Optimized Intelligent Driver Model for a Fluid Traffic Flow and Accidents Avoidance

With an increasing number of vehicles equipped with ACC (Adaptive Cruise Control), the impact of such vehicles on the traffic flow becomes significant. Especially in the zones where traffic safety is a concern. Here, we can mention roads where traffic is characterized by its "stop and go" particularity. One of the widely used and well-known algorithms to serve as the basis for a control algorithm of a real-world ACC system is the IDM (Intelligent Driver Model). This contribution analyzes the IDM, a CFM (Car-Following Model) governed by a system of differential equations. The model, in recent years, has been intensively studied for capturing traffic phenomena properly and making driver braking behavior safer. Despite the intensive analyses, to our knowledge, a rigorous study of comfortable braking, when an emergency occurs, has rarely been performed. Especially when vehicles' densities and speeds are in constant variation and when, in most situations, urgent braking is necessary. In this work, we modify the classic IDM to introduce the O-IDM (Optimized-Intelligent Driver Model). We increase safety distances between vehicles to prevent accident risks without permitting vehicles' velocities to become negative or to diverge to -∞ in finite time. We personalize IDM parameters for each type of vehicle and we aim to make the transition of the traffic flow from "free flow" to "stop and go" traffic more comfortable. We compare the obtained O-IDM results to the Krauss-FM (Krauss-Following Model) and to the classic IDM to prove the efficiency of our approach.