Research Lead: Michael Zhang
UC Campus(es): UC Davis
Problem Statement: Setting an optimum speed limit on freeways for all types of vehicles, including trucks, is a critical task for highway management agencies. There are two schools of thought for setting speed limits. One is the uniform speed limit (USL), which mandates the same speed for all types of vehicular traffic. The other is the differential speed limit (DSL), which specifies distinct speed limits for cars and trucks. California along with six other states follow the DSL policy that sets a lower speed limit for trucks compared to passenger vehicles. There is no conclusive evidence on whether DSLs are safer or more economical than USLs or vice-versa for urban and rural areas. Thus, the safety and operational impacts of alternative speed limit policies are an open-ended question and to answer it will require a rich dataset with traffic attributes, roadway geometry, crash data, travel time, and other data points.
Project Description: This research will develop several micro-simulation and statistical models using multiple datasets to estimate the number and severity of crashes under different freeway speed limit scenarios. The models will account for multiple traffic attributes (speed, density, flow, travel time) under different traffic conditions (peak, off-peak). The research will also estimate the monetary cost components of different speed limits (the value of travel time, vehicle operation costs, and the economic losses caused by crashes) for both urban and rural roadways.
Status: In Progress
Project Partner(s): California Department of Transportation, California Department of Public Health, Assembly Transportation Committee