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 influence of the infrastructure characteristics in urban road accidents occurrence

This paper summarizes the result of a study regarding the creation of tools that can be used in intervention methods in the planning and management of urban road networks in Portugal.     

The predictive validity of empirical Bayes estimates of road safety

This paper examines the predictive validity of empirical Bayes (EB) estimates of road safety. The predictive performance of EB-estimates was tested by applying five versions of EB-estimates of road safety:

• (1) 

  A simple estimate derived from the empirical distribution of accidents in a population of sites, by which the number of accidents predicted for period 2 for sites that recorded k accidents in period 1 equals the number of accidents for sites that recorded k + 1 accidents in period 1.

• (2) 

  Estimates derived from the parameters of a negative binomial distribution fitted to an empirical distribution of accidents in a population of sites by means of the method of moments.

• (3) 

  Estimates derived from the parameters of a negative binomial distribution fitted to an empirical distribution of accidents in a population of sites by means of the maximum likelihood technique.

• (4) 

  Estimates derived by combining the predictions of an accident prediction model and the recorded number of accidents for a site.

• (5) 

  Estimates derived by combining the predictions of a different version of an accident prediction model and the recorded number of accidents for a site.

All versions of EB-estimates are compared to the traditional, naïve assumption of treating the recorded number of accidents as an unbiased estimate of the expected number of accidents.To test the predictive performance of EB-estimates, data for two periods was used. EB-estimates based on data for the first period were treated as predictions of the number of accidents in the second period for road sections that had 0, 1, 2, etc., accidents in the first period, the idea being that the more accurate the prediction, the more accurate the result of a before-and-after study.All versions of EB-estimates were found to give considerably more correct predictions of the number of accidents in the second period than relying on the count of accidents in the first period as a prediction of the count in the second period. Smaller prediction errors were associated with predictions based on accident prediction models than predictions not based on such models.     

Model calibration: A hierarchical Bayesian approach

Many geotechnical engineering models are empirical and calibrated based on data gathered from various sites/projects, using optimisation algorithms with criteria like least squared errors or minimising the coefficient of variation of method bias with the constraint of mean bias equal to unity. This paper discusses the use of hierarchical Bayesian regression models for the same purpose. A database of axial capacity of piles in predominantly clay sites and a CPT-based design model, compiled and developed as part of a Joint Industry Project (JIP) led by the Norwegian Geotechnical Institute (NGI), is used for demonstration. The analyses focus on two related areas that the traditional approaches overlook: (i) quantification of uncertainty in the estimated parameters of the model, and (ii) modelling site-dependency of the model parameters (i.e., between-group variation). The former is important in the context of reliability-based design and contributes to establishing confidence in estimated reliability indices, particularly when only limited data are available. The latter expands our understanding regarding the domain of applicability of a model; that is, if a model is broadly applicable or highly site-dependent. The benefits of the proposed Bayesian approach are highlighted with a prediction exercise where the calibrated models are used in conjunction with limited site or project-specific data.     

A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area

Road accidents can be caused by different factors such as human factors. Quality of the decision-making process of drivers could have a considerable impact on preventing disasters. The main objective of this study is the analysis of factors affecting road accidents by considering the severity of accidents and decision-making styles of drivers. To this end, a novel framework is proposed based on data envelopment analysis (DEA) and statistical methods (SMs) to assess the factors affecting road accidents. In this study, for the first time, dominant decision-making styles of drivers with respect to severity of injuries are identified. To show the applicability of the proposed framework, this research employs actual data of more than 500 samples in Tehran, Iran. The empirical results indicate that the flexible decision style is the dominant style for both minor and severe levels of accident injuries.     

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.     

A discrete-time Bayesian network reliability modeling and analysis framework

Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel reliability modeling and analysis framework based on the Bayesian network (BN) formalism. The overall approach is to investigate timed Bayesian networks and to find a suitable reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis.     

Comparing single vehicle and multivehicle fatal road crashes: A joint analysis of road conditions,time variables and driver characteristics

The difference between single vehicle crashes and multivehicle crashes was investigated in a collection of fatal crashes from six European countries. Variables with respect to road conditions, time variables, and participant characteristics were studied separately at first and then jointly in a logistic multiple regression model allowing to weigh different accounts of single vehicle as opposed to multivehicle crash occurrence.     

