Critical power two wheeler driving patterns at the emergence of an incident

The paper proposes a methodology based on Bayesian Networks for identifying the power two wheeler (PTW) driving patterns that arise at the emergence of a critical incident based on high resolution driving data (100 Hz) from a naturalistic PTW driving experiment. The proposed methodology aims at identifying the prevailing PTW drivers’ actions at the beginning and during critical incidents and associating the critical incidents to specific PTW driving patterns. Results using data from one PTW driver reveal three prevailing driving actions for describing the onset of an incident and an equal number of actions that a PTW driver executes during the course of an incident to avoid a crash. Furthermore, the proposed methodology efficiently relates the observed sets of actions with different types of incidents occurring during overtaking or due to the interactions of the rider with moving or stationary obstacles and the opposing traffic. The observed interrelations define several driving patterns that are characterized by different initial actions, as well as by different likelihood of sequential actions during the incident. The proposed modeling may have significant implications to the efficient and less time consuming analysis of the naturalist data, as well as to the development of custom made PTW driver assistance systems.     

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.     

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.     

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.     

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.     

Road safety effects of porous asphalt: a systematic review of evaluation studies

This paper presents a systematic review of studies that have evaluated the effects on road safety of porous asphalt. Porous asphalt is widely used on motorways in Europe, mainly in order to reduce traffic noise and increase road capacity. A meta-analysis was made of six studies, containing a total of eighteen estimates of the effect of porous asphalt on accident rates. No clear effect on road safety of porous asphalt was found. All summary estimates of effect indicated very small changes in accident rates and very few were statistically significant at conventional levels. Studies that have evaluated the effects of porous asphalt on nine different risk factors associated with accident occurrence were also reviewed. It was found that four of the risk factors were favourably influenced by porous asphalt, three were adversely influenced, and two were not influenced by porous asphalt. The net impact of these changes in risk factors on accident occurrence cannot be predicted. On the whole, the research that has been reported so far regarding road safety effects of porous asphalt is inconclusive. The studies are not of high quality and the findings are inconsistent.     

Injury incidence rates of cyclists compared to pedestrians,car occupants and powered two-wheeler riders,using a medical registry and mobility data,Rhône County,France

Purpose

In France, the bicycle's modal share is stabilizing after a decline; in some of France's major cities, it has even increased since the 1990s. It is hence relevant to improve the knowledge of the injury risk associated with cycling, compared with other means of transport such as car, walking and powered two-wheeler (PTW) riding.

Methods

The injury incidence rates were estimated by the ratio between accident data and mobility (exposure) data. Two accident data sources were used: police data and hospital-based data (outpatients and inpatients) from the Rhône road trauma Registry. This provides four injury categories: all-injury, hospitalization, serious-injury and fatal-injury. Exposure data were estimated from a regional household travel survey (RTS), using three measures of mobility: number of trips, distance traveled and time spent traveling. The survey was carried out from November 2005 to April 2006, on weekdays, outside school and public holidays; this seasonality was corrected using the 2007–2008 national household travel survey (NTS) that covered an entire year. Only information involving accidents and trips in, and residents of, the Rhône County (1.6 million inhabitants, including the city Lyon) were included in our study. Trends of injury rates were also evaluated in Greater Lyon, using previous travel surveys.

Results

The PTW riders had the highest all-injury, hospitalization, serious-injury and fatal-injury rates, followed by cyclists, and lastly by pedestrians and car occupants. The rates between men and women seemed similar among pedestrians and among car occupants. For car occupants, pedestrians and cyclists, the age group 18–25 years had higher all-injury rate compared with the age group 25–65 years. On the contrary, the age group ≥65 years seemed to have higher hospitalization and serious-injury rates, compared with the age group 25–65 years. For cyclists, the injury rates seemed higher in non-dense areas than in dense areas. Between 1996–1997 and 2005–2006 and with regards to time spent traveling, the all-injury, serious-injury and fatal-injury rates seemed to have decreased for car occupants and cyclists.

Conclusion

The higher risk for PTW riders is confirmed and quantified; it is very high. Decrease in injury rates seems more marked for cyclists; this may indicate the “safety in numbers” effect. Countermeasures for improving road safety could be implemented, especially for vulnerable road user types. However, they will not be sufficient to fill in the gap between the much higher risk for PTW riders and that of car occupants. Exposure-based injury rates can be a tool for monitoring and evaluating the effectiveness of policies and programs, and for comparisons between countries.     

