## 6 Conclusions  
This paper has presented a method for classifying crashes based on the type of congestion in which they occur. The methodology has been tested using North Carolina crash data for 274 centerline kilometers on I-40 in North Carolina for Tuesdays, Wednesdays, and Thursdays in April, May, September, and October of 2012 and 2013. In addition, an approach for identifying \"recurrent\" congestion has been used as part of the procedure. Unlike previous studies that used the mean and median of the speed distribution to distinguish recurrent and non-recurrent congestion pattern, the method used here employs a recurring congestion definition that is based on an average congestion history using probe-based speeds. The proportion of secondary crashes (a surrogate for crashes in non-recurrent congestion) identified in the case study is in line with results from previous classification studies, where the secondary crash percentage ranged from 2.2% to 15.5% [28; 35; 36; 37; 38; 39; 40; 41]. However, the study found a secondary crash proportion that is near the upper end of the range reported in these earlier studies. This is to be expected because the proposed methodology classifies crashes as secondary whenever they occur in atypical congestion without the need to identify the primary crash or incident event. There are some limitations to the study. The most important one is the need to validate or verify efforts regarding secondary incidents or crash identification. It calls for identifying and defining real-world secondary events with more detailed approaches in the real world. Another issue is that the link-based traffic data provide uniform traffic performance information for the entire TMC. Thus, this study employed the starting and ending points of TMC's as the beginning and ending points for the congestion. This may cause fewer errors in identifying non-recurrent congestion conditions because of the limitation of the TMC segment itself (different link lengths). However, this limitation can be addressed as vendors provide speed data for sub-segments. In addition, as the probe percentages increase, data quality will also be an issue. Despite the fact that only 4% of the total crashes in this case study were identified as a \"Crash in recurrent congestion\", the percentage of \"secondary\" crashes caused by primary incidents in recurrent congestion increased compared well to the results of previous studies. Therefore, identifying these crashes calls for a further detailed classification methodology, which the authors are presently investigating with more accurate crash data available from the state-of-the-art technologies of vehicle such as vehicle black box and event data recorder can be used. Finally, there has been no consideration about the impact of rubbernecking because the objective of this study was to focus on developing for a robust and easily implementable methodology to classify crashes in different types of congestion. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Crashes in Non-Recurrent Congestion Locations**} & \multicolumn{2}{c}{**Reported Incident or Crash**} & \multicolumn{2}{c}{**No Data Primary Incident or Crash**} & \multicolumn{2}{c}{**Total Crashes in Non-Recurrent Congested TMC**} \\ \cline{2-5}  & **Primary Crash** & **Primary Incident** & **Incident or Crash** & **Non-Recurrent Congested TMC** \\ \hline Number of crashes & 36 & 3 & 23 & 62 \\ Percentage & 58\% & 5\% & 37\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reported vs. unreported primary incidents or crashes on crash in non-recurrent congested areas. **Author Contributions:** Conceptualization, T.-J.S., S.K., B.M.W., and N.M.R. ; methodology, T.-J.S. ; formal analysis, T.-J.S. and B.M.W. ; investigation, T.-J.S. ; resources, B.M.W. ; data curation, T.-J.S. and B.M.W. ; writing--original draft preparation, T.-J.S. and S.K. ; writing--review and editing, B.M.W., N.M.R., and G.F.L. ; visualization, T.-J.S. ; supervision, B.M.W., N.M.R., and G.F.L. ; project administration, T.-J.S.