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Title:Evaluating Relationships Between Perception-Reaction Times, Emergency Deceleration Rates, and Crash Outcomes Using Naturalistic Driving Data
Authors:Jonathan Wood and Shaohu Zhang
Publication Date:Dec 2017
Report #:MPC-17-338
Project #:MPC-521
TRID #:01658599
Keywords:automatic data collection systems, crash reconstruction, deceleration, evaluation and assessment, highway design, mathematical prediction, perception, reaction time, statistical analysis, traffic crashes

 

Abstract

Perception-reaction times (PRT) and deceleration rates are critical components in the design of highways and streets. This research has several objectives, including 1) evaluate differences in PRT and deceleration rates between crash and near-crash events, 2) assess the correlation between PRT and deceleration rate, 3) determine if there is a causal relationship between PRT and deceleration rate (and what it is), and 4) develop predictive models for PRT and deceleration rate that can be used for roadway design and crash reconstruction. These objectives were met by applying multiple statistical analysis techniques to the SHRP2 naturalistic driving data.

The analysis results indicated that crash events were associated with longer PRT values and lower deceleration rates. The Pearson correlation between PRT and deceleration rate was low. However, PRT was a causal factor of deceleration rate in both crash and near-crash events. In crash events, longer PRT values were associated with lower deceleration rates. In near-crash events, longer PRT values were associated with higher deceleration rates.

Regression models for crash reconstruction were estimated using panel and quantile regression methods. Applications of these models for both purposes are illustrated and discussed. The results for design applications are compared with existing AASHTO design guidance.

How to Cite

Wood, Jonathan, and Shaohu Zhang. Evaluating Relationships Between Perception-Reaction Times, Emergency Deceleration Rates, and Crash Outcomes Using Naturalistic Driving Data, MPC-17-338. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2017.

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