Outline for a causal model of traffic conflicts and crashes

Road crashes tend to be infrequent but with nontrivial consequences, leading to a long-running interest in identifying surrogate events, such as traffic conflicts, that can support a timely recognition and correction of safety deficiencies. Although a variety of possible surrogates have been suggested, questions remain regarding how crashes and surrogates are related. Using recent developments in causal analysis we propose a simple model which represents crashes and conflicts as resulting from interactions between initiating conditions and evasive actions, and then use this model to identify relationships between these types of events. Our first set of results expresses the probability of a crash as a mixture of probabilities over different sets of initiating conditions, where the mixing probabilities are governed by the evasive action. Our second set of results considers situations where sampling is restricted to non-crash events, and gives conditions where these truncated probabilities can serve as proxies for crash probabilities. We then illustrate how trajectory-based reconstruction can be used to classify initiating events with respect to severity, and to estimate a proxy for the crash probability from a set of incompletely observed non-crash events.     

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.     

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.     

基于时态描述逻辑的Web服务行为匹配

服务的行为匹配在服务自动发现中扮演重要角色,本文分析了Web服务的行为描述要求和服务行为自动发现的特点,建立了服务行为的语义标注和描述框架;给出了服务行为的语义建模方法;通过使用描述逻辑时态扩展的方法匹配服务行为,实现了基于服务行为语义描述和匹配的Web服务自动发现.     

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.     

Perception of traffic risks for older and younger adults

The study examined differences in the perception of traffic risks for older and younger adults. Thirty-four younger participants (mean age 24.7 years) and 30 older participants (mean age 70 years) estimated the number of younger and older people (out of 100,000 people) that were injured in car and pedestrian crashes in a recent year. Both age groups viewed older adults’ risks in pedestrian crashes as higher than those of younger adults, and saw older adults’ risks in car crashes as identical to the risks for younger adults. Both age groups assessed the risks for their own group quite accurately, but erred in their assessment of the risk for the other group. Older participants tended to overestimate the risk for younger adults, and younger participants tended to underestimate the risk for older adults. These results point to the need to enhance awareness of the age-related increase in traffic risk, which could potentially promote more considerate driving behavior.     

A model of traffic crashes in New Zealand

The aim of this study was to examine the changes in the trend and seasonal patterns in fatal crashes in New Zealand in relation to changes in economic conditions between 1970 and 1994. The Harvey and Durbin (Journal of the Royal Statistical Society 149 (3) (1986) 187-227) structural time series model (STSM), an 'unobserved components' class of model, was used to estimate models for quarterly fatal traffic crashes. The dependent variable was modelled as the number of crashes and three variants of the crash rate (crashes per 10,000 km travelled, crashes per 1,000 vehicles, and crashes per 1000 population). Independent variables included in the models were unemployment rate (UER), real gross domestic product per capita, the proportion of motorcycles, the proportion of young males in the population, alcohol consumption per capita, the open road speed limit, and dummy variables for the 1973 and 1979 oil crises and seat belt wearing laws. UERs, real GDP per capita, and alcohol consumption were all significant and important factors in explaining the short-run dynamics of the models. In the long-run, real GDP per capita was directly related to the number of crashes but after controlling for distance travelled was not significant. This suggests increases in income are associated with a short-run reduction in risk but increases in exposure to a crash (i.e. distance travelled) in the long-run. A 1% increase in the open road speed limit was associated with a long-run 0.5% increase in fatal crashes. Substantial reductions in fatal crashes were associated with the 1979 oil crisis and seat belt wearing laws. The 1984 universal seat belt wearing law was associated with a sustained 15.6% reduction in fatal crashes. These road policy factors appeared to have a greater influence on crashes than the role of demographic and economic factors.     

