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SHRP 2 S01(A) [Completed]
Development of Analysis Methods Using Recent Data
| Project Data |
| Funds: |
$300,000 |
| Phase I: $100,000 and Phase II: $200,000 |
| Research Agency: |
University of Minnesota |
| Principal Investigator: |
Gary A. Davis |
| Effective Date: |
2/5/2007 |
| Completion Date: |
8/4/2009 |
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Project snapshot. More details below.
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Products
(Project Number)
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Impact on Practice
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Product Status
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DEVELOPMENT OF ANALYSIS METHODS USING EXISTING DATA (S01A-E)
These projects investigated four different potential methods for analyzing the SHRP 2 Naturalistic Driving Study by using data from similar field studies to examine road departure and intersection safety issues. In particular, the four projects examined the statistical relationship between surrogate measures of collisions (conflicts, critical incidents, near collisions, or roadside encroachment) and actual collisions.
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These projects provided useful background information for NDS data users.
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Staff Responsibility: Walter Diewald
The SHRP 2 Safety research program focuses on a naturalistic driving study (NDS) that monitors drivers in their cars and records information about each car trip.
The objective of the S01 project series was to identify/develop analytic methods for the SHRP 2 driving behavior and crash risk study (NDS) and demonstrate these methods by using data from similar field studies to examine road departure and intersection safety issues.
Project S01A developed analysis tools to support microscopic modeling of crashes and near-crashes. After illustrating these tools, the project team reconstructed individual crashes and near-crashes using vehicle-based data from the 100-car naturalistic study conducted by the Virginia Tech Transportation Institute (VTTI), site-based video data from the Minnesota Traffic Observatory, and site-based Doppler shift data from Cooperative Intersection Collision Avoidance Systems (CICAS) project. From the vehicle-based data and site-based data, the project team reconstructed several events involving crashes or conflicts between a following and leading vehicle. In each case, plausible estimates of acceleration histories for the leading and following drivers’ reaction times and the situation at the start of the reaction phase were obtained. For the CICAS site-based data, the project team first developed data-mining procedures to identify possible near-crash events, but they found that manual post-processing was needed to obtain data in a useable form. Nonetheless, the team was able to illustrate the potential of NDS data to enable studies of intersection near-crashes, given some technical modifications.
Status: The project is complete.
Product Availability: Research Report S2-S01A-RW-1 is only available as an Adobe PDF.
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