How to identify the key factors that affect driver perception of accident risk. A comparison between Italian and Spanish driver behavior

Road crashes can be caused by different factors, including infrastructure, vehicles, and human variables. Many research studies have focused solely on identifying the key factors that cause road crashes. From these studies, it emerged that human factors have the most relevant impact on accident severity. More specifically, accident severity depends on several factors related directly to the driver, i.e., driving experience, driver's socio-economic characteristics, and driving behavior and attitudes. In this paper, we investigate driver behaviors and attitudes while driving and specifically focus on different methods for identifying the factors that most affect the driver's perception of accident risk. To this end, we designed and conducted a survey in two different European contexts: the city of Cosenza, which is located in the south of Italy, and the city of Granada, which is located in the south of Spain. Samples of drivers were contacted for their opinions on certain aspects of driving rules and attitudes while driving, and different types of questions were addressed to the drivers to assess their judgments of these aspects. Consequently, different methods of data analysis were applied to determine the aspects that heavily influence driver perception of accident risk. An experiment based on the stated preferences (SP) was carried out with the drivers, and the SP data were analyzed using an ordered probit (OP) model. Interesting findings emerged from different analyses of the data and from the comparisons among the data collected in the two different territorial contexts. We found that both Italian and Spanish drivers consider driving in an altered psychophysical state and violating the overtaking rules to be the most risky behaviors.     

To what extent can theory account for the findings of road safety evaluation studies?

This paper proposes a conceptual framework that can be used to assess to what extent the findings of road safety evaluation research make sense from a theoretical point of view. The effects of road safety measures are modelled as passing through two causal chains. One of these, termed the engineering effect, refers to the intended effects of a road safety measure on a set of risk factors related to accident occurrence or injury severity. The engineering effect of road safety measures is modelled in terms of nine basic risk factors, one or more of which any road safety measure needs to influence in order to have the intended effect on accidents or injuries. The other causal chain producing the effects of road safety measures is termed the behavioural effect, and refers to road user behavioural adaptations to road safety measures. The behavioural effect is related to the engineering effect, in the sense that certain properties of the engineering effect of a road safety measure influence the likelihood that behavioural adaptation will occur. The behavioural effect of a road safety measure is modelled in terms of six factors that influence the likelihood that behavioural adaptation will occur. The nine basic risk factors representing the engineering effect of a road safety measure, and the six factors influencing the likelihood of behavioural adaptation can be used as checklists in assessing whether or not the findings of road safety evaluation studies make sense from a theoretical point of view. At the current state of knowledge, a more stringent evaluation of the extent to which theory can explain the findings of road safety evaluation studies is, in most cases, not possible.     

Predictive factors of chronic post-traumatic stress disorder 6 months after a road traffic accident

Background

This study sets out to identify risk factors for post-traumatic stress disorder (PTSD) after a road traffic accident with a view to improving prevention.

Methods

The study used a prospective cohort of road traffic accident casualties. All subjects over 15 years of age were recruited in the course of an interview conducted while they were receiving care in a hospital of the Rhône area administrative département. Six months after their accident, they answered a self-administered postal questionnaire that included the Post-traumatic Check-List Scale (PCLS) in order to evaluate PTSD. Multivariate logistic regression analysis was conducted to compare those subjects with a PCLS score of 44 or over with those with a lower score, in order to identify factors that might be associated with PTSD.

Results

592 subjects (out of 1168) returned the 6-month questionnaire and 541 completed the PCLS test. One hundred subjects had a PCLS score ≥44, suggesting PTSD, and 441 subjects did not. The factors associated with PTSD were initial injury severity, post-traumatic amnesia, the feeling of not being responsible for their accident and persistent pain 6 months after it. A lower odds-ratio was associated with users of two-wheel than four-wheel motor vehicles (OR = 0.4; 0.2–0.9).

Conclusion

Besides predictive factors for PTSD (injury severity, post-traumatic amnesia and the feeling of not being responsible for their accident), our study suggested a reduced risk of PTSD among two-wheel motor vehicle users.     

Use of Cognitive Work Analysis for exploration of safety management in the operation of motorcycles and scooters

The risk of accident, injury and death is disproportionately higher for motorcycle riders than for motorists. In this paper, we investigate strategies of safety management associated with operation of powered two-wheel vehicles (motorcycles and scooters). Accident prevention is most often driven by an epidemiological approach that investigates the risk factors associated with accidents. By focusing on risk factors, these types of studies fail to examine the strengths of the system in any depth. In this paper we employ an ethnographic approach structured by reference to the framework of Cognitive Work Analysis, to identify how riders of powered two-wheel vehicles manage their own safety and the safety of others. We anticipate that this research will open up a rich, relatively untapped, area for exploration of safety interventions.     

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.     

