Lecture | May 5 | 4-5 p.m. | 290 Hearst Memorial Mining Building
David Kan, UC Berkeley
Abstract: The objective of freeway on-ramp metering is to regulate the entry of vehicles to prevent capacity drop on the freeway mainline. However, the nearby arterial traffic signals facilitating freeway access fail to recognize that the metered on-ramps can be oversaturated due to the flow restriction and limited storage. Instead, the arterial traffic signals provide long cycles in order to maximize arterial capacity during peak hours. This often leads to large platoons of arterial traffic advancing to the on-ramps and thus queue spillback on the surface street. As a result, most ramp meters employ a queue override feature that is intended to prevent the on-ramp queue from obstructing traffic conditions along the adjacent surface streets. The override is triggered whenever a sensor placed at the entrance of the on-ramp detects a potential queue spillover of the on-ramp vehicles on the adjacent surface streets, and releases the queue into the freeway. The queue override reduces the effectiveness of ramp metering during the time of highest traffic demand, when the ramp metering is most needed. A field test undertaken at a freeway bottleneck in San Jose, California shows that queue override may reduce the freeway capacity by 10%. Significant benefits can be realized by reducing cycle length to prevent on-ramp oversaturation and thereby queue override. A method for determining the appropriate cycle length was developed and the improved signal timing was tested through simulation. The results show that the proposed approach prevented queue override and reduced both freeway and arterial delays.
Bio: David Kan is a Ph.D. candidate in Civil and Environmental Engineering in the Transportation Engineering program. He received his M.S. in Civil and Environmental Engineering at UC Berkeley in May 2014, and his B.S. in Civil and Environmental Engineering at University of Illinois Urbana Champaign in May 2013. His research interests include traffic operations, intelligent transportation systems, and connected and automated vehicles. He will be joining PATH as a postdoctoral researcher in May 2017.