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.	Key Points Individual States The rural and urban interstates of each state were analyzed using the structural modeling approach with deterministic step intervention variables at the time(s) of the speed limit increases. Rural interstates are subject to 1987 and 1996 changes while urban interstates were only changed around 1996. We will refer to the changes around that 1987 time period as the FIRST speed limit increases and those made around 1996 as the SECOND speed limit increases. We can see the significance of the speed limit increases graphically with the following code: Code 0 No Change 1 Not Significant at 0.1 Level 2 Significant at 0.1 level We can summarize the findings as follows: 19 of 40 states experienced a significant increase in fatal crashes along with the FIRST speed limit increase on rural interstates (Figure 4). 10 of 36 states experienced a significant increase in fatal crashes along with SECOND speed limit increase on rural interstates (Figure 5). 6 of 31 states experienced a significant increase in fatal crashes along with the speed limit increase on urban interstates (Figure 6). Table 1 shows the states that had significant changes on rural interstates, the estimated monthly percentage impact of the speed limit change and the number of fatal crashes in 1986-1988. From this table, we can see the monthly percentage increase in the number of fatal crashes attributable to the speed limit changes. The number of total fatal crashes for 1986-1988 are included for two reasons: 1) To interpret the percentages in terms of real numbers and 2) To see if the number of fatal crashes decreases in the year after the speed limit change. The importance behind Reason #1 is, without minimizing the value of human life, to see what the significant increase translates to in terms of actual number of crashes. For example, suppose a state averages 36 crashes per year, or 3 per month and the estimated monthly increase of fatal crashes is about 33%. The expected increase in the number of crashes is about one per month. Although statistically significant, such an increase is small in absolute numbers and may be attributable to other factors. The importance behind Reason #2 is to assess whether drivers gradually adjust to new driving conditions. For example, Arizona, displayed graphically in Figure 1, had an increase in the number of crashes the year of the speed limit change, but a decrease from that level in subsequent years. This suggests that drivers in Arizona may have learned how to safely drive at the new limit. Such patterns are not consistent across states and this issue requires further investigation. Table 2 shows the same information for the urban interstates and also includes 1998 data. Table 2 includes only states that experienced a statistically significant increase in the number of fatal crashes. During the 1996 set of changes, some states encountered a negative impact–that is, a decline in the number of fatal crashes–along with the speed limit increase. While this effect may be real, it is difficult to attribute it to the increase in speed limits, so the results are not included in Table 2. In order to get an idea of how many fatal crashes are associated with a particular speed limit increase, we can "remove" the effect of the increase from those states that had a significant increase in fatal crashes and analyze the difference between the expected and actual numbers. The predicted number of fatal crashes had the speed limit not increased is approximated by dividing the observed number of fatal crashes by (1 + the %-Change). Tables 3 and 4 show this information separated by rural and urban interstates. We see that the estimated overall percentage increases are along the same order as the individual increases resulting in approximately an additional 200 rural and 80 urban fatal crashes. It is important to note that these numbers only represent a crude approximation of the effect of the speed limit increase.   Seasonality One of the powerful benefits of using structural modeling is that, instead of removing seasonality, the effect of being in a specific month is directly modeled. The strength of the seasonal pattern was one of the most surprising aspects of this analysis. It is shown graphically below that: 29 states exhibited seasonality at the 0.05 level of significance on rural interstates (Figure 8) 18 states exhibited seasonality at the 0.05 level of significance on urban interstates (Figure 9) where the legend is interpreted as: Code 0 Not significant 1 Significant at 0.1 level 2 Significant at 0.05 level The extent of seasonality is different for each state. Most states typically have a higher number of fatal crashes in August. Some states have different patterns that have interpretations unique to that state. For instance, Florida tends to have more fatal crashes in March on its urban interstates. This corresponds to the time most college students are on Spring Break and many choose to drive to locations in Florida. In general, seasonal peaks appear to coincide with peak holiday seasons. Most states do not produce data on vehicle miles traveled, so we cannot adjust the data in a consistent manner for such effects. Aggregate ANALYSIS Though the analysis is by state, it is of interest to generalize the effect of speed limit increases to the nation as a whole. To answer this question, we use a "Super T-Test" which is a t-test on the t-values of the intervention variables. Positive t-values indicate a positive (increase) impact of the number of fatal crashes. Individually, they determine the significance of the individual impact of the policy change. To answer the question whether or not fatal crashes increase along with speed limit increases, we perform a one-sided t-test testing whether or not the mean of the t-values of all of the intervention variables are significantly greater than zero. If we reject the null hypothesis, then we can conclude that there is indeed an increase in the number of fatal crashes. It DOES NOT tell us, however, how large this increase is, only whether on average an effect exists.   Super-T Test for Rural Interstates   Test Value = 0 t df Sig. (2-tailed) Mean Difference FIRST 10.597 39 .000 1.2384 SECOND 4.009 36 .000 .6555 Super-T Test for Urban Interstates   Test Value = 0 t df Sig. (2-tailed) Mean Difference FIRST 1.373 30 .180 .2746   We see from the Super-T Tests that rural interstates tend to be affected by speed limit increases while urban interstates are not.   REFERENCES Kendall, Maurice and J. Keith Ord, 1990. Time Series. 3rd Edition. Edward Arnold, London and Oxford University Press, New York. DeLurgio, Stephen A, 1998. Forecasting Principles and Applications. Irwin McGraw-Hill, Boston.   Next: See Tables, Appendices, Attachments, and References --->