Quasi-induced exposure revisited

Considerable attention is still given to developing and using alternate methods for determining exposure for calculating highway accident rates. A quasi-induced method of measuring exposure developed in the late 1960s is reexamined and found to be promising for determining relative accident involvement rates. A new empirical investigation is offered as the first step in verifying that the characteristics of the "innocent victim" in two-vehicle highway accidents represent a random sample of the driver-vehicle combinations present on the highway system under specified conditions. Quasi-induced exposure estimates are shown to be, at a minimum, consistent and reproducible.     

Effects on road safety of new urban arterial roads

This paper presents an evaluation of the effects on road safety of new urban arterial roads in Oslo, Norway, and a synthesis of evidence from similar studies that have evaluated the safety effects of new urban arterial roads in other cities. A before-and-after study was made of four urban arterial road projects in Oslo. The study controlled for general accident trends in Oslo and for regression-to-the-mean. A statistically non-significant reduction of 9% in the number of injury accidents was found for all four projects combined. The effects on safety of new urban arterial roads were found to vary, depending on whether a new arterial road was built, or an existing arterial road upgraded by means of lane additions and reconstruction of junctions to interchanges. New arterial roads tend to induce more traffic, which tends to offset the benefits of a lower accident rate on the new roads. The results for other cities are very consistent with those for Oslo. For a total of seven cases in which new arterial roads were built, a statistically non-significant reduction of 1% in the number of injury accidents was found. Two cases that involved lane additions and converting at-grade junctions to interchanges resulted in a mean accident reduction of 51%, which was highly significant. On the average, the nine arterial road projects from which evidence was summarised resulted in a net induced traffic of 16%, and a net reduction in accident rate (accidents per million vehicle kilometres) of 18%. These effects almost cancel each other, leading to a very small net change in the expected number of accidents.     

Measuring the safety effect of road measures at junctions

The effects of road measures implemented at main road junctions in 1984–1986 were studied on the basis of police-reported accidents that had occurred at the junctions in 1983–1987. The data were obtained from an extensive junction inventory performed by the District Offices of the Finnish National Road Administration in 1988. A total of 325 three-arm and 298 four-arm junctions were included in the study. The number and type of accidents were studied at each junction both before and after the implementation of the measure. The effects of the measure were determined by comparing the observed number of accidents after the measure with the number that would have been expected to occur at the junction if the measure had not been implemented. The expected number of accidents was calculated on the basis of the observed number of accidents in the before period and the expected number of accidents as predicted by an accident model, utilizing at the same time the information on the variation of the number of accidents at similar junctions revealed by the model. Likelihood functions determined for the effects of the measures were used for studying the accuracy of the estimates of the effects. The likelihood functions can also be utilized later when new before-and-after studies are performed. Road lighting, stop signs, signal control, and lowering of the speed limit value were found to decrease the number of accidents. Through-flow junction widenings, additional lanes for turning vehicles, and road widenings, however, did not seem to affect the safety at junctions to any marked extent. If we had not accounted for the regression-to-the-mean effect, these measures would have seemed to have a positive effect on safety. The study method also enabled us to quantify the regression-to-the-mean effect. The magnitude of the regression effect was on average 20%, i.e. the number of accidents would have decreased by 20% at the junctions studied even if the measures had not been implemented. The magnitude of the regression-to-the-mean varied greatly between the different measures.     

Establishing relationships between accidents and flows at urban priority road junctions

This papers explores the effects of traffic stream flows on accident potential at urban priority-controlled (i.e. unsignalised), four-arm junctions. Forty-three urban priority junctions were carefully selected so that other than flow parameters expected to influence accident potential have similar values at the junctions considered. Using traffic accident data for five-year time periods and the corresponding 24-hour flows, a new exposure index is proposed consisting of an expression of the flows of the junction's interacting traffic streams. The regression of this exposure index on the expected number of accidents per year at junctions of the type examined yields a quite satisfactory correlation coefficient, better than those achieved when other proposed indices are used.     

