The National Academies

SHRP 2 S01(C) [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 Michigan Transportation Research Institute
Principal Investigator: Timothy Gordon
Effective Date: 2/5/2007
Completion Date: 7/31/2009

Project snapshot. More details below.

(Project Number)
Impact on Practice
Product Status

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.
These projects provided useful background information for NDS data users.
Four reports are available:
Development of Analysis Methods Using Recent Data, www.trb.org/Publications/Blurbs/166048.aspx

Analysis of Existing Data: Prospective Views on Methodological Paradigms, www.trb.org/Main/Blurbs/166322.aspx

A Multivariate Analysis of Crash and Naturalistic Data in Relation to Highway Factors, www.trb.org/Main/Blurbs/166049.aspx

Evaluation of Data Needs, Crash Surrogates, and Analysis Methods to Address Lane Departure Research Questions Using Naturalistic Driving Study Data, http://www.trb.org/Publications/Blurbs/166050.aspx

Staff Responsibility: Walter Diewald

The SHRP 2 Safety research program focuses on a naturalistic driving study (NDS) that monitored 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 S01C explored the use and validation of crash surrogates from naturalistic driving studies to perform detailed analysis of risk factors. The approach is based on a unified statistical analysis of crash data and surrogate events using a spatial referencing system and a common measure of exposure. This project addressed single vehicle road departure crashes. The project team proposed that suitable surrogates should be based on underlying continuous measures of disturbance in the driver’s lateral control of the vehicle. Analysis showed that simple lateral lane position did not provide a satisfactory surrogate, while estimated time to road departure was found to show the correct statistical dependencies consistent with the crash data. The team proposed additional enhancements to the crash surrogate definition, but larger data sets were needed to refine the project team’s analysis. Further exploratory analysis indicated that Extreme Value Theory is capable of giving plausible estimates for crash rates, provided a validated surrogate is used.

Status: The project is complete.

Product Availability: The project
final report is available.

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