Arterial traffic signal control is a very important aspect of traffic management system. Efficient arterial traffic signal control strategy can reduce delay, stops, congestion, and pollution and save travel time. Commonly used pre-timed or traffic actuated signal control do not have the capability to fully respond to real-time traffic demand and pattern changes. Although some of the well-known adaptive control systems have shown advantageous over the traditional per-timed and actuated control strategies, their centralized architecture makes the maintenance, expansion, and upgrade difficult and costly.

This book (in three volumes) comprises the proceedings of the Fifth Conference of Transportation Research Group of India (CTRG2019) focusing on emerging opportunities and challenges in the field of transportation of people and freight. The contents of the volume include characterization of conventional and innovative pavement materials, operational effects of road geometry, user impact of multimodal transport projects, spatial analysis of travel patterns, socio-economic impacts of transport projects, analysis of transportation policy and planning for safety and security, technology enabled models of mobility services, etc. This book will be beneficial to researchers, educators, practitioners and policy makers alike.

Proceedings of the 15th COTA International Conference of Transportation Professionals (CICTP 2015), held in Beijing, China, July 24-27, 2015. Sponsored by the Chinese Overseas Transportation Association (COTA), Beijing Jiaotong University, the Transportation Research Board, the Institute of Transportation Engineers (ITE), and the Transportation and Development Institute of ASCE. This proceedings contains 369 papers covering current critical issues focusing on efficient, safe, and sustainable multimodal transportation. Topics include: traffic safety, security, and emergency response; high-speed rail safety; intelligent transportation systems; pavement and materials engineering; highway construction and maintenance; public transit; rail operations, management and control; traffic and driver behavior; transportation policy, planning, and modeling; and aviation, marine, and water transportation. These papers cover current trends in transportation science and will be of interest to researchers, practitioners, and students of transportation engineering.

Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).