Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong

A population-based case-control study was conducted to examine factors affecting the severity of single vehicle traffic accidents in Hong Kong. In particular, single vehicle accident data of three major vehicle types, namely private vehicles, goods vehicles and motorcycles, which contributed to over 80% of all single vehicle accidents during the 2-year-period 1999-2000, were considered. Data were obtained from the newly implemented traffic accident data system (TRADS), which was developed jointly by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. The effect of district, human, vehicle, safety, environmental and site factors on injury severity of an accident was examined. Unique risk factors associated with each of the vehicle types were identified by means of stepwise logistic regression models. For private vehicles, district board, gender of driver, age of vehicle, time of the accident and street light conditions are significant factors determining injury severity. For goods vehicles, seat-belt usage and weekday occurrence are the only two significant factors associated with injury severity. For motorcycles, age of vehicle, weekday and time of the accident were determined to be important factors affecting the injury severity. Identification of potential risk factors pertinent to the particular vehicle type has important implications to relevant official organisations in modifying safety measures in order to reduce the occurrence of severe traffic accidents, which would help to promote a safe road environment.     

Kernel density estimation and K-means clustering to profile road accident hotspots

Identifying road accident hotspots is a key role in determining effective strategies for the reduction of high density areas of accidents. This paper presents (1) a methodology using Geographical Information Systems (GIS) and Kernel Density Estimation to study the spatial patterns of injury related road accidents in London, UK and (2) a clustering methodology using environmental data and results from the first section in order to create a classification of road accident hotspots. The use of this methodology will be illustrated using the London area in the UK. Road accident data collected by the Metropolitan Police from 1999 to 2003 was used. A kernel density estimation map was created and subsequently disaggregated by cell density to create a basic spatial unit of an accident hotspot. Appended environmental data was then added to the hotspot cells and using K-means clustering, an outcome of similar hotspots was deciphered. Five groups and 15 clusters were created based on collision and attribute data. These clusters are discussed and evaluated according to their robustness and potential uses in road safety campaigning.     

Quantifying safety benefit of winter road maintenance: Accident frequency modeling

This research presents a modeling approach to investigate the association of the accident frequency during a snow storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. The results have the premise to be applied for evaluating different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index as a surrogate measure of the commonly used friction measure to capture different road surface conditions. Data from various data sources, such as weather, road condition observations, traffic counts and accidents, are integrated and used to test three event-based models including the Negative Binomial model, the generalized NB model and the zero inflated NB model. These models are compared for their capability to explain differences in accident frequencies between individual snow storms. It was found that the generalized NB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the road surface condition index was statistically significant influencing the accident occurrence. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods.     

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.     

Perception of road accident causes

A theoretical two-dimensional model on prevalence and risk was developed. The objective of this study was to validate this model empirically to answer three questions: How do European drivers perceive the importance of several causes of road accidents? Are there important differences in perceptions between member states? Do these perceptions reflect the real significance of road accident causes? Data were collected from 23 countries, based on representative national samples of at least 1000 respondents each (n=24,372). Face-to-face interviews with fully licensed, active car drivers were conducted using a questionnaire containing closed answer questions. Respondents were asked to rate 15 causes of road accidents, each using a six-point ordinal scale. The answers were analyzed by calculating Kendall's tau for each pair of items to form lower triangle similarity matrices per country and for Europe as a whole. These matrices were then used as the input files for an individual difference scaling to draw a perceptual map of the 15 items involved. The hypothesized model on risk and prevalence fits the data well and enabled us to answer the three questions of concern. The subject space of the model showed that there are no relevant differences between the 23 countries. The group space of the model comprises four quadrants, each containing several items (high perceived risk/low perceived prevalence items; high perceived risk/high perceived prevalence items; low perceived risk/high perceived prevalence items and low perceived risk/low perceived prevalence items). Finally, perceptions of the items driving under the influence of alcohol, drugs and medicines and driving using a handheld or hands-free mobile phone are discussed with regard to their real significance in causing road accidents. To conclude, individual difference scaling offers some promising possibilities to study drivers' perception of road accident causes.     

