## 1 Introduction  
The primary role of an Advanced Traffic Management Systems (ATMS) is to improve reliability and safety through active real-time traffic management and control. Vehicular crashes endanger lives, damage property, and cause congestion, presenting an obstacle to the goal of improving the safety, efficiency, and sustainability of the transportation system. To manage a system well, it is important to understand the conditions under which crashes happen. This knowledge can inform crash management and resource allocation for incident response. For instance, Variable Speed Limits (VSL) may be an effective countermeasure to prevent crashes during recurring congestion. Conversely, incident response time may be the most effective strategy for mitigating the impacts of crashes during congested periods. However, crash records in a crash analysis database do not indicate congestion conditions at the crash scene but only environmental, crash, roadway, and driver characteristics [1; 2; 3].  
Another motivation for understanding and classifying crashes is that such information helps improve reliability and safety [4; 5; 6; 7; 8; 9; 10]. Many previous studies have focused on identifying the relationship between crashes and the traffic flow rate. The relationship between reliability and safety is less well understood, except that vehicular collisions and other unplanned incidents increase travel time variability and decrease reliability. Operating jurisdictions tend to note simply whether the recurring or non-recurring congestion was extant at the time of the crash [11; 12]. Conversely, many traffic safety researchers have studied secondary collision and the factors affecting their occurrence on freeways [13; 14].  
This analysis focuses on classifying crashes, especially whether they occurred during recurrent and non-recurrent congestion. The implicit objective is to help operating agencies understand how to reduce the number of secondary collisions and mitigate their risk. A necessary precursor is a method to classify each crash in terms of whether or not it occurred during congested conditions, and if so, whether the congestion was the cause of or impacted by the event. This calls for developing an integrated methodology to classify crashes by congestion type.  
With these considerations in mind, this paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. It classifies crashes three cases: (1) crash not during congested conditions, (2) crash during non-recurrent congestion, and (3) crash during recurring congestion. Unlike previous secondary collision identification schemes, it requires neither identification of the precipitating incident nor a definition of the precipitating incident's impact area. It supports decision-makers in their efforts to implement both safety and mobility treatments that are precisely targeted and effective.  
In what follows, relevant studies are reviewed and knowledge gaps are highlighted. Then, the proposed methodology is described and applied to a 274 km section of I-40 in North Carolina of the United States. The paper concludes with a presentation of the findings, conclusions, and recommendations for further research.