01624276

Component

This study aims to predict zones that have an increased risk of traffic crashes for utility service trucks of the electric power industry. This study employed a 5-year driver behavior data set from 10,009 drivers using Lytx DriveCAM units installed onboard light-duty trucks. The study consists of two steps. Step 1 employed spatial-statistical analysis to plot clusters of 13 types of driver behaviors and three collisions outcomes (actual collisions, avoidable near-collisions, and unavoidable near collision), respectively, and identify behaviors that co-located with (and predicted) collision outcomes. Four behaviors emerged as primary predictors: distraction, lack of awareness, following too close, and eating/drinking. In Step 2, negative binomial models were used to relate the occurrence of the four behaviors to a host of geospatial variables (e.g., land use, traffic, road network, and socio-economics) while controlling for exposure (defined as a product of vehicle miles traveled, population, and employment). The data span 629,270 grids (each of 200 ft. by 200 ft., roughly a street block) across three counties. Results indicated that well-balanced land use, commercial land share, connected (dense) road networks, and high speed limit contributed to the prevalence of risky behaviors, whereas residential land share. This study proposed a new way to anticipate crashes through the lens of driver behaviors. While the test data set pertains to utility service trucks, the methods can be adapted for predicting locations where the risk of future crashes is higher.

This paper was sponsored by TRB committee AND10 Standing Committee on Vehicle User Characteristics.

01618707

17-06214

English

21p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

References; Tables

Behavior; Distraction; Drivers; High risk locations; Human factors in crashes; Mapping; Spatial analysis; Statistical analysis; Truck crashes; Utility vehicles

Geospatial analysis

Highways; Motor Carriers; Planning and Forecasting; Safety and Human Factors

Transportation Research Board Annual Meeting 2017 Paper #17-06214

TRIS, TRB, ATRI

Dec 8 2016 12:31PM