## 2 Literature Review  
Several studies have tested the relationship between crash rates and flow rates or density [4; 5; 6; 7; 8; 9; 10]. However, the results have been inconclusive. [PERSON] and [PERSON] [4] found that there was a U-shaped relationship between V/C ratio and crash rate on freeways. On the other hand, [PERSON] et al. [8] did not find relationships between crash rate and congestion or severity and congestion. Insofar as crash types and congestion are concerned, there are two important questions: (1) does congestion influence the crash type and (2) vice versa. Most studies have focused on the former but this one was concerned with the latter. [PERSON] et al. [9] and [PERSON] et al. [10] found that rear-end crashes were more likely under unstable traffic flow conditions. [PERSON] et al. [15] identified main factors influencing accidents on road bridge and found that traffic volume was the most influencing factor. [PERSON] et al. [6] found that traffic congestion had little or no impact on crash rates. However, their statistical model failed to pass statistical significance tests. The reason for this might be that they used a congestion index to test the relationship, and that index was based on the average congestion level across an entire year. More recent studies have focused on identifying the relationship between road accidents and traffic volume. [PERSON] et al. [16] found that high traffic volume was responsible for 25.6% of the serious casualty crashes indicating that there is a positive relationship between traffic volume and road accidents. [PERSON] [17] concluded that congestion can be linked with crashes by superimposing crash areas on top of congested areas using GIS shapefiles. [PERSON] and [PERSON] [18] reviewed this work and concluded that the method was not tested using congestion information that pertained at the time of the crash. The study presented here addresses this issue by using a congestion measure that is predicated on the traffic conditions at the time of the crash. At extant traffic condition (congestion) can be classified as either recurrent or non-recurrent. A definition of non-recurrent congestion is as follows; delay caused by an incident, a work zone, adverse weather, or other non-repetitive event [19; 20; 21; 22; 23; 24; 25]. [PERSON] [21] defines non-recurrent congestion as the extra delay caused by incidents compared with the annual average section travel speed. For instance, if the free-flow speed is 60 mi/h and annual average section travel speed is 30 mi/h during peak periods, then it is assumed that recurrent congestion occurs. Defining recurring congestion is more problematic [19; 20; 21; 22; 26]. Oxford's Dictionary defines \"recurrent\" as being something that occurs often or repeatedly. In other words, recurrent congestion should be \"predictable\" in both location and time. Drivers should indicate that \"this area, at this time, is often\". Most previous studies use either the mean or the median of a speed distribution during a specified time of day to measure recurrent delay and extra delay caused by incidents as non-recurrent congestion on a freeway. [PERSON] [27] defines recurring congestion as the combination of a location, time, and speed: for example, an average speed of 35 mile per hour or less for 15 min or more on a specific freeway segment. [PERSON] [25] defines it as a Travel Time Index (TTI) of 1.5 or more for a given segment. [PERSON] et al. [11] recently employed a data-driven approach using link speeds. Their approach defined \"recurrent\" consistent with the Oxford's Dictionary definition and identified recurrent bottleneck locations and their time span. Collision classification tends to rely on deterministic queuing theory. If a specific freeway segment is under study, the rules are often as follows. First, crashes which occur during times of non-recurrent congestion are classified as such. These become the \"primary\" incidents. Then, the boundaries of the impact area are determined. The rules for doing this may be either static or dynamic.