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Appendix C Effect of Speed Limits on Speed Distributions and Highway Safety: A Survey of the Literature Patrick McCarthy Department of Economics, Purdue University The purpose of this review is to examine recent work on the effect of motor vehicle speed limits on highway speeds and highway safety. The review is empirical and concentrates on identifying the quanti- tative effects of changes in regulatory speed limit policies on the dis- tribution of speeds and traffic safety. In general, the proposition that changes in speed limits induce observed changes in the distribution of speeds on a road network and have implications for the network's highway safety is straightforward. However, testing this proposition 277
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MANAGING SPEED 278 in a scientific inquiry to reliably answer such questions as "How will a 5-mph (8-km/h) increase in the speed limit alter the average speed of travel on a road? Will the number of fatal crashes increase if driv- ers, on average, travel 5 mph faster? What effect will a 5-mph increase in the speed limit have on the fatality rate?" is not straight- forward. Through local, state, and federal provision, the country's trans- portation system comprises a network of roads that differ by size, quality, and location. The attributes of each road's users (e.g., socio- economic characteristics of the drivers, proportion of truck traffic) over some time period will generally differ by type, quality, and loca- tion of road. In combination with differential speed limits, traffic enforcement, and other government interventions, the nation's high- ways produce trips and, as a by-product, highway safety outcomes. If all roads and all road users were homogeneous, then determining the effect of alternative speed limit policies on speed distributions and highway safety would be relatively easy. However, the fact that both the individual components of a road system and its users are hetero- geneous complicates the task of identifying the effects of changes in speed limit policies on highway speed distributions and safety. Over the past 24 years, the U.S. Congress has passed three major pieces of federal legislation related to speed limits. First, responding to the oil crisis in the early 1970s, Congress passed the Emergency Highway Energy Conservation (EHEC) Act in 1974. Among its provisions, the act mandated a 55-mph (89-km/h) national maxi- mum speed limit (NMSL) on all U.S. highways and threatened a loss of highway funds if states did not sufficiently enforce the limit. A Transportation Research Board (TRB) study (1984) concluded that implementation of the 55-mph national speed limit saved 2,000 to 4,000 lives annually. In 1987, Congress passed the Surface Transportation Uniform Relocation Assistance (STURA) Act, which gave each state the right to increase speed limits on portions of the Interstate system lying within the least-populated areas of its boundaries. Thirty-eight states immediately responded to the legislation by raising speed limits on their rural Interstate highways, followed in 1988 by 2 additional states. Since passage of the 1987 legislation, there have been numer-
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279 Effect of Speed Limits on Speed Distributions and Highway Safety ous national, regional, and statewide studies analyzing the effects of relaxed rural Interstate speed limits on highway safety. Most recently, Congress passed the National Highway System Designation Act of 1995, which gave states complete freedom to set speed limits within their jurisdictional boundaries. To date, little sci- entific information is available on the effect this has had on speed distributions and highway safety. By examining the empirical relationships among speed limits, speed distributions, and highway safety on nonlimited- and limited- access roads, this paper complements recent international reviews of speed limits and highway safety (Fildes and Lee 1993; Knowles et al. 1997). In general, this review covers domestic and international speed limit studies that have been conducted since the extensive 1984 TRB review. For a subset of studies, this review critically analyzes both the studies' findings and the strengths and weaknesses of their method- ological approaches. This review focuses on recent research that has analyzed the impli- cations for highway safety as a direct result of the relaxed (rural) Interstate 65-mph (105-km/h) speed limit embodied in the STURA Act. There are three primary reasons for this focus. First, passage of the STURA Act was national in scope, affecting all roads, Interstate and non-Interstate, posted with speed limits above 55 mph (89 km/h). Of the rural and urban Interstate highways in 1990 with eli- gible mileage, 98 and 97 percent, respectively, had posted speed lim- its of 65 mph (NHTSA 1992). Moreover, although accounting for only 7.3 percent of total lane miles in the national transportation net- work, Interstate highways account for 26.1 percent of vehicle miles traveled (FHWA 1995). Factors that affect travel on limited-access high-speed roads have the potential for significantly affecting high- way safety. Second, although it was national in scope, the 1987 STURA Act did not roll back the 1974 legislation. Rather, the act modified the structure of speed limits on limited-access roads by per- mitting higher speeds on sections of the system that past research had identified as the safest. This raises interesting and contentious issues concerning the law's effect on highway safety. Concentrating higher speed limits on the safest parts of the transportation network makes more plausible, for example, the controversial possibility that
