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.     

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.     

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.     

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.     

Quasi-induced exposure: The choice of exposure metrics

The quasi-induced exposure method is widely used to estimate exposure and risks of different groups of drivers and vehicles. Essentially, this method assumes that non-at-fault or passive parties in two-vehicle collisions represent a random sample of the populations on the road. Most previous works have used the whole sample of collisions to estimate exposure.There has been some concern about possible biases in quasi-induced estimates. In this paper, we argue that (1) biases are mainly due to differences in accident avoidance abilities, speeds and injury risks, and (2) because the influence of these three factors on the probability of being non-at-fault is not the same for every crash type, differences may arise among non-at-fault populations, in which case some crash types would provide a more accurate estimate of exposure than others.We explore the direction of biases due to speed, accident avoidance ability and injury risk in four accident types: accidents between vehicles travelling on different lanes in two-way, two-lane undivided roads; accidents between vehicles travelling on different lanes on multilane roads; intersection accidents; and accidents between vehicles travelling on the same lane. Our analysis shows that more research would be needed concerning the effect of speed on head-on crashes on undivided roads, and crashes on multilane roads.     

Overdispersion in modelling accidents on road sections and in empirical bayes estimation

In multivariate statistical models of road safety one usually finds that the accident counts are ‘overdispersed’. The extent of the overdispersion is itself subject to estimation. It is shown that the assumption one makes about the nature of overdispersion will affect the maximum likelihood estimates of model parameters. If one assumes that the same overdispersion parameter applies to all road sections in the data base, then, the maximum likelihood estimate of parameters will be unduly influenced by very short road sections and insufficiently influenced by long road sections. The same assumption about the overdispersion parameter also leads to an inconsistency when one estimates the safety of a road section by the Empirical Bayes method. A way to avoid both problems is to estimate an overdispersion parameter (φ) that applies to a unit length of road, and to set the overdispersion parameter for a road section of length L to φL. How this would change the estimates of regression parameters for road section models now in use requires examination. Safety estimation by the Empirical Bayes method is altered substantially.     

The covariance between the number of accidents and the number of victims in multivariate analysis of accident related outcomes

In this study some statistical issues involved in the simultaneous analysis of accident related outcomes of the road traffic process are investigated. Since accident related outcomes like the number of victims, fatalities or accidents show interdependencies, their simultaneous analysis requires that these interdependencies are taken into account. One particular interdependency is the number of fatal accidents that is always smaller than the number of fatalities as at least one fatality results from a fatal accident. More generally, when the number of accidents increases, the number of people injured as a result of these accidents will also increase. Since dependencies between accident related outcomes are reflected in the variance-covariance structure of the outcomes, the main focus of the present study is on establishing this structure. As this study shows it is possible to derive relatively simple expressions for estimates of the variances and covariances of (logarithms of) accidents and victim counts. One example reveals a substantial effect of the inclusion of covariance terms in the estimation of a confidence region of a mortality rate. The accuracy of the estimated variance-covariance structure of the accident related outcomes is evaluated using samples of real life accident data from The Netherlands. Additionally, the effect of small expected counts on the variance estimate of the logarithm of the counts is investigated.     

Estimating transport fatality risk from past accident data

This paper examines the statistical properties of estimates of fatal accident rates, mean fatalities per accident, and fatality rates when these estimates are based on past accident data. The statistical properties are illustrated by two long-term transport fatal accident datasets from Great Britain, the principal one for railways and the other for roads, chosen to provide a statistical contrast. In both modes, the accident rates have fallen substantially over the long term. Two statistical estimates of current accident and fatality rates are presented for each dataset, one based only on recent data and the other based on estimates of long-term trends. The trend-based estimate is preferred for train accidents because this makes maximum use of the limited and variable data; the recent data are preferred for road accidents because this avoids unnecessary dependence on modelling the trends. For train accidents, the estimated fatality rate based on past accidents is compared with an estimate produced by the railway industry using a risk model. The statistical estimate is less than half the industry's estimate, and the paper concludes that the statistical estimate is to be preferred.     

