The Impact of Speed Limit Increases on Fatal Interstate Crashes

Assessing the Impact of Speed Limit Increases on Fatal Interstate Crashes
By Sandy Balkin, Ernst & Young LLP and J. Keith Ord, Georgetown University

Executive Summary

Introduction

Data Source

Methodology

Key Points

Charts and Tables
Fig 1:
Trend Component
for Rural Arizona

Fig 2:
Seasonal Component for Rural Arizona

Figure 3:
Irregular Component
for Rural Arizona

Figure 4:
Significance Level
of First Rural Limit Change

Figure 5:
Significance Level of Second Rural Limit Change

Figure 6:
Significance Level of Urban Limit Change

Figure 7:
Monthly Fatal Crashes

Figure 8:
Rural Seasonal Component Significance Level

Figure 9:
Urban Seasonal Component Significance Level

Table 1:
Significant Rural Changes

Table 2:
Significant Urban Changes

Table 3:
Predicted number of fatal crashes attributed to the speed limit increase on rural interstates.

Table 4:
Predicted number of fatal crashes attributed to the speed limit increase on urban interstates.
Data Source

The data analyzed in this study are the number of fatal crashes per month for each state separated by rural and urban interstates. We used fatal crashes rather than number of deaths since we regard the accidents data as a more reliable guide to road safety conditions. The number of fatal crashes was determined from the Fatality Analysis Reporting System (FARS) which is publicly available at the website http://www-fars.nhtsa.dot.gov. The database was downloaded in SAS© format. At this site, it is possible to query the FARS database for yearly statistics for 1994-1998. We are confident, since our monthly values sum up to the yearly values reported by the online system, that we were able to successfully extract the appropriate data. Note that our yearly totals do not always exactly match the yearly totals given in the studies mentioned previously. These discrepancies can be attributed to the changing of the FARS Database structure, differences in opinion on which roadways were included, or user error. Again, since our data set matches the online database query totals, we are satisfied with the quality of our data compilation.

In this study, we analyze the natural logarithm of the number of fatal crashes per month. The use of logarithms allows us to consider percentage changes rather than absolute shifts and stabilizes the variance of the series. Since some of the months have a zero number of fatal crashes, it is necessary to add 1 to each month prior to transforming the data. Thus, when looking at the plots of the data, it should be remembered that the series is shifted up by one unit.

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