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.     

A method to cope with the random errors of observed accident rates in regression analysis

The paper is concerned with linear multiregression analysis on accident rates related to road geometric design elements. Supposing that a data set of accident records and geometric design elements of a certain stretch of a road is given, there are two steps for regression analysis: first, division of the road into a number of segments; and second, application of regression analysis to the set of segments. The main interest of the present paper is the first step. Occurrence of a traffic accident in a road segment is a stochastic event and an observed accident rate in a segment contains a certain magnitude of random error that deteriorates the explanatory power and reliability of the regression analysis. Random errors are required to be appropriately controlled for an effective regression analysis. The first part of the paper discusses how to evaluate a random error contained in an accident rate of a road segment and shows that a random error depends on the number of accidents and vehicle-kilometerage of the segment. It is then shown that random errors of the segment should be as much as possible equal to each other and small enough compared with the accident rate variance based on the discussion of how the random errors affect the efficiency of regression analysis. Several alternative criteria on the random errors for dividing a road into segments are proposed and numerical examples of Tokyo-Kobe Expressway are presented to examine the appropriateness of the alternative criteria. One of them is finally recommended as the most practically useful criterion.     

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.     

Network-level accident-mapping: Distance based pattern matching using artificial neural network

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily ‘transferable’ as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in “learning” the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that the accuracy is much better than other methods.     

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.     

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.     

Bayes classifiers for imbalanced traffic accidents datasets

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009–2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.     

Risk factors associated with traffic violations and accident severity in China

With the recent economic boom in China, vehicle volume and the number of traffic accident fatalities have become the highest in the world. Meanwhile, traffic accidents have become the leading cause of death in China. Systematically analyzing road safety data from different perspectives and applying empirical methods/implementing proper measures to reduce the fatality rate will be an urgent and challenging task for China in the coming years. In this study, we analyze the traffic accident data for the period 2006–2010 in Guangdong Province, China. These data, extracted from the Traffic Management Sector-Specific Incident Case Data Report, are the only officially available and reliable source of traffic accident data (with a sample size >7000 per year). In particular, we focus on two outcome measures: traffic violations and accident severity. Human, vehicle, road and environmental risk factors are considered. First, the results establish the role of traffic violations as one of the major risks threatening road safety. An immediate implication is: if the traffic violation rate could be reduced or controlled successfully, then the rate of serious injuries and fatalities would be reduced accordingly. Second, specific risk factors associated with traffic violations and accident severity are determined. Accordingly, to reduce traffic accident incidence and fatality rates, measures such as traffic regulations and legislation—targeting different vehicle types/driver groups with respect to the various human, vehicle and environment risk factors—are needed. Such measures could include road safety programs for targeted driver groups, focused enforcement of traffic regulations and road/transport facility improvements. Data analysis results arising from this study will shed lights on the development of similar (adjusted) measures to reduce traffic violations and/or accident fatalities and injuries, and to promote road safety in other regions.     

Underreporting in traffic accident data, bias in parameters and the structure of injury severity models

Injury severities in traffic accidents are usually recorded on ordinal scales, and statistical models have been applied to investigate the effects of driver factors, vehicle characteristics, road geometrics and environmental conditions on injury severity. The unknown parameters in the models are in general estimated assuming random sampling from the population. Traffic accident data however suffer from underreporting effects, especially for lower injury severities. As a result, traffic accident data can be regarded as outcome-based samples with unknown population shares of the injury severities. An outcome-based sample is overrepresented by accidents of higher severities. As a result, outcome-based samples result in biased parameters which skew our inferences on the effect of key safety variables such as safety belt usage. The pseudo-likelihood function for the case with unknown population shares, which is the same as the conditional maximum likelihood for the case with known population shares, is applied in this study to examine the effects of severity underreporting on the parameter estimates. Sequential binary probit models and ordered-response probit models of injury severity are developed and compared in this study. Sequential binary probit models assume that the factors determining the severity change according to the level of the severity itself, while ordered-response probit models assume that the same factors correlate across all levels of severity. Estimation results suggest that the sequential binary probit models outperform the ordered-response probit models, and that the coefficient estimates for lap and shoulder belt use are biased if underreporting is not considered. Mean parameter bias due to underreporting can be significant. The findings show that underreporting on the outcome dimension may induce bias in inferences on a variety of factors. In particular, if underreporting is not accounted for, the marginal impacts of a variety of factors appear to be overestimated. Fixed objects and environmental conditions are overestimated in their impact on injury severity, as is the effect of separate lap and shoulder belt use. Combined lap and shoulder belt usage appears to be unaffected. The parameter bias is most pronounced when underreporting of possible injury accidents in addition to property damage only accidents is taken into account.     

