The National Academies

SHRP 2 S01(E) [Completed]

Development of Analysis Methods Using Recent Data

  Project Data
Funds: $300,000
Phase I: $100,000 and Phase II: $200,000
Research Agency: Iowa State University Center for Transportation Research & Education
Principal Investigator: Shauna Hallmark
Effective Date: 3/2/2007
Completion Date: 10/30/2010

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 Driving Study 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 Event Data in Relation to Highway Factors Using the GIS Framework, 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: 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 2driving 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 S01E developed a framework and provided background information that can be used to answer lane departure research questions using data from the NDS. The research team developed three analytical approaches to accomplish this. The first is a data mining approach, which uses a classification and regression tree analysis to identify variables with the most power in influencing the occurrence of a right- or left-side lane departure. The second is an event-based approach, which uses calculation of simple odds ratios and logistic regression. The third approach uses continuous data in a time series model, which can incorporate information from previous time periods.

Status: The project is complete.

Product Availability: The project final report is available as an Adobe PDF document at http://www.trb.org/Main/Blurbs/166050.aspx.

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