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MANAGING SPEED 280 "fine-tuning" Interstate speed limits may have actually raised overall safety on the nation's highways. Third, a sufficient amount of time since the law's 1987 passage has elapsed for researchers to conduct longer-term studies on the law's varied effects. In contrast to short- term "impact" studies that, at times, may provide misleading infor- mation to policy makers, studies based on a longer sample of postenactment data are likely to more fully capture the traveling pop- ulation's myriad adjustments to the new environment. This paper is organized as follows. First, an overall framework within which to place existing empirical studies on the effects of speed limit laws is discussed. The methodological constructs most frequently used to empirically evaluate the effect of speed limit laws on speed distributions and highway safety are summarized. The known information on the effects of changing posted speed limits on the distribution of speeds is discussed. The effect of speed distribu- tions on highway safety is examined. The findings of various studies examining the direct and indirect effects that relaxed speed limits on rural Interstate highways have had on the motoring public's safety are reported. International experience with speed limits is reviewed. Finally, a number of areas for further research are identified, and con- cluding comments are given. SPEED LIMIT≠HIGHWAY SAFETY FRAMEWORK Figure C-1 shows the relationship between speed limits and highway safety on a given road. The basic mechanism between speed limits and highway safety is shown in the middle of the figure. Put simply, speed limits, among other factors, influence drivers' choices of opti- mal speeds. Solid arrows indicate a direction of direct causality. As shown by the figure, the posted speed limit is one of many objective factors that directly feed into a driver's speed decision. Other impor- tant determinants include highway and vehicle design, traffic enforcement and other highway government interventions (e.g., safety belt use laws), environmental attributes (e.g., weather condi- tions, topography), and characteristics of the driving population (e.g., proportion of younger drivers). At the same time, a driver has under- lying preferences for risk and, in any specific driving situation, a sub-
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281 Effect of Speed Limits on Speed Distributions and Highway Safety Figure C-1 Relationship between speed limit and highway safety. jective view of traffic safety. By combining to determine a driver's optimal speed in each travel environment, these objective and subjec- tive factors produce a distribution of speeds on the roadway and a set of safety outcomes (e.g., number and type of crashes, number and type of injuries). Notice also that a driver's optimal speed is a latent variable. Although analysts can observe posted speed limits, measure the dis- tribution of speeds on roadways, and observe safety outcomes, analysts cannot directly observe a driver's optimal speed. However, analysts can potentially (i.e., under certain conditions) infer the empirical effect of highway safety policy on a typical driver's optimal speed by examining the effect of policy on the road's distribution of speeds. In addition to direct links between causal or determining factors, denoted by dotted-line boxes, and determined factors, denoted by
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MANAGING SPEED 282 solid-line boxes, there are indirect links that represent feedback effects. The figure shows the feedback roles that speed distributions and highway safety play on roads. Not only does speed distribution affect highway safety, but also a road's safety record has a feedback effect on the road's distribution of speeds (through its effect on indi- vidual drivers' optimal speed decisions). Further, a road's safety record will ultimately have long-term effects on highway and vehi- cle design, governmental policy, and the topic of this review--posted speed limits. Two other points about Figure C-1 are worth mentioning. First, if, as hypothesized, the indicated linkages represent a given type of road (e.g., rural Interstate highway) in the system, then similar link- ages will characterize other roads in the system (e.g., urban Interstate highways, arterials). Each road does not operate in isolation but is linked with other roads in the network, which implies that policy changes specific to one type of road are not likely to produce effects specific to that road but will influence travel behavior on other roads in the network. Traffic diversion, reflecting changes in route choice, traffic generation, reflecting latent travel demands, and spillover effects, whereby travel behavior on the affected road carries over onto other roads, are indicators of these secondary or indirect effects of a tar- geted highway policy. Second, most empirical studies of highway