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.     

Survey concerning international computer files of road accidents

A survey concerning international harmonization of accident reporting was distributed to 80 experts in accident reporting and analysis. Completed surveys were received from 50 persons in 13 countries, 74% of the respondents had more than 10 years of experience in the field of traffic safety. The main findings of this survey are: (1) 86% of the respondents think that an international computer file of disaggregated fatal-accident data would contribute to understanding of traffic safety, and 84% would use such a file. (2) An international non-fatal-accident file was considered to be of value in research on human factors and accident causation (60%), and in determining black spots in the road network (57%). (3) Police was the most frequently mentioned source of data for both the fatal and non-fatal international data files. Nevertheless, fewer than one-quarter of respondents considered police as the suitable exclusive source of either data. (4) The majority view was that the data for both types of files should come from more than one agency. (5) In the case of the fatal-accident file, 78% of the respondents considered it important that the data be cross-checked with the public health records. (6) The 10 most useful variables for a fatal-accident file were traffic unit type (e.g. car), accident type (e.g. angle), road class, driver age, date/time of day, age of person killed, number of killed persons, number of injured persons, drinking or drug use, and restraint usage of person killed. (7) The analogous 10 variables for a non-fatal-accident file were accident type, traffic unit type, driver age, date/time of day, road class, extent of injury, number of injured persons, age of involved persons, number of involved persons, and seat location.     

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.     

Understanding spatial concentrations of road accidents using frequent item sets

This paper aims at understanding why road accidents tend to cluster in specific road segments. More particularly, it aims at analyzing which are the characteristics of the accidents occurring in "black" zones compared to those scattered all over the road. A technique of frequent item sets (data mining) is applied for automatically identifying accident circumstances that frequently occur together, for accidents located in and outside "black" zones. A Belgian periurban region is used as case study. Results show that accidents occurring in "black" zones are characterized by left-turns at signalized intersections, collisions with pedestrians, loss control of the vehicle (run-off-roadway) and rainy weather conditions. Accidents occurring outside "black" zones (scattered in space) are characterized by left turns on intersections with traffic signs, head-on collisions and drunken road user(s). Furthermore, parallel collisions and accidents on highways or roads with separated lanes, occurring at night or during the weekend are frequently occurring accident patterns for all accident locations. These exploratory results show the potentiality of the frequent item set method in addition to more classical statistical techniques, but also suggest that there is no unique countermeasure for reducing the number of accidents.     

Causal models for road accident fatalities in Yemen.

An identification of the causes of road accident fatalities is becoming more important with the growth of technology, population, number of vehicles and the need for their use. Many authors have addressed the problem in the past but no universal findings have been obtained. The problem tends to be different under different environments and for different geographical regions. The aim of this paper is to develop a model for the analysis and forecasting of road accident fatalities in Yemen considering data restrictions. The proposed data has a particular structure of accident occurrence that has not been reported in any existing research using data in other countries. The available data for the period 1978-1995 is used to build models to understand the nature and extent of the causes of fatalities. Part of the data is used for model building and part of it for test purposes. The issues of correlation and causality have been addressed and multiple collinearity is investigated and dealt with. Two alternative models are proposed based on both statistical grounds and that of practicality in viable decision making. The influence of consuming a locally grown stimulant called Qat on road users has been addressed and it is found that it increases the risk of accidents. This is not the common understanding within the authorities in Yemen as growing and consuming Qat is unregulated.