Relationships between road accidents and hourly traffic flow—I: Analyses and interpretation

This research extends the investigation of the relationships between measures of accidents and traffic flow, and considers the hourly flow instead of the average daily traffic (ADT), which has already been reported. The findings of this study serve as a basis for further clarification of the interactions between various levels of traffic flow and road accidents. Eight four-lane road sections were studied during an 8-year period, providing adequate data based on carefully predefined criteria. Power functions are fitted and classified according to: (1) time-sequence analysis for each roadway section; and (2) cross sectional analysis on a one year basis. The results are presented, separately for multi and single vehicle accidents, in a matrix-format. A linear dependency was observed between the power and the logarithm of the multiple constant. This was done in a similar fashion to the previously reported study of the relationships between road accidents and ADT. The results for each type of analysis and type of accident are discussed, and three examples of a practical application are given.     

Accident prediction models for urban roads

This paper describes some of the main findings from two separate studies on accident prediction models for urban junctions and urban road links described in [Uheldsmodel for bygader-Del1: Modeller for 3-og 4-benede kryds. Notat 22, The Danish Road Directorate, 1995; Uheldsmodel for bygader- Del2: Modeller for straekninger. Notat 59, The Danish Road Directorate, 1998] (Greibe and Hemdorff, 1995, 1988).The main objective for the studies was to establish simple, practicable accident models that can predict the expected number of accidents at urban junctions and road links as accurately as possible. The models can be used to identify factors affecting road safety and in relation to 'black spot' identification and network safety analysis undertaken by local road authorities.The accident prediction models are based on data from 1036 junctions and 142 km road links in urban areas. Generalised linear modelling techniques were used to relate accident frequencies to explanatory variables.The estimated accident prediction models for road links were capable of describing more than 60% of the systematic variation ('percentage-explained' value) while the models for junctions had lower values. This indicates that modelling accidents for road links is less complicated than for junctions, probably due to a more uniform accident pattern and a simpler traffic flow exposure or due to lack of adequate explanatory variables for junctions.Explanatory variables describing road design and road geometry proved to be significant for road link models but less important in junction models. The most powerful variable for all models was motor vehicle traffic flow.     

A crash-prediction model for road tunnels

Considerable research has been carried out into open roads to establish relationships between crashes and traffic flow, geometry of infrastructure and environmental factors, whereas crash-prediction models for road tunnels, have rarely been investigated. In addition different results have been sometimes obtained regarding the effects of traffic and geometry on crashes in road tunnels. However, most research has focused on tunnels where traffic and geometric conditions, as well as driving behaviour, differ from those in Italy. Thus, in this paper crash prediction-models that had not yet been proposed for Italian road tunnels have been developed. For the purpose, a 4-year monitoring period extending from 2006 to 2009 was considered. The tunnels investigated are single-tube ones with unidirectional traffic. The Bivariate Negative Binomial regression model, jointly applied to non-severe crashes (accidents involving material-damage only) and severe crashes (fatal and injury accidents only), was used to model the frequency of accident occurrence. The year effect on severe crashes was also analyzed by the Random Effects Binomial regression model and the Negative Multinomial regression model. Regression parameters were estimated by the Maximum Likelihood Method. The Cumulative Residual Method was used to test the adequacy of the regression model through the range of annual average daily traffic per lane. The candidate set of variables was: tunnel length (L), annual average daily traffic per lane (AADT_L), percentage of trucks (%Tr), number of lanes (N_L), and the presence of a sidewalk. Both for non-severe crashes and severe crashes, prediction-models showed that significant variables are: L, AADT_L, %Tr, and N_L. A significant year effect consisting in a systematic reduction of severe crashes over time was also detected. The analysis developed in this paper appears to be useful for many applications such as the estimation of accident reductions due to improvement in existing tunnels and/or to modifications of traffic control systems, as well as for the prediction of accidents when different tunnel design options are compared.     

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.     

Application of classification algorithms for analysis of road safety risk factor dependencies

Transportation continues to be an integral part of modern life, and the importance of road traffic safety cannot be overstated. Consequently, recent road traffic safety studies have focused on analysis of risk factors that impact fatality and injury level (severity) of traffic accidents. While some of the risk factors, such as drug use and drinking, are widely known to affect severity, an accurate modeling of their influences is still an open research topic. Furthermore, there are innumerable risk factors that are waiting to be discovered or analyzed. A promising approach is to investigate historical traffic accident data that have been collected in the past decades. This study inspects traffic accident reports that have been accumulated by the California Highway Patrol (CHP) since 1973 for which each accident report contains around 100 data fields. Among them, we investigate 25 fields between 2004 and 2010 that are most relevant to car accidents. Using two classification methods, the Naive Bayes classifier and the decision tree classifier, the relative importance of the data fields, i.e., risk factors, is revealed with respect to the resulting severity level. Performances of the classifiers are compared to each other and a binary logistic regression model is used as the basis for the comparisons. Some of the high-ranking risk factors are found to be strongly dependent on each other, and their incremental gains on estimating or modeling severity level are evaluated quantitatively. The analysis shows that only a handful of the risk factors in the data dominate the severity level and that dependency among the top risk factors is an imperative trait to consider for an accurate analysis.     

