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From page 42... ... 42 This chapter describes the statistical modeling used to better characterize the relationship between fatalities and potential factors that could explain the major drop in fatalities after 2007. Given the nature of the data (i.e., random variables and unobserved heterogeneity) Read the entire page →
From page 43... ... Modeling 43 Before any modeling can begin, it is useful to address the high level of correlation among the variables assembled for this project. This is done using an analysis tool known as factor analysis, a data-reduction technique that helps identify patterns of correlation among many variables (as opposed to two at a time) Read the entire page →
From page 44... ... 44 Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 in a later step.) Factor analyses were done in groups of similar variables: expenditures, economic measures, population, and VMT. Read the entire page →
From page 45... ... Modeling 45 7.2.1 Regression of Factors on VMT During the period from 2007 to 2012, changes in VMT coincided with the change in fatalities. It is therefore logical to first identify the factors that influenced VMT, especially the factors that reflected economic performance. Read the entire page →
From page 46... ... 46 Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 Figure 7-1 shows the comparison of the total VMT predicted by the model with the reported VMT from 2001 to 2012. Each of the circles in the graph represents one observation, i.e., one state in one year. Read the entire page →
From page 47... ... Modeling 47 Variable Chapter 8 Estimate Chapter 9 Standard error Chapter 10 P-value Intercept –2588.33 2609.50 0.322 Total population 0.00705 0.0001 <.0001 Unemployment rate of age 16 to 24 –179.40 100.70 0.075 GDP per capita in 2013 dollars 0.0140 0.0328 0.670 R2 statistic 0.97 Table 7-4. Parameter estimates for the urban VMT model. Read the entire page →
From page 48... ... 48 Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 given data set, the mean has to equal the variance. However, in practice, it has been found that count data often exhibit overdispersion, meaning that the variance is larger than the mean (Lord, Washington et al. Read the entire page →
From page 49... ... Modeling 49 The mean of the Poisson is structured as: it it it( ) q = µ eexp Eq. Read the entire page →
From page 50... ... 50 Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 The functional form used for the MCS model is the following: e s i i Xiµ = × ( ) β +γ +Σ βModel 1: VMT Eq. Read the entire page →
From page 51... ... Modeling 51 and beer consumption all are located in the pre-crash level of the Human domain, since the presumed mechanisms by which they are linked to safety are to affect decisions to drive and how (riskiness) to drive. Read the entire page →
From page 52... ... 52 Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 model using VMT, the population model will not be discussed in greater detail. Although population estimates may be generally more accurate than VMT estimates, as argued by Noland and Sun (2014) Read the entire page →
From page 53... ... Modeling 53 Variable Estimate Standard error P-value Exponentiated parameter Intercept 10.6995 0.302 <.0001 Rural VMT proportion –0.1916 0.0972 0.0486 0.826 Capital spending (in $1,000 per mile) –0.0002 0.0002 0.2255 1.000 Safety spending (in $1,000 per mile) Read the entire page →
From page 54... ... 54 Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 Variable Estimate Standard error P-value Exponentiated parameter Intercept 5.3846 0.3306 <.0001 Rural VMT proportion –0.0249 0.1068 0.8157 0.975 Capital spending (in $1,000 per mile) –0.0006 0.0002 0.0043 0.999 Safety spending (in $1,000 per mile) Read the entire page →
From page 55... ... Modeling 55 When exponentiated, the coefficients (βs) in this model can be interpreted as multipliers on how the rate of change in a predictor influences the rate of change in fatalities (Eq. Read the entire page →
From page 56... ... 56 Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 Model diagnostics indicated that the assumptions of linear regression were generally met. However, overall R2 was relatively low with only 16.8% of the total variance accounted for. Read the entire page →

From page 42...

... 42 This chapter describes the statistical modeling used to better characterize the relationship between fatalities and potential factors that could explain the major drop in fatalities after 2007. Given the nature of the data (i.e., random variables and unobserved heterogeneity)