Prediction of accidents at full green and green arrow traffic lights in Switzerland with the aid of configuration-specific features

In this study it was endeavored to predict full green and green arrow accidents at traffic lights, using configuration-specific features. This was done using the statistical method known as Poisson regression. A total of 45 sets of traffic lights (criteria: in an urban area, with four approach roads) with 178 approach roads were investigated (the data from two approach roads was unable to be used). Configuration-specific features were surveyed on all approach roads (characteristics of traffic lanes, road signs, traffic lights, etc.), traffic monitored and accidents (full green and green arrow) recorded over a period of 5 consecutive years. It was demonstrated that only between 23 and 34% of variance could be explained with the models predicting both types of accidents. In green arrow accidents, the approach road topography was found to be the major contributory factor to an accident: if the approach road slopes downwards, the risk of a green arrow accident is approximately five and a half times greater (relative risk, RR = 5.56) than on a level or upward sloping approach road. With full green accidents, obstructed vision plays the major role: where vision can be obstructed by vehicles turning off, the accident risk is eight times greater (RR = 8.08) than where no comparable obstructed vision is possible. From the study it emerges that technical features of traffic lights are not able to control a driver's actions in such a way as to eradicate error. Other factors, in particular the personal characteristics of the driver (age, sex, etc.) and accident circumstances (lighting, road conditions, etc.), are likely to make an important contribution to explaining how an accident occurs.     

Models relating traffic safety with road environment and traffic flows on arterial roads in Addis Ababa

This paper presents the study carried out to develop accident predictive models based on the data collected on arterial roads in Addis Ababa. Poisson and negative binomial regression methods were used to relate the discrete accident data with the road and traffic flow explanatory variables. Significant accident predictive models were found with a number of significant explanatory variables. The results show that the existing inadequate road infrastructure and poor road traffic operations are the potential contributors of this ever-growing challenge of the road transport in Addis Ababa. The results also indicate that improvements in roadway width, pedestrian facilities, and access management are effective in reducing road traffic accidents.     

Road accidents caused by drivers falling asleep

About 29600 Norwegian accident-involved drivers received a questionnaire about the last accident reported to their insurance company. About 9200 drivers (31%) returned the questionnaire. The questionnaire contained questions about sleep or fatigue as contributing factors to the accident. In addition, the drivers reported whether or not they had fallen asleep some time whilst driving. and what the consequences had been. Sleep or drowsiness was a contributing factor in 3.9% of all accidents, as reported by drivers who were at fault for the accident. This factor was strongly over-represented in night-time accidents (18.6%), in running-off-the-road accidents (8.3%), accidents after driving more than 150 km on one trip (8.1%), and personal injury accidents (7.3%). A logistic regression analysis showed that the following additional factors made significant and independent contributions to increasing the odds of sleep involvement in an accident: dry road, high speed limit, driving one's own car, not driving the car daily, high education, and few years of driving experience. More male than female drivers were involved in sleep-related accidents, but this seems largely to be explained by males driving relatively more than females on roads with high speed limits. A total of 10% of male drivers and 4% of females reported to have fallen asleep while driving during the last 12 months. A total of 4% of these events resulted in an accident. The most frequent consequence of falling asleep--amounting to more than 40% of the reported incidents--was crossing of the right edge-line before awaking, whereas crossing of the centreline was reported by 16%. Drivers' lack of awareness of important precursors of falling asleep--like highway hypnosis, driving without awareness, and similar phenomena--as well as a reluctance to discontinue driving despite feeling tired are pointed out as likely contributors to sleep-related accidents. More knowledge about the drivers' experiences immediately preceding such accidents may give a better background for implementing effective driver warning systems and other countermeasures.     

Effects of road geometry and traffic volumes on rural roadway accident rates

This paper revisits the question of the relationship between rural road geometric characteristics, accident rates and their prediction, using a rigorous non-parametric statistical methodology known as hierarchical tree-based regression. The goal of this paper is twofold: first, it develops a methodology that quantitatively assesses the effects of various highway geometric characteristics on accident rates and, second, it provides a straightforward, yet fundamentally and mathematically sound way of predicting accident rates on rural roads. The results show that although the importance of isolated variables differs between two-lane and multilane roads, 'geometric design' variables and pavement condition' variables are the two most important factors affecting accident rates. Further, the methodology used in this paper allows for the explicit prediction of accident rates for given highway sections, as soon as the profile of a road section is given.     

Bus accident analysis of routes with/without bus priority

This paper summarises findings on road safety performance and bus-involved accidents in Melbourne along roads where bus priority measures had been applied. Results from an empirical analysis of the accident types revealed significant reduction in the proportion of accidents involving buses hitting stationary objects and vehicles, which suggests the effect of bus priority in addressing manoeuvrability issues for buses. A mixed-effects negative binomial (MENB) regression and back-propagation neural network (BPNN) modelling of bus accidents considering wider influences on accident rates at a route section level also revealed significant safety benefits when bus priority is provided. Sensitivity analyses done on the BPNN model showed general agreement in the predicted accident frequency between both models. The slightly better performance recorded by the MENB model results suggests merits in adopting a mixed effects modelling approach for accident count prediction in practice given its capability to account for unobserved location and time-specific factors. A major implication of this research is that bus priority in Melbourne's context acts to improve road safety and should be a major consideration for road management agencies when implementing bus priority and road schemes.