A re-parameterisation of the Power Model of the relationship between the speed of traffic and the number of accidents and accident victims

This paper presents a re-analysis of the Power Model of the relationship between the mean speed of traffic and road safety. Past evaluations of the model, most recently in 2009, have broadly speaking supported it. However, the most recent evaluation of the model indicated that the relationship between speed and road safety depends not only on the relative change in speed, as suggested by the Power Model, but also on initial speed. This implies that the exponent describing, for example, a 25% reduction in speed will not be the same when speed changes from 100 km/h to 75 km/h as it will when speed changes from 20 km/h to 15 km/h. This paper reports an analysis leading to a re-parameterisation of the Power Model in terms of continuously varying exponents which depend on initial speed. The re-parameterisation was accomplished by fitting exponential functions to data points in which changes in speed and accidents were sorted in groups of 10 km/h according to initial speed, starting with data points referring to the highest initial speeds. The exponential functions fitted the data extremely well and imply that the effect on accidents of a given relative change in speed is largest when initial speed is highest.     

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).     

A simulator study on the impact of traffic calming measures in urban areas on driving behavior and workload

This study examined the impact of traffic calming measures (TCM) on major roads in rural and urban areas. More specifically we investigated the effect of gate constructions located at the entrance of the urban area and horizontal curves within the urban area on driving behavior and workload. Forty-six participants completed a 34 km test-drive on a driving simulator with eight thoroughfare configurations, i.e., 2 (curves: present, absent) × 2 (gates: present, absent) × 2 (peripheral detection task (PDT): present, absent) in a within-subject design.     

Safety performance of traffic phases and phase transitions in three phase traffic theory

Crash risk prediction models were developed to link safety to various phases and phase transitions defined by the three phase traffic theory. Results of the Bayesian conditional logit analysis showed that different traffic states differed distinctly with respect to safety performance. The random-parameter logit approach was utilized to account for the heterogeneity caused by unobserved factors. The Bayesian inference approach based on the Markov Chain Monte Carlo (MCMC) method was used for the estimation of the random-parameter logit model. The proposed approach increased the prediction performance of the crash risk models as compared with the conventional logit model. The three phase traffic theory can help us better understand the mechanism of crash occurrences in various traffic states. The contributing factors to crash likelihood can be well explained by the mechanism of phase transitions. We further discovered that the free flow state can be divided into two sub-phases on the basis of safety performance, including a true free flow state in which the interactions between vehicles are minor, and a platooned traffic state in which bunched vehicles travel in successions. The results of this study suggest that a safety perspective can be added to the three phase traffic theory. The results also suggest that the heterogeneity between different traffic states should be considered when estimating the risks of crash occurrences on freeways.     

Drug use and the severity of a traffic accident

Several studies have showed that driving under the influence of alcohol and/or certain illicit or medicinal drugs increases the risk of a (severe) crash. Data with respect to the question whether this also leads to a more severe accident are sparse. This study examines the relationship between the use of alcohol, illicit drugs and/or medicinal drugs and the severity of an accident within a group of drivers that were involved in a crash in The Netherlands. Blood samples of 993 drivers, collected in the period from October 1998 through September 1999, were linked to accident characteristics as available from the National Transport Research Centre. The outcome measure was the severity of the accident. An accident was considered severe when the accident had resulted in hospital admission or death. All the blood samples obtained after the accident were screened for the presence of alcohol, illicit drugs (opiates, amphetamines and amphetamine-like substances, cocaine and metabolites, methadone, cannabinoids) and medicinal drugs (benzodiazepines, barbiturates and tricyclic antidepressants). The strength of the associations between exposure to the different classes of alcohol/drugs/medicines and the severity of the accident was evaluated using logistic regression analysis and were expressed as odds ratios (OR), adjusted for age, gender, time of the day, day of the week and urban area. The most frequently detected drugs were cannabinoids, benzodiazepines and cocaine. Our results showed no clear association between the use of alcohol, illicit drug and/or medicinal drug use and the severity of the accident. Given the process of obtaining blood samples from drivers involved in accidents and the retrospective nature of the study, we cannot rule out the occurrence of selection bias. Therefore, our findings need further confirmation.     