safety, particularly as they relate to the 65-mph (105-km/h) speed limit, fall into one of three general categories. A relatively small group of studies examines how posted speed limits are set--in practice, what factors traffic engineers take into account when posting speed limits on alternative roadways. A related question concerns the effect of changes in the posted speed limit on speed distributions and driver compliance with the posted speed limit. A second group of empirical studies examines the relationship between attributes of the speed distribution, specifi- cally average speed and speed dispersion, and highway safety. An important issue in this research is the role that average speed plays in highway safety after controlling for the statistical effect of speed dispersion. The last, and largest, set of empirical studies focuses on the effect of changes in the posted speed limit on highway safety. In Figure C-1, this is the direct link between the Speed Limit
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283 Effect of Speed Limits on Speed Distributions and Highway Safety box and the Highway Safety box (indicated by the dashes). Although a large body of research on this issue is extant, a consensus has yet to form on whether increasing speed limits, particularly on limited- access roads, deteriorates safety. Part of the current debate in this area concerns how the law affected alternative highway safety mea- sures. METHODOLOGICAL CONSIDERATIONS Empirical studies examining the effect of changes in posted speed limits on speed distributions or highway safety generally identify the primary null hypothesis as either (or at times both) of the following: H01: Increased speed limits have no effect on highway speed dis- tributions. H02: Increased speed limits have no effect on highway safety out- comes. The respective alternative hypotheses are as follows: HA1: Increased speed limits have a nonzero effect on highway speed distributions. HA2: Increased speed limits have a nonzero effect on highway safety outcomes. Depending on the analyses' objectives, there are numerous variations of these hypotheses. Highway speed limits, for example, may be referred to as having "direct" or "indirect" effects. A direct effect refers to the highway directly affected by the speed limit change. Indirect effects, on the other hand, correspond to speeds on highways whose speed limits have not changed. The direct effect of the STURA Act in 1987, for example, is the effect of a 65-mph (105- km/h) speed limit on rural Interstate speed distributions; the effect on speed distributions of all other roads is the indirect effect. If, all else being constant, relaxed speed limits on one road produce changes in the distribution of speeds on other road networks whose speed limits have not changed, there is said to be a "spillover" or "tainting"
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MANAGING SPEED 284 effect associated with the speed limit change. Often, various null hypotheses are tested on characteristics of the distribution of speeds (on the affected and unaffected roads), including average speed, speed dispersion, 85th percentile speed, and the proportion of drivers traveling above a given speed. Also of interest are hypotheses related to a speed limit's effect on highway speeds by type of vehicle, road type, time of day, and a variety of other factors that differentiate travel in the highway system. Similarly, numerous hypotheses concerning the effect of relaxed speed limits on highway safety outcomes are tested. As with highway speed distributions, these hypotheses often focus on direct and indi- rect effects, as well as the effects by vehicle type, road type, time of travel, location, alcohol consumption, and socioeconomic factors (e.g., age and gender). Although most of the attention is on fatal crashes and fatalities, some studies also identify the effect of relaxed speed limits on the severity of crashes. In the empirical literature on speed limits and their effects, three methodological approaches have typically been used to test hypothe- ses concerning the effects of changes in posted speed limits: paired comparisons, regression analysis, and time series analysis. Paired Comparisons Ideally, testing null hypotheses on the effects of altered posted speed limits would require an experimental design whereby the analyst ran- domly selects a set of homogeneous roads (i.e., roads that are physi- cally identical and that have an identical user profile) for analysis. The analyst randomly divides the set of roads into two subsets, a con- trol group and an experimental group. The analyst then alters the speed limit for those roads in the experimental group, observes speed distributions and safety outcomes, and tests whether these outcomes are statistically different from similar measures for the control group. A benefit of this methodology is that the analyst can then draw infer- ences for the population of roads under study [e.g., a 10-mph (16- km/h) increase in rural Interstate speed limits increases average rural Interstate speeds by 3.2 mph (5.1 km/h), all else held constant]. Depending on whether the null hypothesis is accepted (rejected), the
