Evaluating pedestrian crashes in areas with high low-income or minority populations
Introduction
In 2005, motor-vehicle crashes killed 4881 pedestrians in traffic in the United States and sent 120,815 pedestrians to emergency medical departments (National Highway Traffic Safety Administration [NHTSA], 2006). While the contributing causal factors to these crashes vary widely, previous studies have indicated that certain factors relating to the socio-demographics and the physical environment of neighborhoods may heighten the risk of pedestrian–vehicle crashes. These initial indications are particularly relevant in regards to environmental justice (EJ) guidelines which broadly require each U.S. federal agency to make the alleviation of inequitable environmental burden a part of its mission by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of its programs, policies, and activities on minority and low-income populations (Environmental Protection Agency, EPA, Executive Order, 1994). This paper is intended to specifically address pedestrian–vehicle crashes within the framework of EJ guidance to determine if pedestrian safety is an issue that should be specifically addressed within EJ activities.
Several authors have noted that there is an increased incidence of pedestrian–vehicle crashes in EJ areas (Mid-America Regional Council, 2007, Murtha, 2005). EJ areas are those with high proportions of minority and low-income households (the specific definition used in this paper is given in Section 3.1). While numbers gathered from these evaluations are of interest, what is notable is that occurrences of crashes are often cited while paying little attention to the question of general exposure to risk, including such factors as mixture of land uses, the number of potential pedestrians in an area, or the overall character of the surrounding environment and whether it is inviting to and safe for pedestrian travel.
The current study more extensively explores some geographic correlates of pedestrian–vehicle crashes, through an analysis of data from the Chicago metropolitan area in the State of Illinois, USA, with particular emphasis on spatial variations on these events by EJ versus non-EJ area. We examine 2005 Chicago vehicle–pedestrian crash data, gathered from the Illinois Department of Transportation (IDOT) as a case study in order to hold factors beyond socio-demographics constant and to provide information relating to risk levels in environmental justice areas. Statistical evidence, as presented below, shows that an elevated risk is present, perhaps indicating that policies and programs addressing pedestrian–vehicle crashes in these areas should be targeted to those factors that are likely to account for such increases in risk.
The objective of the paper is to report on a comparative analysis of motor-vehicle–pedestrian crashes in EJ versus non-EJ areas and to understand which factors contribute to variations in pedestrian crashes in those areas. We have taken the view that two groups of geographic factors can potentially affect observed differences – those relating to the physical environment (called environmental factors) of the areas and those relating to social behaviors, cognitive and demographics (called behavioral factors) of the residents of the areas. These measures are a part of an ongoing research project to develop a Spatial Decision Support System on the Chicago metro area for planning purposes by the authors’ research team. The construction and potential use of these measures in accident analysis are described. The importance of these factors in explaining EJ versus non-EJ differences in pedestrian crashes are demonstrated using exploratory methods as well as regression. An issue of concern that is addressed in the paper is underreporting of pedestrian crashes – we have adopted a statistical approach to address potential underreporting in the Chicago vehicle–pedestrian crash data.
The paper is organized as follows: prior literature on environmental justice and pedestrian crashes is reviewed in Section 2. The study area, the method by which EJ areas were determined and the environmental and behavioral factors considered are presented in Section 3. Section 4 presents an exploratory analysis of crash trends in EJ versus non-EJ areas and reports summary statistics on the factors and on crash patterns in the two types of areas. Statistical models of pedestrian crashes are given in Section 5. Summary and conclusions are drawn in Section 6.
Section snippets
Background and prior literature
In 1994, President Clinton signed Executive Order 12898, “Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations”. As part of this order, it was required that, “each Federal agency shall make achieving environmental justice part of its mission by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of its programs, policies, and activities on minority populations and low-income
Study area
In 2005, the Illinois Department of Transportation (IDOT) reported 4886 vehicle crashes in the Chicago region in which a pedestrian was involved. Of those crashes, 120 (or about 3%) resulted in the death of the pedestrian. Fig. 1 shows the distribution of vehicle–pedestrian crashes in the six-county Chicago metro area (consisting of Cook, McHenry, Kane, Will, Lake, and DuPage counties; Cook county includes the City of Chicago). Overall, pedestrian–vehicle crashes accounted for about 1% of all
Exploratory analysis of pedestrian crashes in EJ versus non-EJ areas
Table 2, Table 3 show summary statistics on variables that are of importance in the analysis. Table 2 shows that the number of pedestrian crashes in EJ areas are well over twice that in non-EJ areas and that the pedestrian crash rate per 1000 population is 9.66 in EJ areas compared to 3.37 in non-EJ areas. Large differences do not exist in the proportion of injuries of different types between EJ and non-EJ areas. Injuries are classified as follows (IDOT Crash Report Data Dictionary, 2005):
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Statistical models of pedestrian crash counts
The basic form of the statistical models considered is PED_CRASHj = f(EJ_TRACT, environmental factors, behavioral factors)i where PED_CRASHj is the count of motor-vehicle–pedestrian crashes in the jth tract and EJ_TRACTj is a binary variable taking the value of 1 if the jth tract is considered to be an EJ tract based on the rules given in Section 3.1 and the environmental and behavioral factors are as described in Section 3.2.
The crash data considered in this paper poses several methodological
Summary and conclusions
This paper evaluates the incidence of pedestrian–vehicle crashes in high low-income and minority population (EJ) census tracts as compared to pedestrian–vehicle crashes in non-EJ tracts in the Chicago metropolitan area. The summary statistics presented above, primarily the overall incidence, are generally supportive of the finding that pedestrian–vehicle crashes are more prevalent in EJ census tracts, as indicated in previous research. By taking a two-pronged approach to the data set and
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