Sensitivity analysis of an accident prediction model by the fractional factorial method

Sensitivity analysis of a model can help us determine relative effects of model parameters on model results. In this study, the sensitivity of the accident prediction model proposed by Zegeer et al. [Zegeer, C.V., Reinfurt, D., Hummer, J., Herf, L., Hunter, W., 1987. Safety Effect of Cross-section Design for Two-lane Roads, vols. 1-2. Report FHWA-RD-87/008 and 009 Federal Highway Administration, Department of Transportation, USA] to its parameters was investigated by the fractional factorial analysis method. The reason for selecting this particular model is that it incorporates both traffic and road geometry parameters besides terrain characteristics. The evaluation of sensitivity analysis indicated that average daily traffic (ADT), lane width (W), width of paved shoulder (PA), median (H) and their interactions (i.e., ADT-W, ADT-PA and ADT-H) have significant effects on number of accidents. Based on the absolute value of parameter effects at the three- and two-standard deviation thresholds ADT was found to be of primary importance, while the remaining identified parameters seemed to be of secondary importance. This agrees with the fact that ADT is among the most effective parameters to determine road geometry and therefore, it is directly related to number of accidents. Overall, the fractional factorial method was found to be an efficient tool to examine the relative importance of the selected accident prediction model parameters.     

Explaining the road accident risk: Weather effects

This research aims to highlight the link between weather conditions and road accident risk at an aggregate level and on a monthly basis, in order to improve road safety monitoring at a national level. It is based on some case studies carried out in Work Package 7 on “Data analysis and synthesis” of the EU-FP6 project “SafetyNet – Building the European Road Safety Observatory”, which illustrate the use of weather variables for analysing changes in the number of road injury accidents. Time series analysis models with explanatory variables that measure the weather quantitatively were used and applied to aggregate datasets of injury accidents for France, the Netherlands and the Athens region, over periods of more than 20 years. The main results reveal significant correlations on a monthly basis between weather variables and the aggregate number of injury accidents, but the magnitude and even the sign of these correlations vary according to the type of road (motorways, rural roads or urban roads). Moreover, in the case of the interurban network in France, it appears that the rainfall effect is mainly direct on motorways – exposure being unchanged, and partly indirect on main roads – as a result of changes in exposure. Additional results obtained on a daily basis for the Athens region indicate that capturing the within-the-month variability of the weather variables and including it in a monthly model highlights the effects of extreme weather. Such findings are consistent with previous results obtained for France using a similar approach, with the exception of the negative correlation between precipitation and the number of injury accidents found for the Athens region, which is further investigated. The outlook for the approach and its added value are discussed in the conclusion.     

Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using intra-class correlation coefficient (ICC) and deviance information criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time and in good street-lighting condition as well as those involving pedestrian injuries tend to be less severe. But crashes that occur in night time, at T/Y type intersections, and on right-most lane, as well as those that occur in intersections where red light cameras are installed tend to be more severe. Moreover, heavy vehicles have a better resistance on severe crash and thus induce less severe injuries, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.     

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

The goal of this paper is to evaluate whether the incentives incorporated in toll highway concession contracts in order to encourage private operators to adopt measures to reduce accidents are actually effective at improving safety. To this end, we implemented negative binomial regression models using information about highway characteristics and accident data from toll highway concessions in Spain from 2007 to 2009. Our results show that even though road safety is highly influenced by variables that are not managed by the contractor, such as the annual average daily traffic (AADT), the percentage of heavy vehicles on the highway, number of lanes, number of intersections and average speed; the implementation of these incentives has a positive influence on the reduction of accidents and injuries. Consequently, this measure seems to be an effective way of improving safety performance in road networks.     