Assessing the validity of road safety evaluation studies by analysing causal chains

This paper discusses how the validity of road safety evaluation studies can be assessed by analysing causal chains. A causal chain denotes the path through which a road safety measure influences the number of accidents. Two cases are examined. One involves chemical de-icing of roads (salting). The intended causal chain of this measure is: spread of salt --> removal of snow and ice from the road surface --> improved friction --> shorter stopping distance --> fewer accidents. A Norwegian study that evaluated the effects of salting on accident rate provides information that describes this causal chain. This information indicates that the study overestimated the effect of salting on accident rate, and suggests that this estimate is influenced by confounding variables the study did not control for. The other case involves a traffic club for children. The intended causal chain in this study was: join the club --> improve knowledge --> improve behaviour --> reduce accident rate. In this case, results are rather messy, which suggests that the observed difference in accident rate between members and non-members of the traffic club is not primarily attributable to membership in the club. The two cases show that by analysing causal chains, one may uncover confounding factors that were not adequately controlled in a study. Lack of control for confounding factors remains the most serious threat to the validity of road safety evaluation studies.     

Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents.

The impact that large trucks have on accident severity has long been a concern in the accident analysis literature. One important measure of accident severity is the most severely injured occupant in the vehicle. Such data are routinely collected in state accident data files in the U.S. Among the many risk factors that determine the most severe level of injury sustained by vehicle occupants, the number of occupants in the vehicle is an important factor. These effects can be significant because vehicles with higher occupancies have an increased likelihood of having someone seriously injured. This paper studies the occupancy/injury severity relationship using Washington State accident data. The effects of large trucks, which are shown to have a significant impact on the most severely injured vehicle occupant, are accounted for by separately estimating nested logit models for truck-involved accidents and for non-truck-involved accidents. The estimation results uncover important relationships between various risk factors and occupant injury. In addition, by comparing the accident characteristics between truck-involved accidents and non-truck-involved accidents, the risk factors unique to large trucks are identified along with the relative importance of such factors. The findings of this study demonstrate that nested logit modeling, which is able to take into account vehicle occupancy effects and identify a broad range of factors that influence occupant injury, is a promising methodological approach.     

Analysing the risk of LNG carrier operations

This paper presents a generic, high-level risk assessment of the global operation of ocean-going liquefied natural gas (LNG) carriers. The analysis collects and combines information from several sources such as an initial hazid, a thorough review of historic LNG accidents, review of previous studies, published damage statistics and expert judgement, and develops modular risk models for critical accident scenarios. In accordance with these risk models, available information from different sources has been structured in the form of event trees for different generic accident categories. In this way, high-risk areas pertaining to LNG shipping operations have been identified. The major contributions to the risk associated with LNG shipping are found to stem from five generic accident categories, i.e. collision, grounding, contact, fire and explosion, and events occurring while loading or unloading LNG at the terminal. Of these, collision risk was found to be the highest. According to the risk analysis presented in this paper, both the individual and the societal risk level associated with LNG carrier operations lie within the As Low As Reasonable Practicable (ALARP) area, meaning that further risk reduction should be required only if available cost-effective risk control options could be identified. This paper also includes a critical review of the various components of the risk models and hence identifies areas of improvements and suggests topics for further research.     

Sources of error in road safety scheme evaluation: a quantified comparison of current methods

This paper considers the various factors that can have a confounding effect in the evaluation of road safety schemes and examines the extent to which current methods can effectively deal with these. A modification to current methods is proposed which allows the reduction in accidents attributable to risk and flow changes to be separately evaluated. Data are presented to demonstrate the relative magnitudes of the various sources of error. It is shown that a principal source of error is normally regression-to-mean (RTM) and a correction for this effect should always be applied. Changes in traffic flow can also result in substantial accident changes and it is important to establish whether flow changes have occurred and if they are attributable to the effect of the scheme.     

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.     

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.     

Risk and protection factors in fatal accidents

This paper aims at addressing the interest and appropriateness of performing accident severity analyses that are limited to fatal accident data. Two methodological issues are specifically discussed, namely the accident-size factors (the number of vehicles in the accident and their level of occupancy) and the comparability of the baseline risk. It is argued that - although these two issues are generally at play in accident severity analyses - their effects on, e.g., the estimation of survival probability, are exacerbated if the analysis is limited to fatal accident data. As a solution, it is recommended to control for these effects by (1) including accident-size indicators in the model, (2) focusing on different sub-groups of road-users while specifying the type of opponent in the model, so as to ensure that comparable baseline risks are worked with. These recommendations are applied in order to investigate risk and protection factors of car occupants involved in fatal accidents using data from a recently set up European Fatal Accident Investigation database (Reed and Morris, 2009). The results confirm that the estimated survival probability is affected by accident-size factors and by type of opponent. The car occupants’ survival chances are negatively associated with their own age and that of their vehicle. The survival chances are also lower when seatbelt is not used. Front damage, as compared to other damaged car areas, appears to be associated with increased survival probability, but mostly in the case in which the accident opponent was another car. The interest of further investigating accident-size factors and opponent effects in fatal accidents is discussed.