As the market availability of autonomous vehicles (AVs) (e.g. Google Cars) and connected vehicle (CV) technologies is prognosticated by 2020 (Reich, 2013), the potential impact of AVs and CV technologies on effective demand management is worth investigating. In the proposed research, we will focus on the potential of employing CV technologies for demand management of managed lane (ML) facilities, with the goal of improving the ML efficiency and effectiveness.
Managed Lanes (MLs) are considered promising facilities for early deployment of automated or connected vehicles. We envision CV technologies being adopted to provide richer and real-time information about the MLs, such as travel time variability and reliability as well as pricing variability (if applicable), to approaching drivers. Such information would likely affect travelers’ propensity of choosing MLs and thus the usage rate and the traffic conditions of the MLs and the general purpose lanes.