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285 Effect of Speed Limits on Speed Distributions and Highway Safety analyst concludes that the results are not (are) consistent with an altered speed limit having an effect on speed distributions and high- way safety. Often, to obtain additional insights on the extent to which the observed effect is (is not) close to 0, the analyst reports confidence intervals. Unfortunately, use of an experimental design approach to analyze the effect of speed limit changes is not generally feasible. Even assuming that one could sufficiently control for differences in a set of randomly selected roads to isolate the effect of changing speed limits, transportation agencies are reluctant to participate in such experiments for a variety of reasons. The alternative to an experimental design approach is a quasi-experimental methodology, which recognizes that speed limit changes do occur on some roads while not on others. In quasi-experimental procedures, there is a set of nonrandomly chosen roads (e.g., rural Interstate highways) on which the speed limit has changed--the experimental group--and a set of nonrandomly chosen roads on which the speed limit has not changed (e.g., rural non-Interstate highways)--the comparison group. After controlling for confounding factors that reflect heterogeneity across roads and road users, the analyst tests whether there are statistically significant differences between speed distributions and highway safety outcomes on the affected and unaffected roads. Depending on whether the null hypothesis is accepted, the analyst concludes that the change in speed limit has or has not had an effect. Paired comparison approaches come in many forms. In some cases, the analyst draws conclusions from simple comparisons of average speeds and safety outcomes for the affected and unaffected roads. Other applications use more formal testing procedures to determine whether there is a statistically significant difference in speeds or safety, or both. Further, the approach is used across roads at a given point in time (e.g., a comparison between states that did and did not increase rural Interstate speeds) or across time (a comparison of affected states before and after the speed limit change). A common approach is the use of odds ratios. Consider the fol- lowing table:
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MANAGING SPEED 286 After Before Speed Limit Speed Limit Increase Increase 65-mph (105-km/h) rural Interstate states n11 n12 55-mph (89-km/h) rural Interstate states n21 n22 where nij (i, j = 1, 2) is the number of fatal crashes in state type i and speed limit environment j. Relative to 55-mph (89-km/h) states, the odds of a fatal crash in a 65-mph (105-km/h) state prior to the speed limit increase are (n12/n22). After the speed limit increase, the odds of a fatal crash in a 65-mph state relative to 55-mph states are (n11/n21). Thus, the odds ratio is (n11/n21)/(n12/n22). If the speed limit increase in 65-mph states has no effect on fatal crashes, then the odds ratio will be 1. Alternatively, if the law significantly increased (decreased) fatal crashes, then the ratio would be greater (less) than 1. Analysts typically calculate chi-square tests and confi- dence intervals to test null hypotheses using these methods. A primary advantage of quasi-experimental methodologies based on paired comparison approaches is that very little information is needed to conduct the test. In the preceding example, four bits of information are sufficient to test the hypothesis. However, the example, as with all paired comparison analyses, has a maintained hypothesis that relating outcomes in the experimental group [i.e., fatal crashes in 65-mph (105-km/h) states] to those in the compar- ison group [i.e., 55-mph (89-km/h) states] controls for all differ- ences between the two groups in all other determining factors. Whether these techniques provide sufficient control for confound- ing factors that may influence the variable of interest and, accord- ingly, affect the test results and the associated policy implications is an important empirical issue. Analysts often stratify the sample by other variables (e.g., highway exposure, socioeconomic character- istics) to explicitly control for other determining factors. In most cases, however, the analyst uses univariate stratification, that is, stratification of the sample one variable at a time. Simultaneously stratifying by multiple variables would enable an analyst to better isolate the effect of altered speed limits, although a practical limita-
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287 Effect of Speed Limits on Speed Distributions and Highway Safety tion of multivariate stratification is that it generates many empty cells. Regression Models An alternative procedure for analyzing the effect of altered speed limits on highway speeds and safety is to develop a statistical model that not only includes the relevant policy variable but also controls for other confounding factors. This is the regression approach, whose aim is to estimate equations of the following general form: N k j xit,j Hit i it i=1 j=1 where Hit = highway outcome (e.g., fatal crashes, fatality rate, injury crashes, injury rate) for cross section i and time period t (i = 1,..., N; t = 1,..., T ), xit,j = jth explanatory variable for cross section i and time period t (i = 1,..., N; t = 1,..., T; j = 1,..., k), j = parameter reflecting the marginal effect of the jth explana- tory variable on the highway outcome ( j = 1,..., k), and = error term for cross section i and time period t (i = 1,..., N; it t = 1,...,T ). The data for this regression are cross sections over a period of time, called a pooled data set, an example of which is the number of annual fatal crashes in each state from 1970 through 1995. The formulation in the preceding equation is often referred to as a fixed effects specification because the model includes separate parameters ( i) to reflect each of the cross sections included in the analysis. This pooled data formulation is a general specification that, depending on one's data set, collapses to simpler econometric models. The two most common are time series regression models and cross-section models. In a time series regression model, N = 1 and there is a single cross section (e.g., annual nationwide fatal crashes from 1970 through 1997, or the monthly fatality rate from January
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MANAGING SPEED 348 (before the change) and 1989 (after the change), average speeds fell, but not by as much as the speed limit reduction. Total and injury crashes fell 15 and 11 percent, respectively. However, since there was not an attempt to control for changes in enforcement, public infor- mation, or other confounding factors, the extent to which the observed beneficial effects are specific to speed limit changes is unclear. Johansson's (1996) analysis of the same event sheds addi- tional light on the law's effects. On the basis of monthly data cover- ing the period January 1982 through December 1991, Johansson estimated a Poisson time series model, which controlled for serial correlation, seasonal effects, safety belt legislation, and exposure. Although citing alcohol and driver age as potential determinants of safety, Johansson excluded each from the analysis, the former because alcohol policy had not changed in Sweden during the analysis period and the latter because data were unavailable. Like Nilsson, Johansson found that the speed limit reduction was beneficial in that the num- ber of minor injuries and property damage only crashes fell. However, unlike Nilsson, Johansson found that the law had no effect on the number of fatal or serious injury crashes. In addition to the studies reported in Table C-7, there has been considerable work in Finland on the effects of speed limit changes on higher-speed roads. From the 1960s through the 1970s, Finland undertook a series of speed limit experiments, the results of which were broadly consistent with the findings reported in Table C-7 (Salusjarvi 1988). In addition, between 1987 and 1988, Finland ini- tiated a series of seasonal speed limit experiments (Finch et al. 1994) that included (a) reducing speed limits on 1,200 mi (2000 km) of roads from 62 to 50 mph (100 to 80 km/h) in the winter of 1987 [in the winter of 1988, speed limits on an additional 1,200 mi (2000 km) of roads were similarly reduced], (b) reducing speed limits on all 75- mph (120-km/h) motorways to 62 mph (100 km/h) during each win- ter period, and (c) increasing speed limits on 870 mi (1400 km) of roads from 50 to 62 mph (80 to 100 km/h) in the summers of 1988 and 1989, half each summer. Findings from these experiments indi- cated that reductions (increases) in speed limits were associated with decreases (increases) in average speed and speed dispersion. In addi- tion, there was a positive correlation between the direction of the
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349 Effect of Speed Limits on Speed Distributions and Highway Safety speed limit change and the effect on crashes. The extent to which other factors may have affected these results, however, is not known. Summary This group of international studies yields the following insights in setting speed limits: ∑ For nonlimited-access urban roads, local speed limit zones were successful in reducing speeds and crashes when implemented with complementary policies such as public information campaigns, greater enforcement, and engineering measures. However, part of the improvement in urban speed zones was due to reduced volumes, and there was little analysis of where the traffic went, that is, the policy's effects outside the zones. Limited evidence suggests that the sys- temwide effects may be zero. ∑ For nonlimited-access roads, there is limited evidence that enforcement is an important determinant of safety. This is consistent with more recent work by Elvik (1997), who used meta-analysis to explore the effect of automated speed enforcement on traffic safety. On the basis of work in Norway, Germany, Sweden, England, the Netherlands, and Australia, Elvik estimated that automated speed enforcement reduced injury crashes by 17 percent. ∑ On higher-speed roads, the international work is broadly con- sistent with that in the United States. With little control for other confounding factors, higher (lower) speed limits generally lead to higher (lower) speeds and more (fewer) crashes. The absolute change in speed is less than the absolute change in the speed limit. As a final point, there has been limited international work on the role of enforcement in highway safety. CONCLUSIONS AND AREAS FOR FUTURE RESEARCH The purpose of this review is to provide an overview of recent empir- ical research on the effect of changes in posted speed limits on speed distributions and highway safety. To provide a contextual basis for the