Intelligent transport systems and effects on road traffic accidents: state of the art

The results from several reviews have been presented and the aspects of road safety associated with intelligent transport systems (ITS) applications have been addressed. The attempt is to make a state-of-the-art regarding effects on accidents by categorising systems according to levels of evaluations methods that have been applied. These categories are effects on behaviour, effects on accidents by proxy/surrogate methods, accident studies from real traffic, effects on accident types and finally by meta-analysis where weighted estimates of effects on accidents can be calculated. Thirty-three IT systems including driver assistance systems/advanced driver assistance systems, in-vehicle information systems, in-vehicle data-collection systems and road telematics have been listed. Effects based on meta-analysis are estimated for 11 systems, and single accident studies are found for an additional 2 systems. For the remaining 20 systems, no studies from real road traffic have been identified. Effects on accidents of antilocking brake systems and electronic stability control (ESC) are presented in more detail according to their effects on certain accident types. ESC appears to be very efficient in reducing the number of accidents. Behavioural adaptations to ITS are considered and discussed, especially in terms of compensation mechanisms. Four hypotheses regarding prediction of effects on accidents are stated according to whether systems increase or decrease 'windows of opportunities' by calling upon a driver behaviour model where emotions play a central role     

Modeling traffic accident occurrence and involvement

The Negative Binomial modeling technique was used to model the frequency of accident occurrence and involvement. Accident data over a period of 3 years, accounting for 1,606 accidents on a principal arterial in Central Florida, were used to estimate the model. The model illustrated the significance of the Annual Average Daily Traffic (AADT), degree of horizontal curvature, lane, shoulder and median widths, urban/rural, and the section's length, on the frequency of accident occurrence. Several Negative Binomial models of the frequency of accident involvement were also developed to account for the demographic characteristics of the driver (age and gender). The results showed that heavy traffic volume, speeding, narrow lane width, larger number of lanes, urban roadway sections, narrow shoulder width and reduced median width increase the likelihood for accident involvement. Subsequent elasticity computations identified the relative importance of the variables included in the models. Female drivers experience more accidents than male drivers in heavy traffic volume, reduced median width, narrow lane width, and larger number of lanes. Male drivers have greater tendency to be involved in traffic accidents while speeding. The models also indicated that young and older drivers experience more accidents than middle aged drivers in heavy traffic volume, and reduced shoulder and median widths. Younger drivers have a greater tendency of being involved in accidents on roadway curves and while speeding.     

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.     

Spatio-temporal analysis of road traffic accidents in Oyo State, Nigeria

Studies on road traffic accidents in developing nations have been very scanty. In Nigeria in particular not much is known about accident phenomena. This paper is an account of a scientific investigation into the spatial and temporal characteristics of road traffic accidents in Oyo State, Nigeria. The study is based, principally, on the Nigerian Police Official documented road traffic accident statistics from January 1980 to December 1984. The study examined general features of road traffic accident occurrence in the state and undertook a critical analysis of both temporal and spatial dimensions of the problem. The study identified six traffic zones that could be designated as accident Black Spots in the state, to which priority attention should be given in any road safety programme. Moreover, the study attempted to explain some of the complex factors that might account for the observed spatial and temporal variation in road accidents frequency and fatality. Significantly, the study observed a consistently high number of road accidents during the months of March, September, and December, while fluctuatingly high and low accident figures are recorded for other months of the year. Some possible reasons for this temporal trend in accident occurrence is discussed.     

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.     

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.     

Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model

Multi-vehicle rear-end accidents constitute a substantial portion of the accidents occurring at signalized intersections. To examine the accident characteristics, this study utilized the 2001 Florida traffic accident data to investigate the accident propensity for different vehicle roles (striking or struck) that are involved in the accidents and identify the significant risk factors related to the traffic environment, the driver characteristics, and the vehicle types. The Quasi-induced exposure concept and the multiple logistic regression technique are used to perform this analysis. The results showed that seven road environment factors (number of lanes, divided/undivided highway, accident time, road surface condition, highway character, urban/rural, and speed limit), five factors related to striking role (vehicle type, driver age, alcohol/drug use, driver residence, and gender), and four factors related to struck role (vehicle type, driver age, driver residence, and gender) are significantly associated with the risk of rear-end accidents. Furthermore, the logistic regression technique confirmed several significant interaction effects between those risk factors.     

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.     

Impact of safety belt use on road accident injury and injury type in Kuwait

The enactment of Kuwait's seat belt law in January 1994 provided an opportunity to examine the impact of seat belt use on road accident fatalities and injury types in this affluent Persian Gulf nation. Via a structured data form, the results of injurious/fatal road accidents for more than 1200 accident victims were gathered from the files of the six major government hospitals which treat most traffic accident victims. Statistical analysis of the data showed that seat belt use has had a positive effect in reducing both road traffic fatalities and multiple injuries in Kuwait. The use of seat belts has also affected the nature of the injuries resulting from road traffic accidents. Non-users of belts experienced higher frequencies of head, face, abdominal and limb injuries. Users of belts, on the other hand, suffered higher frequencies of neck and chest injuries. The interrelationship between the victim, his age, and the type of injuries resulting from road traffic accidents is also investigated.     

