01123195

Component

Typical engineering research on traffic safety focuses on identifying either dangerous locations or contributing factors through a post-crash analysis using aggregated traffic flow data and crash records. A recent development of transportation engineering technologies provides ample opportunities to enhance freeway traffic safety using individual vehicular information. However, methodologies on how to utilize and link such technologies to traffic safety analysis have not been thoroughly explored. Moreover, traffic safety research has not benefited from the use of hurdle-type models that treat excessive zeros in crash modeling. This study developed a new crash risk predictor, safe headway distance, to estimate traffic crash likelihood by using individual vehicular information and applying it to basic sections of interstates in Virginia. Individual vehicular data and crash data were used in the development of statistical crash prediction models including hurdle models. The results showed that safe highway distance measure was effective in predicting traffic crash occurrence and the hurdle negative binomial model outperformed other count data models including zero-inflated models.

01120148

09-0368

English

17p

2009

Transportation Research Board 88th Annual Meeting

DVD

Figures (1) ; References (16) ; Tables (5)

Crash analysis; Crash data; Crash records; Crash risk forecasting; Risk assessment; Safety; Traffic data; Traffic engineering; Traffic flow

Crash prediction models

Virginia

Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2009 Paper #09-0368

TRIS, TRB

Jan 30 2009 4:38PM