66 Van Treese et al: Tree and Other Fixed Object Crashes in Florida, 2006–2013 (Cackowski and Nasar 2003). The psychological health benefits of roadside vegetation are an important consideration for landscape planning. At the same time, streetscape trees are fixed objects that can be struck during run-off-road (ROR) accidents (Turner and Mansfield 1990; Wolf and Bratton 2006). The relative risk of tree crashes is dependent on a num- ber of variables, including roadway design, roadway conditions, vehicle weight, and roadway geometry (Wolf and Bratton 2006; Abdin et al. 2009). However, there is some disagreement among researchers as to the effect of fixed objects (such as trees) on crash fre- quency. Some researchers such as Ewing and Dumbaugh (2009) argue that roadside trees promote safety by enhancing roadway definition, whereas other research- ers posit that roadside trees are hazardous (Hall et al. 1976; Zeigler 1986; Turner and Mansfield 1990). In addition to crash frequency, it is important to identify crash-related factors associated with severe injuries or death. Holdridge et al. (2005) modeled injury severity in fixed-object crashes and found that trees, utility poles, and the leading ends of guardrails increase the probability of fatal injuries in ROR crashes. Harvey and Aultman-Hall (2015) conducted a logistic regression study of 244,684 crashes in New York City between 2011 and 2013 and found that smaller, more enclosed streetscapes were character- ized by less severe crashes. The authors suggested that a more constrained streetscape makes drivers more aware of potential hazards and causes them to engage in less risky driving behavior (Harvey and Aultman-Hall 2015). While these works offer key insights, other factors related to the driver, vehicle, site, and fixed object struck during an ROR collision may impact crash severity. Quantifying the relative frequency and severity of tree-related, single-vehicle ROR crashes is an important step in assessing past roadside vegetation manage- ment efforts and developing future management plans. In assessing the frequency and severity of tree-related crashes, we posed the following research questions: (1) What is the impact of land use (urban/rural), vehi- cle type, light conditions, and weather conditions on tree and non-tree crash frequency? and (2) How does the severity of tree-related accidents compare to other single-vehicle accidents? Our results highlight the poten- tial costs of roadside trees with regard to injury and death. In identifying these potential costs, those manag- ing trees along roadways can begin to assess whether the benefits of roadside trees outweigh the potential risks. ©2019 International Society of Arboriculture MATERIALS AND METHODS Archival vehicle accident data collected by the Flor- ida Department of Highway Safety and Motor Vehi- cles (FL DHSMV) from 2006 to 2014 were analyzed between December 2016 and February 2017. These data was collected from reports (HSMV Long Report Form 90005) filled out by police officers responding to crash events. The DHSMV data included 3,033,048 crashes in total. Of these, only single-vehicle crashes were included in our analysis of crash severity. Within the single-vehicle crash data, motorcycle crashes and commercial vehicle crashes were excluded—leaving a final dataset containing 323,581 unique events. Data were standardized as needed to account for revi- sions made to the long report form in 2011. For exam- ple, before 2011, there were multiple ways to record seatbelt use (e.g., lap belt only, shoulder harness only, both lap belt and shoulder harness). With the revised form, this was a simple yes or no response. In cases where differences in data resolution were noted, choices were aggregated (if possible) to make direct compar- isons. In some cases, the 2011 revisions made it impos- sible to match variables across the entire data set. These variables were ultimately dropped from the analysis. Chi-square tests were used to assess the impact of various driver-, site-, and vehicle-related factors that influenced crash frequency. These tests were com- pleted using the prop.test() function in R (R Develop- ment Core Team, 2017). Specifically, we assessed whether or not the number of tree-related collisions varied by driver gender, suspected alcohol/drug use (i.e., yes vs. no), vehicle type, land use (i.e., rural vs. urban), light conditions (i.e., daylight, dark with light- ing, dark, dusk/dawn), and weather conditions (i.e., clear, cloudy, low visibility, precipitation, severe winds). In modeling crash severity, we utilized the variable First Harmful Event to determine what type of single- vehicle collision occurred (e.g., striking one of several fixed objects, rollover, or simply going off the road). The DHSMV (2008) defines First Harmful Event as the “injury or damage producing event which charac- terizes the crash type and identifies the nature of the first harmful event.” First Harmful Event (hereafter, Crash Type) levels were standardized as one of the following: tree, barrier, ditch, fence, no fixed object (and no rollover), pole, sign, structure, water, and rollover. Additional predictors beyond first harmful event are listed in Table 1. The outcome variable severity was recorded as one of four levels: none, minor, severe, and fatal.
March 2019
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