Location

Bolton Landing, New York

Date

18-6-2013

Session

Session 1 – Lectures Driver Behavior and Naturalistic Studies

Abstract

Although speeding is a major contributor to traffic fatalities, attempts to address this problem have not led to significant reductions in speed-related crashes. In this paper, we describe an investigation of speeding behaviors that was intended to: (1) identify which drivers speed, (2) model the relative roles of situational, demographic, and personality factors in predicting travel speeds, and (3) classify drivers based on their speeding patterns. The speeding behaviors of 88 drivers were recorded over the course of approximately four weeks of naturalistic driving in Seattle WA. Data collected included 1-Hz recordings of vehicle position and speed using a GPS receiver, and responses to survey questions. Regression models were developed to identify predictors of 1) “any” speeding and 2) amount of speeding. Significant predictors included demographic variables such as age and gender, situational factors such as time-of-day and day-of-week, and key personality factors such as attitudes towards reckless driving.

Rights

Copyright © 2013 the author(s)

DC Citation

Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, June 17-20, 2013, Bolton Landing, New York. Iowa City, IA: Public Policy Center, University of Iowa, 2013: 2-8.

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Jun 18th, 12:00 AM

Not So Fast! An Investigation of Real-World Speeding Behaviors and Underlying Attitudes

Bolton Landing, New York

Although speeding is a major contributor to traffic fatalities, attempts to address this problem have not led to significant reductions in speed-related crashes. In this paper, we describe an investigation of speeding behaviors that was intended to: (1) identify which drivers speed, (2) model the relative roles of situational, demographic, and personality factors in predicting travel speeds, and (3) classify drivers based on their speeding patterns. The speeding behaviors of 88 drivers were recorded over the course of approximately four weeks of naturalistic driving in Seattle WA. Data collected included 1-Hz recordings of vehicle position and speed using a GPS receiver, and responses to survey questions. Regression models were developed to identify predictors of 1) “any” speeding and 2) amount of speeding. Significant predictors included demographic variables such as age and gender, situational factors such as time-of-day and day-of-week, and key personality factors such as attitudes towards reckless driving.