A systematic approach to analysing errors of commission from diagnosis failure in accident progression

The accident scenarios of a nuclear power plant are composed of an initiating event (IE), additional events/failures and human inappropriate actions, the combinations of which lead to irreversible consequences. In such a dynamic situation, operators should diagnose the occurring events/failures (including an initiating event and additional events) and assess the related situations utilising the available resources such as operating procedures or human–machine systems to control and maintain the plant in a stable condition. The misdiagnosis or diagnosis failure of the occurring events could cause critical human inappropriate actions that aggravate the plant condition, which is termed as errors of commission (EOCs). This paper presents a methodology for analysing the potential for diagnosis failure of the initiating and additional events and the consequent EOC events, based on the operating procedures, in the accident scenarios of nuclear power plants. The method to be presented categorizes the diagnostic situations in the accident scenarios into three cases according to the structure of the emergency operating procedures (EOPs) and the time of the occurring events: (1) the diagnosis of an initiating event, (2) the diagnosis of both an initiating event and an additional event when an additional event occurs prior to the performance of the diagnosis procedure, and (3) the diagnosis of an additional event when an additional events occurs after the performance of the diagnosis procedure. The application of the method is illustrated through three case example scenarios: (1) the power-operated relief valve (PORV) or the pressurizer safety valve (PSV) LOCA, (2) the loss of all feedwater (LOAF) event (loss of main feedwater*loss of auxiliary feedwater), (3) the sequence of<the station blackout (SBO)*loss of turbine-driven (or diesel-driven) auxiliary feedwater*PSV stuck-open*recovery of AC power>.     

The likelihood of achieving quantified road safety targets: A binary logistic regression model for possible factors

In past several decades, many countries have set quantified road safety targets to motivate transport authorities to develop systematic road safety strategies and measures and facilitate the achievement of continuous road safety improvement. Studies have been conducted to evaluate the association between the setting of quantified road safety targets and road fatality reduction, in both the short and long run, by comparing road fatalities before and after the implementation of a quantified road safety target. However, not much work has been done to evaluate whether the quantified road safety targets are actually achieved. In this study, we used a binary logistic regression model to examine the factors – including vehicle ownership, fatality rate, and national income, in addition to level of ambition and duration of target – that contribute to a target’s success. We analyzed 55 quantified road safety targets set by 29 countries from 1981 to 2009, and the results indicate that targets that are in progress and with lower level of ambitions had a higher likelihood of eventually being achieved. Moreover, possible interaction effects on the association between level of ambition and the likelihood of success are also revealed.     

Benchmarking road safety of U.S. states: A DEA-based Malmquist productivity index approach

In this study, a DEA based Malmquist index model was developed to assess the relative efficiency and productivity of U.S. states in decreasing the number of road fatalities. Even though the national trend in fatal crashes has reached to the lowest level since 1949 (Traffic Safety Annual Assessment Highlights, 2010), a state-by-state analysis and comparison has not been studied considering other characteristics of the holistic national road safety assessment problem in any work in the literature or organizational reports. In this study, a DEA based Malmquist index model was developed to assess the relative efficiency and productivity of 50 U.S. states in reducing the number of fatal crashes. The single output, fatal crashes, and five inputs were aggregated into single road safety score and utilized in the DEA-based Malmquist index mathematical model. The period of 2002–2008 was considered due to data availability for the inputs and the output considered. According to the results, there is a slight negative productivity (an average of −0.2 percent productivity) observed in the U.S. on minimizing the number of fatal crashes along with an average of 2.1 percent efficiency decline and 1.8 percent technological improvement. The productivity in reducing the fatal crashes can only be attributed to the technological growth since there is a negative efficiency growth is occurred. It can be concluded that even though there is a declining trend observed in the fatality rates, the efficiency of states in utilizing societal and economical resources towards the goal of zero fatality is not still efficient. More effective policy making towards increasing safety belt usage and better utilization of safety expenditures to improve road condition are derived as the key areas to focus on for state highway safety agencies from the scope of current research.     

Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach

The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in a single-echelon-single-event setting is desirable and indeed critical for some firms, modelling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by the need to consider both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model based on integration of Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) to quantify the ripple effect. We use the DTMC to model the recovery and vulnerability of suppliers. The proposed DTMC model is then equalised with a DBN model in order to simulate the propagation behaviour of supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level. This ripple effect metric is applied to two case studies and analysed. The findings suggest that our model can be of value in uncovering latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritising contingency and recovery policies.     

