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The full manifestation of connected vehicles (CVs) is highly anticipated to become a reality soon given the current prevalence of CVs in our roadway networks. CVs technologies have enormous potential to improve traffic mobility and safety.

With CV-generated information, traffic flow parameters become leniently quantifiable, enabling characterization and evaluation of traffic state over a variety of operational conditions. Since the observation is independent of any space restrictions and not impacted by queue discharge and buildup, CV data offer more comprehensive, more reliable inputs to the traffic signal performance measures.

This study will propose a conceptual framework for a high-definition analysis intended to ascertain the effectiveness of trajectory-based measures in characterizing a corridor incidence such as an accident. Using a 3-intersection corridor with different signal plans, a microscopic simulation model will be created in SUMO, Omnetpp and veins platforms to quantify the benefits of CVs.

Furthermore, an algorithm for connected vehicles (CVs) that defines, detects and disseminates an accident incident to other vehicles and a roadside unit (RSU) will be proposed. The paper will demonstrate the identification of an incident with the use of visual performance metrics incorporating CV data.