5. Compare hot spots identified in Task 4 with traditional crash data analysis to determine level of correlation.
Task 1 – Task 1 consists of analyzing literature and collecting background information. This includes, but is not limited to, an in depth look at the connected vehicle technology currently available in the UTC Virginia testbeds, a detailed look at frequent evasive maneuvers taken during a roadway incident, as well as analyzing available vehicular data. The available vehicular data from the following sources will be used to hypothesize which variables (speed, deceleration, application of brakes, etc.) are most important in defining a near miss incident or use of evasive maneuvers:
1.Strategic Highway Research Program II (SHRP II) conducted by Virginia Tech Transportation
Institute (VTTI)
2.100-Car Naturalistic Driving Study conducted by VTTI
3.Other connected vehicle testbed data
Using the information from these sources, variables that may be able to be used as safety performance measures will be further analyzed in task 2 of this assessment. The J2735 standard message set includes numerous probe data elements that can be used in this study. This includes the following variables among other variables that may be less relevant in evasive maneuvers:
1. Acceleration
2. Brake Applied Pressure
3. Brake Boost Applied
4. Coefficient of Friction
5. Driving Wheel Angle
6. Latitude
7. Longitude
8. Obstacle Direction
9. Obstacle Distance
10. Speed
11. Stability Control Status
12. Steering Wheel Angle
13. Steering Wheel Rate of Change
14. Traction Control State 15.Yaw Rate
Task 2 – Task 2 involves determining threshold values that define a near miss incident or extreme evasive maneuvers using the performance measures selected in task 1. By applying the thresholds to the SHRP II and other testbed data, a list of every location where a near miss incident occurred will be acquired. A combination of network kernel density (KDE) analysis as well as Getis-Ord (GI*) analysis will be used to determine the probability of any location being a hot spot. Results will be compared to existing crash data for validation.
Task 3 – In Task 3, the research team will develop and prototype a safety screening application to operate using data collected by the standard Virginia Tech Transportation Institute data acquisition system on the UTC Northern Virginia testbed. This will allow for the demonstration and testing of this concept. Note that this approach allows for a very low-risk approach to field testing the concept. For the sake of the test, it will not be necessary to transmit data from the on-board acquisition system using the DSRC infrastructure given that this is not a “real-time” application. The research team will periodically acquire the vehicular data, apply the thresholds identified in Task 2, and automatically determine if there is a certain area that frequently receives data that exceeds the thresholds determined for chosen performance measures. Near miss and evasive maneuvering data will be compiled and evaluated using a KDE and GI* techniques in order to identify hot spot locations along the UTC Northern Virginia testbed. Kuo et. al. showed that using both was important because they will indicate different areas for secondary hot spots in the results. This is because GI* is based on the number of data points while network kernel density estimation detects points with extremely high values. Both are important in locating hot spots. This phase will take the most amount of time, however preparation for task 4 may be started while the system collects the data needed.