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Infrastructure Safety Assessment Using Connected Vehicle Data

Abstract
Transportation agencies devote significant resources to analyzing crash data to identify “hot spots” – locations which experience larger than normal number of accidents. In many cases, upon identification of a hot spot, field investigation will point to a feature of the infrastructure that is contributing to the accidents. This feature may then be addressed specifically to improve safety. This method has been used for many years, and has proven to be effective. However, this method also has significant shortcomings. One of the key shortcomings is that the agency must wait for a large number of accidents to accumulate before a hot spot may be identified. In other words, this is a very reactive method that requires a number of crashes to occur before corrective action may be taken. Fortunately, there is a reason that crashes are most often referred to as “accidents”. They are infrequent, even at most hot spot locations. Thus, for a statistically significant accumulation of crashes to occur requires a rather long period of time. Furthermore, accurate capture of the location of crashes has long been a challenge in the transportation community. Police reports have been notoriously inaccurate in terms of crash location – although this is improving somewhat with the use of GPS. Thus, there is a need to develop a more proactive way to accurately identify “hot spots” – locations that require modifications to the transportation infrastructure to improve safety. The premise behind this project is that for every actual crash, there are numerous “near misses” where drivers’ take last second, extreme evasive action (such as swerving or skidding) to avoid a crash. These near misses are as significant as actual crashes in terms of pointing to safety problems. The challenge lies in identifying and compiling these near misses (since they are not formally reported). However, with vehicles in a connected vehicle environment, basic data will be available from the vehicle bus. If significant evasive maneuvers may be extracted from this data, along with the GPS location, this near miss data may be communicated to a transportation agency. This data may then be analyzed in a manner similar to current police crash reports to identify hot spots. This project will analyze data archived from the connected vehicle test best to extract “near miss” maneuvers. This data will then be analyzed to determine if hot spots may be identified. Then, finally, these hot spots will be examined in terms of traditional crash data to determine if there is a correlation – thus pointing to the potential of this approach.

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