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.     

An assessment of the effect of driver age on traffic accident involvement using log-linear models

Statistical models were developed to help understand the relationship between the driver age and several important accident-related factors and circumstances such as injury severity, collision types, average daily traffic (ADT), roadway character, speed ratio, alcohol involvement, and accident location. By using techniques of categorical analysis on the 1994 and 1995 Florida accident database, four log-linear models with three variables in each model with all possible two-way interactions were developed. In order to compare the differences in response between the age groups and a particular accident-related variable, odds multipliers were computed. The effects of age and accident-related factors were examined, and interactions among them were considered. The results indicated significant relationships between the driver age and ADT, injury severity, manner of collision, speed, alcohol involvement, and roadway character. The findings' contribution to the understanding of the effect of age on accident involvement is addressed. A discussion of how log-linear and logit modeling with estimation of `odds multipliers' may contribute to traffic safety studies is also provided.     

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.     

Traffic accidents involving fatigue driving and their extent of casualties

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a “silent killer.” Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006–2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.     

Identification of differences between rural and urban safety cultures

The prevailing risk of traffic fatalities is much larger in rural areas compared to urban areas. A number of explanations have been offered to explain this including road design, emergency medical service proximity, and human factors. This research explored the potential contribution of rural driver attitudes that may underlie the increased fatal crash risk in rural environments. This analysis examined differences between rural and urban drivers in terms of self-reported risk taking for driving behaviors associated with fatal crashes and attitudes toward safety interventions using a large-scale survey. The results suggested that rural drivers engage in riskier behavior, such as not wearing seatbelts, because they have lower perceptions of the risks associated with such behaviors. Results also suggested that vehicle type (e.g., pickup trucks versus passenger vehicles) may be related to seatbelt compliance and frequency of driving under the influence of alcohol. Rural drivers perceived the utility of government-sponsored traffic safety interventions to be lower than their urban counterparts. This study provides insights into the role of the human factor in rural fatal crashes and provides policy suggestions for developing safety interventions that are designed with respect to the psychosocial factors that define the rural culture.     

Crash reduction following installation of centerline rumble strips on rural two-lane roads

Rural two-lane roads generally lack physical measures such as wide medians or barriers to separate opposing traffic flows. As a result, a major crash problem on these roads involves vehicles crossing the centerline and either sideswiping or striking the front ends of opposing vehicles. These types of opposing-direction crashes account for about 20% all fatal crashes on rural two-lane roads and result in about 4500 fatalities annually in the US. The present study evaluated a potential engineering countermeasure for such crashes—installation of rumble strips along the centerlines of undivided rural two-lane roads to alert distracted, fatigued, or speeding motorists whose vehicles are about to cross the centerlines and encroach into opposing traffic lanes. Data were analyzed for approximately 210 miles of treated roads in seven states before and after installation of centerline rumble strips. An empirical Bayes before–after procedure was employed to properly account for regression to the mean while normalizing for differences in traffic volume and other factors between the before and after periods. Overall results indicated significant reductions for all injury crashes combined (14%, 95% confidence interval (95% CI)=5–23%) as well as for frontal and opposing-direction sideswipe injury crashes (25%, 95% CI=6–44%)—the primary target of centerline rumble strips. In light of their effectiveness and relatively low installation costs, consideration should be given to installing centerline rumble strips more widely on rural two-lane roads to reduce the risk of frontal and opposing-direction sideswipe crashes.     

Investigation of pedestrian crashes on two-way two-lane rural roads in Ethiopia

Understanding pedestrian crash causes and contributing factors in developing countries is critically important as they account for about 55% of all traffic crashes. Not surprisingly, considerable attention in the literature has been paid to road traffic crash prediction models and methodologies in developing countries of late. Despite this interest, there are significant challenges confronting safety managers in developing countries. For example, in spite of the prominence of pedestrian crashes occurring on two-way two-lane rural roads, it has proven difficult to develop pedestrian crash prediction models due to a lack of both traffic and pedestrian exposure data. This general lack of available data has further hampered identification of pedestrian crash causes and subsequent estimation of pedestrian safety performance functions. The challenges are similar across developing nations, where little is known about the relationship between pedestrian crashes, traffic flow, and road environment variables on rural two-way roads, and where unique predictor variables may be needed to capture the unique crash risk circumstances. This paper describes pedestrian crash safety performance functions for two-way two-lane rural roads in Ethiopia as a function of traffic flow, pedestrian flows, and road geometry characteristics. In particular, random parameter negative binomial model was used to investigate pedestrian crashes. The models and their interpretations make important contributions to road crash analysis and prevention in developing countries. They also assist in the identification of the contributing factors to pedestrian crashes, with the intent to identify potential design and operational improvements.     

