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The National Academies

SHRP 2 S01(B) [Completed]

Development of Analysis Methods Using Recent Data

  Project Data
Funds: $300,000
Phase I: $100,000 and Phase II: $200,000
Research Agency: Pennsylvania Transportation Institute
Principal Investigator: Paul P. Jovanis
Effective Date: 3/19/2007
Completion Date: 7/30/2010

Project snapshot. More details below.

Products
(Project Number)
Impact on Practice
Product Status
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.
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, https://www.trb.org/Publications/Blurbs/166050.aspx


Staff Responsibility: Charles Fay

SHRP 2 Safety research is conducting a naturalistic driving study (NDS) that monitors drivers in their cars. 

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 carry out demonstrations of the methods using data from recent field studies for road departure and intersection safety issues.

Project S01B explored structured modeling paradigms to better analyze naturalistic driving data. A series of linear regression, count regression, categorical, and hierarchical Bayesian models were tested with the data sets. Results emphasize the importance of context, driver distraction, and driver predisposition measures as determinants of crash or near-crash probability. Similar methodological structures can be used with data from the NDS to compare crash and non-crash events.

Status: The project is complete.

Product Availability: The project final report is available as an Adobe PDF document at https://www.trb.org/Main/Blurbs/166322.aspx

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