Road traffic congestion has adverse effects on commuter safety and transport network efficiency, apart from its environmental consequences. To address this issue, Traffic Incident Detection (TID) models have been developed, leveraging advanced connectivity technologies. However, ensuring the alignment and effective operation of these technologies within existing systems and contexts is critical. This research aims to create an incident detection algorithm supported by Vehicle-to-Vehicle (V2V) technologies, alerting road users approaching incident zones. The algorithm’s effectiveness was assessed through metrics like vehicle delays, travel time, and macroscopic fundamental diagrams (MFDs). Real-time traffic conditions were simulated using VISSIM, employing data from Inductive Loop Detectors (ILDs) and ground truth data from an instrumented vehicle on a UK motorway section. Results reveal varying impacts on delays and overall traffic based on V2V adoption rates. The presence of Connected Vehicles (CVs) ensures efficient traffic flow. These insights benefit network operators, enabling prompt identification and communication of traffic incidents to drivers, roadside infrastructure, and traffic control centers, ultimately aiming to mitigate traffic and safety impacts.