The severity of pedestrian crashes: an analysis using Google Street View imagery
Introduction
Pedestrian safety has typically been dealt with by controlling pedestrian movements, educating the pedestrian, and enforcing laws aimed at both driver and pedestrian behavior. The control of pedestrian movements has relied largely on traffic signals, designated pedestrian crossing points, and barriers to prevent pedestrians from crossing high-speed roads. Historically, the conflict between vehicle movement and pedestrian access has favored the vehicle (Norton, 2008). The provision of sidewalks, medians, crossing points, and barrier fences in London in the early 1900s was predicated on the desire to maintain traffic flow by keeping pedestrians out of the street (Ishaque and Noland, 2006).
A recent review of the built environment and traffic safety discusses these issues from the perspective of urban planning (Ewing and Dumbaugh, 2009). Urban sprawl tends to be associated with more vehicle crashes and can result in unsafe pedestrian environments. One issue is that highway designs meant for high-speed roads are often applied within urbanized areas that have pedestrian activity (Dumbaugh and Rae, 2009), leading to dangerous conditions for pedestrians and even reducing the level of pedestrian activity (Ewing and Cervero, 2010).
Much of the research that has examined various urban design factors on road safety has used spatial analysis techniques. A common technique is to use geographic information systems to construct various measures that represent the built environment and the road network. These measures include road-network density (i.e., miles of road per unit area), intersection density (nodes or junctions per unit area), land uses (e.g., commercial, industrial, and residential, amongst others), development intensity (population and employment density), and various spatial demographic indicators (income, population, car ownership, etc.). Some studies examine the association of these variables with vehicle crashes (Aguero-Valverde and Jovanis, 2006, Dumbaugh and Rae, 2009, Noland and Quddus, 2004, Noland and Quddus, 2005), while others explore pedestrian crashes (Graham and Glaister, 2003, Kim et al., 2006, LaScala et al., 2000, Loukaitou-Sideris et al., 2007, Miranda-Moreno et al., 2011, Ukkusuri et al., 2011, Wier et al., 2009) and child pedestrian crashes (Graham et al., 2005, Petch and Henson, 2000). Much of this research has also focused on the role that area-based deprivation can have on casualties (Graham et al., 2005, Noland and Quddus, 2004).
While this research has been instrumental in showing the associations between various spatial measures and both vehicle and pedestrian crashes, it misses details about how the specific design features of roads can influence the safety of the pedestrian environment. These design details include the presence or absence of features that can benefit pedestrians, including sidewalks, medians, street buffers (planting strips), and crosswalks. Some studies have examined details such as lane width and the number of lanes, and found that increases in both are associated with more pedestrian casualties (Ukkusuri et al., 2011). This result has also been found for total road casualties (Noland, 2003). Pedestrian-friendly design policies emphasize smaller roads, adequate sidewalks, pedestrian medians, and buffers between the road and the sidewalk. In theory, these would likely increase pedestrian safety, but not much is known about the specific effects of these design treatments. Many of these may moderate vehicle speeds and thus lead to less severe crash outcomes and fewer crashes.
Recent guidelines provide engineering criteria for planning and designing of pedestrian facilities consistent with these goals (AASHTO, 2004). Information is provided on when and how to place sidewalks along streets, including their width, continuity, medians and refuges, and recommended landscaping and tree coverage. Traffic calming as a way to improve safety is also discussed. Given that these guidelines are relatively new, most existing infrastructure has not been upgraded to match recommendations.1
The research presented here examines these issues using detailed design data recorded primarily from Google Street View imagery for New Jersey (and other internet mapping resources, if needed) and using this in statistical models associating these features with the severity of pedestrian-involved crashes that have occurred. Other research has recently evaluated the reliability of using Google Street View imagery for characterizing the built environment by comparing the information collected with field audits. All have concluded that Google Street View images are a reliable source of this type of data and useful for research (Clarke et al., 2010, Rundle et al., 2011, Wilson et al., 2012). One study evaluated the ability to survey features of the built environment associated with pedestrian safety and found good concordance with field audit data (Rundle et al., 2011).
These data are used in statistical models of pedestrian crash severity. One restriction of this analysis is that we must model only those crashes that have occurred, as we do not have Google Street View imagery for every location in New Jersey where pedestrian crashes did not occur. Thus, our models estimate the association of various infrastructure features with the probability of pedestrian fatal versus injury-only outcomes. To our knowledge, this is the first analysis that examines the associations of pedestrian and road infrastructure features with crash severity at this level of detail.
Section snippets
Data and methods
A case-control methodology is used to evaluate the likelihood of fatal versus serious injury outcomes in New Jersey crashes that involved pedestrians between 2007 and 2009. Models were estimated separately for two case definitions: pedestrian fatalities versus all other injuries as the control, and fatalities and incapacitating injuries versus all other less-serious-injury outcomes as the control. The analysis does not examine the incidence of pedestrian-involved crashes. The data only allow an
Results
Of the 2351 crashes geocoded from 2007 to 2009, a slight majority (57.4%) were male. The age distribution includes 65.1% adults, 20.7% school-age children (5–17 years), 12.7% elderly (>=65 yrs), and 1.5% preschool-age children. Just over half of the crashes took place in daylight (53.6%). Drivers under the influence of alcohol were involved in 11.2% of pedestrian crashes. The influence of alcohol on the driver was uncertain in 1.9% of pedestrian-involved crashes.
The largest proportion (40.4%) of
Discussion
Our results have implications for the design of roads and pedestrian infrastructure, and for how pedestrian casualties can be made less severe when they do occur. These results do not allow an analysis of the probability of a pedestrian crash or casualty, as the data contain only crashes that have occurred. Despite this limitation, our results suggest that measures taken to reduce pedestrian casualties likely also reduce the severity of crashes when they do occur. Because there is selection
Conclusions
This research used a new approach to examine associations between pedestrian crash outcomes and a variety of road and pedestrian infrastructure features. The use of Google Street View imagery provides a far more cost-effective means of collecting the data necessary to conduct this type of analysis. Despite being cost effective, it is still a time-consuming enterprise to record a large-enough sample to conduct a reasonable statistical analysis. One major limitation is that data cannot be
Acknowledgments
This research was funded by the New Jersey Department of Transportation through the New Jersey Bicycle and Pedestrian Resource Center. All omissions and errors are the responsibility of the authors.
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