Analysis of the effects of visibility and warning devices on driver injury severity at highway-rail grade crossings considering temporal transferability of data.

This study explores the joint effect of visibility and warning devices on driver injury severity at the highway-rail grade crossings (HRGCs), while also considering other contributing factors. For this purpose, four mixed logit models are developed to estimate the determinants of driver injury severity considering the combinations of visibility conditions (daylight vs. no daylight) and type of warning devices (active vs. passive warning). The models were calibrated using the data obtained from the USDOT Federal Railroad Administration for HRGC crashes that occurred over a ten-year period 2008-2017 across the United States. A temporal transferability test was conducted and confirmed the stability of model specifications considering a ten-year span of collected data. The pseudo-elasticity analysis was conducted to ascertain marginal impact of the contributing factors on driver injury severity in each model. While the vehicle speed, train speed, time of day and driver age are found to be common significant factors among the four models, there are marked differences between parameters associated with various crash factors. The study provides new insight into the driver injury severity in train-vehicle collisions considering visibility and type of warning devices, which can help in setting up proper policies to improve safety at HRGCs.

Potential risk of using General Estimates System: bicycle safety.

Beneficial effects of bicycle helmet use have been reported mostly based on medical or survey data collected from hospitals. This study was to examine the validity of the United States General Estimates System (GES) database familiar to many transportation professionals for a beneficial effect of helmet use in reducing the severity of injury to bicyclists and found potential risk of erroneous conclusions that can be drawn by a narrowly focused study when the GES database is used. Although the focus of the study was on bicycle helmet use, its findings regarding potential risk might be true for any type of traffic safety study using the GES data. A partial proportional odds model reflecting intrinsic ordering of injury severity was mainly used. About 16,000 bicycle-involved traffic crash records occurring in 2003 through 2008 in the United States were extracted from the GES database. Using the 2003-2008 GES data, a beneficial effect of helmet use was found in 2007, yet a detrimental effect in 2004 and no effect in 2003, 2005, 2006, and 2008, which are contrary to the past findings from medical or hospital survey data. It was speculated that these mixed results might be attributable to a possible lack of representation of the GES data for bicycle-involved traffic crashes, which may be supported by the findings, such as the average helmet use rates at the time of the crashes varying from 12% in 2004 to 38% in 2008. This suggests that the GES data may not be a reliable source for studying narrowly focused issues such as the effect of helmet use. A considerable fluctuation over years in basic statistical values (e.g., average) of variables of interest (e.g., helmet use) may be an indication of a possible lack of representation of the GES data. In such a case, caution should be exercised in interpreting and generalizing analysis results.