Influence of deficiencies in traffic control devices in crashes on two-lane rural roads

One of the main objectives of all public administrations is reducing traffic crashes. To this end, Road Safety Inspections (RSI) stand out as a key measure. Signaling roads is one of the foremost tasks of RSI. A road that is improperly or poorly signaled can lead to incorrect placement or maneuvers of vehicles and ambiguous situations that can increase the risk of crashes. This paper analyses the relationship between road crashes in two-lane rural highways and certain deficiencies in signaling. The results show that deficiencies such as ⿿incomplete removal of road works markings⿿ or ⿿no guide sign or in incorrect position⿿ are the ones associated with a higher probability of crashes in two-lane rural highways. In view of these results, governmental agencies should verify that the original conditions of a highway are re-established after any construction work is completed. They should also continuously follow up on the signaling of this type of highway in order to maintain optimal conditions.     

Effects of transverse rumble strips on safety of pedestrian crosswalks on rural roads in China

The primary objective of this study is to evaluate the impacts of transverse rumble strips in reducing crashes and vehicle speeds at pedestrian crosswalks on rural roads in China. Using crash data reported at 366 sites, the research team conducted an observational before-after study using a comparison group and the Empirical Bayesian (EB) method to evaluate the effectiveness of transverse rumble strips in reducing crashes at pedestrian crosswalks. It was found that transverse rumble strips may reduce expected crash frequency at pedestrian crosswalks by 25%. The research team collected more than 15,000 speed observations at 12 sites. The speed data analysis results show that transverse rumble strips significantly reduce vehicle speeds in vicinity of pedestrian crosswalks on rural roads with posted speed limits of 60 km/h and 80 km/h. On average, the mean speed at pedestrian crosswalks declined 9.2 km/h on roads with a speed limit of 60 km/h; and 11.9 km/h on roads with a speed limit of 80 km/h. The 85th percentile speed declined 9.1 km/h on roads with a speed limit of 60 km/h; and 12.0 km/h on roads with a speed limit of 80 km/h. However, the speed reduction impacts were not found to be statistically significant for the pedestrian crosswalk on the road with a speed limit of 40 km/h. The study also looked extensively at the influence area of transverse rumble strips on rural roads. Speed profiles developed in this study show that the influence area of transverse rumble strips is generally less than 0.3 km.     

Transferability of calibrated microsimulation model parameters for safety assessment using simulated conflicts

Several studies have investigated the relationship between field-measured conflicts and the conflicts obtained from micro-simulation models using the Surrogate Safety Assessment Model (SSAM). Results from recent studies have shown that while reasonable correlation between simulated and real traffic conflicts can be obtained especially after proper calibration, more work is still needed to confirm that simulated conflicts provide safety measures beyond what can be expected from exposure. As well, the results have emphasized that using micro-simulation model to evaluate safety without proper model calibration should be avoided. The calibration process adjusts relevant simulation parameters to maximize the correlation between field-measured and simulated conflicts.The main objective of this study is to investigate the transferability of calibrated parameters of the traffic simulation model (VISSIM) for safety analysis between different sites. The main purpose is to examine whether the calibrated parameters, when applied to other sites, give reasonable results in terms of the correlation between the field-measured and the simulated conflicts. Eighty-three hours of video data from two signalized intersections in Surrey, BC were used in this study. Automated video-based computer vision techniques were used to extract vehicle trajectories and identify field-measured rear-end conflicts. Calibrated VISSIM parameters obtained from the first intersection which maximized the correlation between simulated and field-observed conflicts were used to estimate traffic conflicts at the second intersection and to compare the results to parameters optimized specifically for the second intersection. The results show that the VISSIM parameters are generally transferable between the two locations as the transferred parameters provided better correlation between simulated and field-measured conflicts than using the default VISSIM parameters. Of the six VISSIM parameters identified as important for the safety analysis, two parameters were directly transferable, three parameters were transferable to some degree, and one parameter was not transferable.     

Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.     

The development and application of a proxy measure of alcohol-related traffic crashes for rural communities

Context

To date, no studies have adequately examined whether community-level, rather than individual-level, characteristics, are associated with high rates of alcohol-related traffic crashes (ARTC).

Objective

This study aims to identify a proxy measure of ARTC most appropriate to rural communities in New South Wales (NSW), Australia, and to identify community characteristics that predict higher rates of ARTC.

Methods

ARTC (2001–2007) incident data from 20 rural communities in NSW were obtained. Cost data were applied to take account of ARTCs of different severity.

Results

The method used to define a proxy measure of ARTCs for NSW generally was found to be inadequate when applied to local communities within NSW: specifically, two time periods were found to be alcohol-related for local communities only and seven time periods were found to be non-alcohol-related for local communities only. Applying a community-specific proxy measure of ARTCs to 20 communities identified substantial variation in ARTC cost-ratios, ranging from 1.20 to 0.15. Higher cost-ratios were statistically significantly predicted by the proportion of residents who were male and less than 25 years.

Conclusions

Proxy measures of ARTCs represent an ideal method of utilising routinely collected data to identify specific types of ARTCs that are most relevant to a defined community, identify community-specific factors associated with higher rates of ARTCs and measure the impact of interventions tailored to those risk factors. Such measures ought to be community-specific because these results suggest national or provincial-level definitions are not necessarily directly applicable to local communities. These results show substantial variability between communities in their rates of ARTC and identify communities with higher proportions of young males as being at increased risk.     

Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents

Compliance to standardized highway design criteria is considered essential to ensure roadway safety. However, for a variety of reasons, situations arise where exceptions to standard-design criteria are requested and accepted after review. This research explores the impact that such design exceptions have on the frequency and severity of highway accidents in Indiana. Data on accidents at carefully selected roadway sites with and without design exceptions are used to estimate appropriate statistical models of the frequency and severity of accidents at these sites using recent statistical advances with mixing distributions. The results of the modeling process show that presence of approved design exceptions has not had a statistically significant effect on the average frequency or severity of accidents - suggesting that current procedures for granting design exceptions have been sufficiently rigorous to avoid adverse safety impacts. However, the findings do suggest that the process that determines the frequency of accidents does vary between roadway sites with design exceptions and those without.     

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analysed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.     

The effects of speed enforcement with mobile radar on speed and accidents. An evaluation study on rural roads in the Dutch province Friesland

In an evaluation study, the effects of targeted speed enforcement on speed and road accidents were assessed. Enforcement was predominantly carried out by means of mobile radar and focused on rural non-motorway roads. Information and publicity supported the enforcement activities. The evaluation covered a period of 5 years of enforcement. The speed data of these 5 years and the year preceding the enforcement project showed a significant decrease in mean speed and the percentage speed limit violators over time. The largest decrease was found in the first year of the enforcement project and in the fourth year of the project, when the enforcement effort was further intensified. There were similar decreases in speeding at both the enforced roads and at the nearby comparison roads that were not subjected to the targeted speed enforcement project, which may be explained by spillover effects. The best estimate for the safety effect of the enforcement project is a reduction of 21% in both the number of injury accidents and the number of serious casualties. This was based on comparison between the number of accidents/casualties during the enforcement project (5 years) and and the 8 preceding years on the enforced roads and at all other roads outside urban areas in the same region.     

A system of safety management practices and worker engagement for reducing and preventing accidents: An empirical and theoretical investigation

Objective

The overall research objective was to theoretically and empirically develop the ideas around a system of safety management practices (ten practices were elaborated), to test their relationship with objective safety statistics (such as accident rates), and to explore how these practices work to achieve positive safety results (accident prevention) through worker engagement.

Method

Data were collected using safety manager, supervisor and employee surveys designed to assess and link safety management system practices, employee perceptions resulting from existing practices, and safety performance outcomes.

Results

Results indicate the following: there is a significant negative relationship between the presence of ten individual safety management practices, as well as the composite of these practices, with accident rates; there is a significant negative relationship between the level of safety-focused worker emotional and cognitive engagement with accident rates; safety management systems and worker engagement levels can be used individually to predict accident rates; safety management systems can be used to predict worker engagement levels; and worker engagement levels act as mediators between the safety management system and safety performance outcomes (such as accident rates).

Implications

Even though the presence of safety management system practices is linked with incident reduction and may represent a necessary first-step in accident prevention, safety performance may also depend on mediation by safety-focused cognitive and emotional engagement by workers. Thus, when organizations invest in a safety management system approach to reducing/preventing accidents and improving safety performance, they should also be concerned about winning over the minds and hearts of their workers through human performance-based safety management systems designed to promote and enhance worker engagement.     

Willingness to use safety belt and levels of injury in car accidents

In this article, we develop a bivariate ordered Probit model to analyze the decision to fasten the safety belt in a car and the resulting severity of accidents if it happens. The approach takes into account the fact that the decision to fasten the safety belt has a direct causal effect on the category of injury if an accident happens. Our application to a sample drawn from the database of French accident reports in 2003 for three populations of car users (drivers, front passengers, rear passengers) shows that fastening the safety belt is significantly related to a decrease in severe injuries but it shows also that these car users compensate partly for this safety benefit. Furthermore, it is observed that demographic characteristics of car users, as well as transport facilities, play important roles in decisions to fasten safety belts and in the eventual resulting accident injuries.     

Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways

In adverse driving conditions, such as inclement weather and/or complex terrain, trucks are often involved in single-vehicle (SV) accidents in addition to multi-vehicle (MV) accidents. Ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) is studied to investigate the difference in driver-injury severity between SV and MV accidents by using mixed logit models. Injury severity from SV and MV accidents involving trucks on rural highways is modeled separately and their respective critical risk factors such as driver, vehicle, temporal, roadway, environmental and accident characteristics are evaluated. It is found that there exists substantial difference between the impacts from a variety of variables on the driver-injury severity in MV and SV accidents. By conducting the injury severity study for MV and SV accidents involving trucks separately, some new or more comprehensive observations, which have not been covered in the existing studies can be made. Estimation findings indicate that the snow road surface and light traffic indicators will be better modeled as random parameters in SV and MV models respectively. As a result, the complex interactions of various variables and the nature of truck-driver injury are able to be disclosed in a better way. Based on the improved understanding on the injury severity of truck drivers from truck-involved accidents, it is expected that more rational and effective injury prevention strategy may be developed for truck drivers under different driving conditions in the future.