Applications of Soft Computing in Intelligent Transportation Systems

Abstract

Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively proved in many different facets of transport and Soft Computing has played a major role in achieving these successful results. This book chapter aims at gathering and discussing some of the most relevant and recent advances of the application of Soft Computing in four important areas of Intelligent Transportation Systems as autonomous driving, traffic state prediction, vehicle route planning and vehicular ad hoc networks.

Soft computing in business: exploring current research and outlining future research directions

“…To properly understand the significance and potential of soft computing research, it is essential to have a comprehensive understanding of its evolution and current dynamics. Soft computing can be implemented across a wide variety of industries, including mobile and wireless communication (Atayero and Luka, 2012), agriculture (Huang et al ., 2010), oil industry (Aminzadeh, 2005), transportation (Masegosa et al ., 2018), textile (Sette and Van Langenhove, 2003) and few others. While the application of soft computing methodologies constitutes a viable solution in many industries, this technology has certain limitations (Huang et al ., 2010; Khan and Alotaibi, 2021).…”

Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

“…In previous studies, SC methods have been proposed in various transportation problems such as road traffic flow and network predicti indicatio characte network The rest various METAN highligh method descript propose fourth s computing, such as swarm intelligence, etc., have provided significant achievements in improving the performance of ITS. These enhancements are achieved mainly due to the massive changes in the data scale generated and collected from various sources by the involved stakeholders, e.g., governments, citizens, and the industry with respect to these systems [15], [16]. Intelligent transportation-based systems are indeed a wellsuited area to apply soft computing techniques since the data provided here are full of uncertainty and vagueness, where technical disciplines of SC techniques such as approximate computing and randomized search can be properly employed [17], [18].…”