The effects on accidents of studded tires and laws banning their use: a meta-analysis of evaluation studies

Studies that have evaluated the effects on accidents of studded tires are reviewed. There are two types of evaluation studies with respect to the safety effects of studded tires: (1) Studies of the effect on automobile accident rates of using studded tires; and (2) studies of the effect on accidents of banning the use of studded tires. The results of studies of the effects of studded tires on automobile accident rates are found to vary substantially, depending on the quality of the study design. Recent studies employing multivariate techniques of analysis to control for confounding factors, attribute to studded tires minor declines in automobile accident rates of 5% for snow- or ice-covered roads, 2% for bare roads and 4% for all road surfaces combined. The results of these studies are consistent with the most recent estimates of the effect on accidents of banning studded tires. It is concluded that studded tires probably confer a slight safety benefit during wintertime.     

A crash-prediction model for multilane roads

Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation. Goodness-of-fit was measured by means of both the explained fraction of total variation and the explained fraction of systematic variation. The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable. The candidate set of explanatory variables was: length (L), curvature (1/R), annual average daily traffic (AADT), sight distance (SD), side friction coefficient (SFC), longitudinal slope (LS) and the presence of a junction (J). Separate prediction models for total crashes and for fatal and injury crashes only were considered. For curves it is shown that significant variables are L, 1/R and AADT, whereas for tangents they are L, AADT and junctions. The effect of rain precipitation was analysed on the basis of hourly rainfall data and assumptions about drying time. It is shown that a wet pavement significantly increases the number of crashes. The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options. Thus this research may represent a point of reference for engineers in adjusting or designing multilane roads.     

Predicting wet weather accidents

The development of a wet weather safety index, WWSI_e, is presented. WWSI_e is an empirical formulation based on prediction equations for wet accident rates. These equations were derived by multiple regression techniques from a survey of 68 highway segments in Texas. These segments covered a range of wet accident rates from zero to 40 accidents per year per mile. In general, much higher values of wet accident rates are observed in urban areas than in rural areas. Also, the sensitivity to pavement skid resistance is much higher in urban than in rural areas. The findings and developments reported warrant a restructuring of many state programs to reduce wet weather accidents. The Wet Weather Safety Index and associated equations represent a practical method of predicting wet accident rates as a function of traffic, road geometric, and pavements surface characteristics. These predictive equations may be cautiously used to determine accident reduction due to specific remedial measures. These new developments can be integrated into a comprehensive plan to reduce wet weather accidents, a plan which should greatly increase the effectiveness of those resources devoted to this objective.     

A study of factors affecting highway accident rates using the random-parameters tobit model

A large body of previous literature has used a variety of count-data modeling techniques to study factors that affect the frequency of highway accidents over some time period on roadway segments of a specified length. An alternative approach to this problem views vehicle accident rates (accidents per mile driven) directly instead of their frequencies. Viewing the problem as continuous data instead of count data creates a problem in that roadway segments that do not have any observed accidents over the identified time period create continuous data that are left-censored at zero. Past research has appropriately applied a tobit regression model to address this censoring problem, but this research has been limited in accounting for unobserved heterogeneity because it has been assumed that the parameter estimates are fixed over roadway-segment observations. Using 9-year data from urban interstates in Indiana, this paper employs a random-parameters tobit regression to account for unobserved heterogeneity in the study of motor-vehicle accident rates. The empirical results show that the random-parameters tobit model outperforms its fixed-parameters counterpart and has the potential to provide a fuller understanding of the factors determining accident rates on specific roadway segments.     

Estimation of the effects of countermeasures on different types of accidents in the presence of regression effects

In an earlier paper [Danielsson, 1986], we studied the problem of estimating the safety effect of a countermeasure on the expected number of accidents at road junctions when high-accident sites are selected for the study. Often, however, the countermeasure leads to varying effectiveness on different types of accidents. This paper is a generalization in that we estimate effects of countermeasures for each type of accident. A major result, for which empirical support is provided, is that the expected regression effect is the same for all types of accidents.