Road crash fatality rates in France: A comparison of road user types,taking account of travel practices

Background

Travel practices are changing: bicycle and motorized two-wheeler (MTW) use are rising in some of France’s large cities. These are cheaper modes of transport and therefore attractive at a time of economic crisis, but they also allow their users to avoid traffic congestion. At the same time, active transport modes such as walking and cycling are encouraged because they are beneficial to health and reduce pollution. It is therefore important to find out more about the road crash risks of the different modes of transport. To do this, we need to take account of the number of individuals who use each, and, even better, their travel levels.

Method

We estimated the exposure-based fatality rates for road traffic crashes in France, on the basis of the ratio between the number of fatalities and exposure to road accident risk. Fatality data were obtained from the French national police database of road traffic casualties in the period 2007–2008. Exposure data was estimated from the latest national household travel survey (ENTD) which was conducted from April 2007 to April 2008. Three quantities of travel were computed for each mode of transport: (1) the number of trips, (2) the distance traveled and (3) the time spent traveling. Annual fatality rates were assessed by road user type, age and sex.

Results

The overall annual fatality rates were 6.3 per 100 million trips, 5.8 per billion kilometers traveled and 0.20 per million hours spent traveling. The fatality rates differed according to road user type, age and sex. The risk of being killed was 20 to 32 times higher for motorized two-wheeler users than for car occupants. For cyclists, the risk of being killed, both on the basis of time spent traveling and the number of trips was about 1.5 times higher than for car occupants. Risk for pedestrians compared to car occupants was similar according to time spent traveling, lower according to the number of trips and higher according to the distance traveled. People from the 17–20 and 21–29 age groups and those aged 70 and over had the highest rates. Males had higher rates than females, by a factor of between 2 and 3.

Conclusion

When exposure is taken into account, the risks for motorized two-wheeler users are extremely high compared to other types of road user. This disparity can be explained by the combination of speed and a lack of protection (except for helmets). The differential is so great that prevention measures could probably not eliminate it. The question that arises is as follows: with regard to public health, should not the use of MTW, or at least of motorcycles, be deterred? The difference between the fatality risk of cyclists and of car occupants is much smaller (1.5 times higher); besides, there is much room for improvements in cyclist safety, for instance by increasing the use of helmets and conspicuity equipment. Traffic calming could also benefit cyclists, pedestrians and perhaps moped users.     

Sharing the responsibility for driver distraction across road transport systems: A systems approach to the management of distracted driving

Distracted driving is acknowledged universally as a large and growing road safety problem. Compounding the problem is that distracted driving is a complex, multifaceted issue influenced by a multitude of factors, organisations and individuals. As such, management of the problem is not straightforward. Numerous countermeasures have been developed and implemented across the globe. The vast majority of these measures have derived from the traditional reductionist, driver-centric approach to distraction and have failed to fully reflect the complex mix of actors and components that give rise to drivers becoming distracted. An alternative approach that is gaining momentum in road safety is the systems approach, which considers all components of the system and their interactions as an integrated whole. In this paper, we review the current knowledge base on driver distraction and argue that the systems approach is not currently being realised in practice. Adopting a more holistic, systems approach to distracted driving will not only improve existing knowledge and interventions from the traditional approach, but will enhance our understanding and management of distraction by considering the complex relationships and interactions of the multiple actors and the myriad sources, enablers and interventions that make up the distracted driving system. It is only by recognising and understanding how all of the system components work together to enable distraction to occur, that we can start to work on solutions to help mitigate the occurrence and consequences of distracted driving.     

A novel Bayesian approach to reliability modeling: The benefits of uncertainty evaluation in the model selection procedure

This paper proposes a different likelihood formulation within the Bayesian paradigm for parameter estimation of reliability models. Moreover, the assessment of the uncertainties associated with parameters, the goodness of fit, and the model prediction of reliability are included in a systematic framework for better aiding the model selection procedure. Two case studies are appraised to highlight the contributions of the proposed method and demonstrate the differences between the proposed Bayesian formulation and an existing Bayesian formulation.