various research projects carried out by the members of the EWGT and extended versions of presentations given at the last several meetings of the EWGT. The book offers a representative sampling of the EWGT’s research activities and covers the state-of-the-art in quantitative oriented transportation/logistics research. It highlights a range of advanced concepts, methodologies and technologies, divided into four major thematic streams: Multiple Criteria Analysis in Transportation and Logistics; Urban Transportation and City Logistics; Road Safety and Artificial Intelligence and Soft Computing in Transportation and Logistics. The book is intended for academics/researchers, analysts, business consultants, and graduate students who are interested in advanced techniques of mathematical modeling and computational procedures applied in transportation and logistics.