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MANAGING SPEED 350 review, the conceptual framework and statistical methodologies com- mon to many of the reviewed studies were summarized. Recent domestic and international research on the effects of posted speed limits was reviewed. What implications does this work have for determining speed limit policies? This section focuses on two areas. First, notwithstanding the often significant differences in geographic and temporal scope as well as methodological approach, can anything specific be said about the effect of alternative speed limits? Second, although there has been a significant amount of work on speed limits, are there important gaps in existing research that future research should address? Conclusions With regard to specific implications for speed limit policy, existing studies provide support for the following conclusions: ∑ Speed limits must be perceived by the traveling public as "rea- sonable," that is, as consistent with the enforced traffic conditions experienced by the typical driver. To the extent that this is not true, more drivers will be noncompliant, which could compromise high- way safety. ∑ On nonlimited-access roads, speed limit changes of 5 to 10 mph (8 to 16 km/h) "for cause" (e.g., based on crash experience, increased pedestrian traffic, more businesses) will likely have little effect on speed distribution and highway safety. ∑ For nonlimited-access roads, the greatest effect on speed distri- butions and highway safety occurs when a speed zone is implemented as part of an urban planning policy that simultaneously introduces complementary measures (e.g., greater enforcement, public informa- tion campaigns, engineering measures) to slow drivers down. ∑ For limited-access roads, a 10-mph (16-km/h) increase in RI speed limits from 55 mph (89 km/h) to 65 mph (105 km/h) has gen- erally increased nationwide average speeds by less than 4 mph (6 km/h) and increased nationwide speed dispersion by less than 1 mph (2 km/h). But there is considerable cross-state variation in these effects.
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351 Effect of Speed Limits on Speed Distributions and Highway Safety ∑ For limited-access roads, both average speed and speed disper- sion are inversely related to highway safety in general and fatalities in particular. The effect of speed dispersion is most important for RI roads. Drivers traveling in the top 15th percentile appear to compro- mise highway safety more than those traveling in the lowest 15th percentile. ∑ The increase in RI speed limits from 55 to 65 mph (89 to 105 km/h) has generated mixed results with respect to its effect on non- rural Interstate roads whose speed limits remained the same. ∑ Although increasing RI speed limits from 55 to 65 mph (89 to 105 km/h) produces highway fatality distribution effects, the evi- dence is consistent with (at least) a zero net systemwide effect. ∑ Evidence indicates that speed adaptation occurs, but the effect appears to be small, suggesting that the highway safety effect will also be small. ∑ The few studies that have analyzed the effects of alternative enforcement levels indicate that traffic enforcement is an important determinant of highway safety. Areas for Future Research There are a number of areas in which additional research could sig- nificantly contribute to an understanding of the effect of speed lim- its on speed distributions and highway safety. These areas are discussed in the remainder of this section. Data Collection Common among many, if not most, of the studies reviewed in this paper was either the absence of or insufficient control of confound- ing factors that affect speed distributions and highway safety. Studies that do not adequately control for these related factors will likely be biased and lead to improper inferences. Although in some cases it is not possible to obtain additional information, in most cases there is at least some possibility of improved specification to better isolate the true effect of speed limit changes. An important omission in most analyses is enforcement, yet the few studies that
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MANAGING SPEED 352 include this variable find that enforcement is an important determi- nant of highway safety. Much of the data on speed distributions is highly aggregated. Future research should aim to generate speed distribution data that are better aligned with the environment in which the crash occurred (e.g., late-night crashes on rural two-lane roads). Further, free-flow data are often collected to generate measures of speed distribution, but how useful are these data in determining highway safety out- comes in non-free-flow periods (i.e., during congested periods)? This is an open question. Also, since travel occurs on a network of inter- connected roads, are existing aggregate measures of highway safety reasonable speed distribution proxies for different functional road types, or does use of these measures cause important biases? Methodology As noted earlier, existing research on speed limits generally uses uni- variate classification procedures, regression analysis, or ARIMA time series models. Multivariate classification models are rarely used to analyze the effects of highway safety. Among simple regression mod- els, there is often a surprising lack of