Improving intersection safety through variable speed limits for connected vehicles
Michael Levin, Assistant Professor, Civil, Environmental and Geo-Engineering
- Chen-Fu Liao, Senior Systems Engineer, Mechanical Engineering
This research seeks to improve safety around intersections by using connected (partially or fully) autonomous vehicles (CAVs) to reduce variations in traffic speeds. Previous work has proposed variable speed limits (VSLs), which can be used to smooth speeds to improve safety. For human drivers, VSLs require a variable message sign - which restricts the spatial locations of speed changes - and is limited to human precision in speed (typically increments of 5mph). VSL effectiveness is also limited by driver compliance. These limitations can be overcome by CAVs; VSLs can be transmitted using infrastructure-to-vehicle communications, and are therefore not limited to specific locations. The study assumes that CAVs (or partially-automation such as Tesla autopilot) can be required to obey VSLs.
In the short term, CAVs will comprise only a fraction of the vehicles on the road. However, CAVs obeying VSLs travel slower than the free flow speed, thus forming a moving bottleneck for vehicles behind them. Human-driven vehicles following a CAV are forced to comply with the VSL (unless they can pass the moving bottleneck), even if they are not aware of the VSL itself.
This project creates many opportunities for national impact and future funding. VSLs might be beneficial at any intersection, and the potential benefits will increase with the availability of vehicle automation. The analytical modeling itself has additional utility for reducing congestion and environmental impacts. Although the timeframe of the project and the significant analytical modeling and simulation required prevent implementation or field operation, this project should create opportunities for future deployment.