Intelligent Transportation Systems

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes are coupled and interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. Collaborative decision-making has shown significant benefits at the airport level, both in the U.S. and in Europe. This paper examines how it could be extended to the multimodal network level, discusses the supporting evidence, and...

The papers included in this special section were presented at the Sixth Annual ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2015) that was held on April 14-16, 2015 in Seattle, WA, USA, as part of the Eighth Annual Cyber-Physical Systems Week. ICCPS is the premier single-track conference for reporting advances in all aspects of cyber-physical systems, including theory, tools, applications, systems, testbeds, and field deployments. Its focus includes the core science and technology for developing fundamental principles that underpin the integration of cyber and...

This article investigates the impact of app use on traffic patterns. With ubiquity of traffic information and the increased use of routing apps, urban and suburban areas in the US have seen a recent rise in “cut-through” traffic and related congestion patterns. This increase is suspected to be both an instantaneous phenomenon (a natural response of routing apps to special events, accidents, or other problems reducing capacity locally in transportation networks) and a trend (progressive increase of such traffic over time, with a corresponding shift in demand on the transportation...

Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces. To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP. We demonstrate and quantify the benefit of the action-dependent baseline through both theoretical analysis as...