Speaking

Traditional crash analyses depend on historical crash data accumulated over the years. Transportation systems are dynamic, and changes in traffic patterns caused by new access or developments could pose new risks to the traveling public. Waiting years for crash data to accumulate before intervening will passively allow incidents to occur in the interim, causing disruptions, property damage, potential harm to travelers and loss of life. Near miss analysis represents a proactive approach to analyzing the safety of newly identified risk areas using the near miss incidents statistics as a proxy for potential crashes. The FHWA developed the Surrogate Safety Assessment Model (SSAM) to simulate trajectory data to identify near misses and provide an analytical framework to evaluate the relative turbulence of transportation systems.

This presentation will provide an overview of near miss analysis and SSAM, then show how SSAM was applied to a safety intervention in Peoria, Arizona, along the SR 101 Frontage Road near Beardsley Road. Weaving maneuvers between the arterial system, freeway system, and recently opened developments warranted a proactive safety assessment to develop a new configuration for the system to reduce turbulence in the traffic flow. The conditions in the field were observed through drone footage before and after the safety intervention. The solutions developed using SSAM were found to be very effective in reducing conflicts and even produced benefits to the general traffic operations of the surrounding system due to improved lane utilization.