Development of an accident duration prediction model on the Korean Freeway Systems

Since duration prediction is one of the most important steps in an accident management process, there have been several approaches developed for modeling accident duration. This paper presents a model for the purpose of accident duration prediction based on accurately recorded and large accident dataset from the Korean Freeway Systems. To develop the duration prediction model, this study utilizes the log-logistic accelerated failure time (AFT) metric model and a 2-year accident duration dataset from 2006 to 2007. Specifically, the 2006 dataset is utilized to develop the prediction model and then, the 2007 dataset was employed to test the temporal transferability of the 2006 model. Although the duration prediction model has limitations such as large prediction error due to the individual differences of the accident treatment teams in terms of clearing similar accidents, the results from the 2006 model yielded a reasonable prediction based on the mean absolute percentage error (MAPE) scale. Additionally, the results of the statistical test for temporal transferability indicated that the estimated parameters in the duration prediction model are stable over time. Thus, this temporal stability suggests that the model may have potential to be used as a basis for making rational diversion and dispatching decisions in the event of an accident. Ultimately, such information will beneficially help in mitigating traffic congestion due to accidents.     

The effect of road and environmental characteristics on pedestrian hit-and-run accidents in Ghana

The number of pedestrians who have died as a result of being hit by vehicles has increased in recent years, in addition to vehicle passenger deaths. Many pedestrians who were involved in road traffic accident died as a result of the driver leaving the pedestrian who was struck unattended at the scene of the accident. This paper seeks to determine the effect of road and environmental characteristics on pedestrian hit-and-run accidents in Ghana. Using pedestrian accident data extracted from the National Road Traffic Accident Database at the Building and Road Research Institute (BRRI) of the Council for Scientific and Industrial Research (CSIR), Ghana, a binary logit model was employed in the analysis. The results from the estimated model indicate that fatal accidents, unclear weather, nighttime conditions, and straight and flat road sections without medians and junctions significantly increase the likelihood that the vehicle driver will leave the scene after hitting a pedestrian. Thus, integrating median separation and speed humps into road design and construction and installing street lights will help to curb the problem of pedestrian hit-and-run accidents in Ghana.     

Quality function deployment applied to local traffic accident reduction

One of the major tasks of police stations is the management of local road traffic accidents. Proper prevention policy which reflects the local accident characteristics could immensely help individual police stations in decreasing various severity levels of road traffic accidents. In order to relate accident variation to local driving environmental characteristics, we use both cluster analysis and Poisson regression. The fitted result at the level of each cluster for each type of accident severity is utilized as an input to quality function deployment. Quality function deployment (QFD) has been applied to customer satisfaction in various industrial quality improvement settings, where several types of customer requirements are related to various control factors. We show how QFD enables one to set priorities on various road accident control policies to which each police station has to pay particular attention.     

Multiple-vehicle traffic accidents in Hong Kong

‘Multiple-vehicle traffic accident’ refers to a crash between two or more moving objects. Unlike single-vehicle accidents, not all drivers involving in a multiple-vehicle accident are responsible for the occurrence of the event. Accordingly, variables such as road type, speed limit and number of vehicles involved in the accident are expected to play a much more important role in association with injury severity in multiple-vehicle accidents. To study the factors influencing injury severity of multiple-vehicle traffic accidents, a population-based study was conducted. The traffic accident data was obtained from the Traffic Accident Data System (TRADS), which was developed by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. Multiple-vehicle traffic accidents (N = 10,630) occurring during the 2-year period 1999/2000 were considered. Potential risk factors such as district, human, vehicle, safety, environmental and site factors were examined. Categorizing injury severity into “fatal/serious” and “slight”, a stepwise logistic regression model was applied to the population data set. The district board, time of the accident, driver's gender, vehicle type, road type, speed limit and the number of vehicles involved are significant factors influencing the injury severity. Identification of risk factors for severe traffic accidents provides valuable information to help with new and improved road safety control measures.     

The impact of young drivers’ lifestyle on their road traffic accident risk in greater Athens area

Young drivers (18–24) both in Greece and elsewhere appear to have high rates of road traffic accidents. Many factors contribute to the creation of these high road traffic accidents rates. It has been suggested that lifestyle is an important one. The main objective of this study is to find out and clarify the (potential) relationship between young drivers’ lifestyle and the road traffic accident risk they face. Moreover, to examine if all the youngsters have the same elevated risk on the road or not. The sample consisted of 241 young Greek drivers of both sexes. The statistical analysis included factor analysis and logistic regression analysis. Through the principal component analysis a ten factor scale was created which included the basic lifestyle traits of young Greek drivers. The logistic regression analysis showed that the young drivers whose dominant lifestyle trait is alcohol consumption or drive without destination have high accident risk, while these whose dominant lifestyle trait is culture, face low accident risk. Furthermore, young drivers who are religious in one way or another seem to have low accident risk. Finally, some preliminary observations on how health promotion should be put into practice are discussed.