diagnostics and, if necessary, correction for common statistical problems (e.g., serial correlation in time series analysis). The relationship between speed limits, speed distribution, and highway safety would likely be better understood if researchers experimented more with more general models of highway safety or alternative methodologies. The infrequency of fatal crashes and fatal- ities is amenable to Poisson regression techniques, yet these methods have been used less frequently than might be expected. In addition, there has been little work on developing and estimating simultaneous frameworks to capture the interaction between the demand for road space and highway safety. A third area is the use of various probabil- ity models. Ordered probit (Greene 1997), for example, is one methodology that could be used to examine the effect of speed lim- its on crash severity. To date, most work on speed limits applies a particular methodol- ogy to a particular data set (e.g., regression analysis on cross-section
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353 Effect of Speed Limits on Speed Distributions and Highway Safety data from California or ARIMA models on monthly time series data from Michigan). Given the availability of state and national data and computing technology, developing multiple data sets from a given base of information is relatively easy and would enable researchers to make different passes at the same underlying information. Using data from 1980 through 1996 for a given state that raised its speed limit in 1987, for example, would an aggregate regression model based on annual data produce similar effects of the law as would an ARIMA model for that state based on monthly data over the same time period? Would the effects be similar to those generated from uni- variate classification models that compare crash rates between 1980 and 1986 with those between 1987 and 1996? Such analyses may produce dramatically different estimates of the law's effects and, in so doing, identify potentially fruitful areas for further research. Unresolved Research Issues Following are a number of issues on which some research exists but on which further research is warranted. ∑ How robust is the empirical finding that a 10-mph (16-km/h) increase in RI speed limits leads to an increase in average speed and speed dispersion, respectively, of around 4 mph (6 km/h) and under 1 mph (2 km/h), particularly if there is greater control of other deter- mining factors? This is particularly important given the recent increase in speed limits to 70 mph (113 km/h) that some states implemented following passage of the National Highway System Designation Act in 1995. ∑ How important is enforcement in determining speed distribu- tion properties and highway safety when speed limits change? ∑ Although there is some information on the relationship between average speed, speed dispersion, and highway safety, much additional work is needed to improve the understanding of these relationships. Are average speed and speed dispersion negatively related? If so, does this relationship hold at all speeds or is there a nonlinear (or inde- pendent) relationship between these measures at other speeds? A related question is whether speed dispersion is only important on
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MANAGING SPEED 354 high-speed RI highways, as suggested in some research, and less important on lower-speed nonlimited-access roads. ∑ What level of aggregation is appropriate for accurately charac- terizing the relationship between speed distribution and highway safety? ∑ More information is needed on how speed limit laws affect the spectrum of crashes, injury related as well as noninjury related. Although nonfatal crash and nonfatal injury data are less reliable, this does not justify ignoring all the available information. ∑ Additional research is needed on the spillover effects associated with speed limit changes for both limited- and nonlimited-access roads. The literature on this issue is mixed, and there is no consen- sus on either the direction or the magnitude of the effect. Better models are needed to identify the linkages that produce spillover effects. ∑ In addition to hypothesized spillover effects, speed limit changes, particularly on limited-access high-speed roads, are likely to produce traffic generation effects. It is not known whether these effects exist and, if so, how important they are in determining the highway safety effects of speed limit changes. ∑ Sufficient evidence exists to question whether the net effect on highway safety of speed limit laws is to deteriorate highway safety. More research is needed on the distribution effects of speed limit laws to evaluate their net effects on highway safety. ∑ Reducing the impact speed of vehicles can have a significant effect on pedestrian crashes and fatalities. For the United States there is little information on the extent to which pedestrian fatalities are an important consideration when speed limits change. REFERENCES ABBREVIATIONS FHWA Federal Highway Administration NHTSA National Highway Traffic Safety Administration TRB Transportation Research Board Anderson, R. W. G., et al. 1997. Vehicle Travel Speeds and the Incidence of Fatal Pedestrian Crashes. Accident Analysis and Prevention, Vol. 29, pp. 667≠674.
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