Road incidents, including accidents, greatly impact public safety, traffic flow, and overall transportation system functioning. Detecting and predicting incidents is crucial for effective incident management. Accurate algorithms rely on high-quality incident data sets. However, uncertainties exist due to the collection and recording process. To address this, cross-validating incident data with other datasets helps resolve inaccuracies. Additionally, enriching incident data with additional sources enables a more precise analysis of societal costs for planning purposes. In this study, we utilize traffic congestion data to examine and quantify the consequences of incidents on the Dutch highway network. First, we map match recorded incidents with related traffic patterns. Then, we label incidents as 'congestion' if significant congestion patterns were identified during or after the incidents or as 'no-congestion' if no significant congestion pattern occurred. For incidents labeled as congestion, we calculate and associate records with the congestion's duration, location, and Vehicle Loss Hours (VLH). The developed methodology has been implemented on five months of recorded data for the six most significant motorways in the Netherlands. This enriched dataset can be utilized for incident detection algorithms, analysis and management, and policy and decision-making.