Coupling Visual Simulation Model (VISSIM) with Surrogate Safety Assessment Model (SSAM) to Evaluate Safety at Signalized Intersections.

Abstract

Traffic safety is the main support of transportation engineering. Otherwise, there is no benefit to a facility that lacks safety. Despite that, most road engineers in developing countries ignore traffic safety in their designs or adopt traditional methods that suffer from data weakness. So there is an urgent need for surrogate measures more accurate from traditional methods. This paper offers an assessment of safety at signalized intersections in Hilla city’s urban areas using a micro-simulation model (VISSM) coupled with the Surrogate Safety Assessment Model (SSAM). Three signalized intersections are modeled using the micro-simulation VISSM (version 10) model by calculating the traffic flows and speeds extracted from field data. Also, geometric characteristics and timing of the signal are simulated to reach the real-world. Then the vehicle trajectory files are exported to SSAM (version 3). Several indicators for traffic conflicts are computed by SSAM, involving the max speeding (Max S), the rate of deceleration (DR), the time of post encroachment (PET), and time to collision (TTC). The number, type, and severity of conflicts are calculated. The conflicts are categorized into three types according to the conflict’s angle, rear-end, lane change, and crossing conflicts. The results showed that the optimal values for the two safety indicators at intersections differ from one location to another, where TTC values ranged between (1.5-1.8) s and PET (4.7-5.3) s. Rear-end conflicts prevailed in all sites until their reached 55% of all conflicts. The severity of the conflicts at approaches varied from 0.74 as a high-risk collision to 1.8 as a low-risk collision, while the (TTCI) values for 40 St and Bab Al Hussein intersections were 0.86 and 0.82.respectively. Therefore, they are both classified as high-risk intersections. As for the Bab Al-Mashed intersection (TTCI=1.23), it is classified as moderate risk.