Machine Learning for Transportation Research and Applications

Transportation is a complex system that presents challenges that are often too intricate to be addressed by conventional parametric methods. This textbook is designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning and how to develop and apply various types of machine learning models to transportation-related problems, such as traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis.

Format: Paperback / softback
Length: 252 pages
Publication date: 25 April 2023
Publisher: Elsevier - Health Sciences Division

Transportation is a complex and multifaceted domain that presents a wide range of challenges, often surpassing the capabilities of conventional parametric methods. In recent years, the availability of vast amounts of data and the rapid advancements in machine learning have opened up new avenues for addressing these complex problems. This textbook is specifically designed for college or graduate-level students who are interested in exploring the fundamentals of machine learning and its applications in transportation.

Within its pages, readers will gain a comprehensive understanding of machine learning techniques and their potential to revolutionize the field of transportation. They will learn how to develop and apply various types of machine learning models, including supervised learning, unsupervised learning, and reinforcement learning, to tackle transportation-related problems. These problems encompass a wide spectrum, ranging from traffic sensing and data quality control to traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis.

By leveraging the power of machine learning, transportation professionals can gain valuable insights into traffic patterns, optimize transportation systems, and improve safety outcomes. For instance, machine learning algorithms can be used to analyze traffic data and identify patterns that can help reduce congestion, improve traffic flow, and minimize accidents. Similarly, in the realm of transportation asset management, machine learning can be employed to predict the maintenance requirements of vehicles, optimize inventory levels, and reduce costs associated with asset depreciation.

Furthermore, machine learning has the potential to revolutionize traffic-system control and operations by enabling real-time decision-making and optimization. By analyzing traffic data and predicting future traffic conditions, transportation authorities can adjust traffic signals, alter traffic patterns, and optimize traffic flow to minimize delays and improve safety. In the field of traffic-safety analysis, machine learning can be used to analyze crash data and identify risk factors that contribute to accidents. This information can then be used to develop targeted safety interventions, such as speed limits, traffic-control devices, and driver education programs, to reduce the risk of accidents.

In conclusion, transportation is a rapidly evolving field that is being transformed by the power of machine learning. This textbook provides a comprehensive introduction to the fundamentals of machine learning and its applications in transportation, equipping students with the skills and knowledge necessary to address the complex challenges facing the industry. As the availability of data and machine learning technologies continues to grow, we can expect to see even more innovative and effective solutions being developed in the field of transportation.

Weight: 424g
Dimension: 152 x 229 x 16 (mm)
ISBN-13: 9780323961264