Impact of distracted driving on safety and traffic flow

Studies have documented a link between distracted driving and diminished safety; however, an association between distracted driving and traffic congestion has not been investigated in depth. The present study examined the behavior of teens and young adults operating a driving simulator while engaged in various distractions (i.e., cell phone, texting, and undistracted) and driving conditions (i.e., free flow, stable flow, and oversaturation). Seventy five participants 16–25 years of age (split into 2 groups: novice drivers and young adults) drove a STISIM simulator three times, each time with one of three randomly presented distractions. Each drive was designed to represent daytime scenery on a 4 lane divided roadway and included three equal roadway portions representing Levels of Service (LOS) A, C, and E as defined in the 2000 Highway Capacity Manual. Participants also completed questionnaires documenting demographics and driving history. Both safety and traffic flow related driving outcomes were considered. A Repeated Measures Multivariate Analysis of Variance was employed to analyze continuous outcome variables and a Generalized Estimate Equation (GEE) Poisson model was used to analyze count variables. Results revealed that, in general more lane deviations and crashes occurred during texting. Distraction (in most cases, text messaging) had a significantly negative impact on traffic flow, such that participants exhibited greater fluctuation in speed, changed lanes significantly fewer times, and took longer to complete the scenario. In turn, more simulated vehicles passed the participant drivers while they were texting or talking on a cell phone than while undistracted. The results indicate that distracted driving, particularly texting, may lead to reduced safety and traffic flow, thus having a negative impact on traffic operations. No significant differences were detected between age groups, suggesting that all drivers, regardless of age, may drive in a manner that impacts safety and traffic flow negatively when distracted.     

基于Web服务的PDC钻头井底流场分析平台研究

为解决常规PDC(polycrystalline diamond compact)钻头井底流场水力性能数值模拟分析时存在的门槛高、耗时长、重复工作量多等问题,建立了基于Web服务的PDC钻头井底流场分析平台。平台框架由上至下分为软件接口层、分析服务层和结果输出层。在服务端通过知识封装技术实现钻头井底流场分析过程的自动化、参数化和集成化;利用Web封装技术对参数化流场分析模板进行封装和管理,从而实现客户端基于Web服务的PDC钻头井底流场的分析研究。实例验证表明,采用该平台分析PDC钻头水力性能可有效降低CFD软件的使用门槛,提高钻头井底流场的分析效率,节约人力与物力,能够为石油装备企业提供规范化的技术支持。     

Improved recovery of admissible stress in domain decomposition methods — application to heterogeneous structures and new error bounds for FETI‐DP

This paper investigates the question of the building of admissible stress field in a substructured context. More precisely, we analyze the special role played by multiple points. This study leads to (1) an improved recovery of the stress field, (2) an opportunity to minimize the estimator in the case of heterogeneous structures (in the parallel and sequential case), and (3) a procedure to build admissible fields for dual‐primal finite element tearing and interconnecting and balancing domain decomposition by constraints methods leading to an error bound that separates the contributions of the solver and of the discretization. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic recrystallization of Ni-base alloys—Experimental results and comparisons with simulations

The dynamic recrystallization of the Ni-base alloy Böhler L306 VMR (Alloy 80A) in a transient state was investigated both by light microscopy and electron backscatter diffraction (EBSD), and the experimental results were compared with those from simulations. Subgrain structures of the size of the recrystallized grains were observed close to the grain boundaries of the original grains. With increasing strain a texture developed in the deformed fraction. Strong twinning was found in the recrystallized fraction, with area fractions of the twinned grains of around 80% for higher strains. Thus the measured grain sizes strongly depend on the handling of the twins. A pronounced increase in the average grain size of the recrystallized fraction with increasing strain (time) was only observed after twin removal. There was generally good agreement between the measured and the simulated results.     

A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey

A retrospective cross-sectional study is conducted analysing 11,771 traffic accidents reported by the police between January 2008 and December 2013 which are classified into three injury severity categories: fatal, injury, and no injury. Based on this classification, a multinomial logit analysis is performed to determine the risk factors affecting the severity of traffic injuries. The estimation results reveal that the following factors increase the probability of fatal injuries: drivers over the age of 65; primary-educated drivers; single-vehicle accidents; accidents occurring on state routes, highways or provincial roads; and the presence of pedestrian crosswalks. The results also indicate that accidents involving cars or private vehicles or those occurring during the evening peak, under clear weather conditions, on local city streets or in the presence of traffic lights decrease the probability of fatal injuries. This study comprises the most comprehensive database ever created for a Turkish sample. This study is also the first attempt to use an unordered response model to determine risk factors influencing the severity of traffic injuries in Turkey.     

Multivariate receptor models—current practice and future trends

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and projecting future developments. The challenges common to all fields are: (1) the need for better and less subjective methods to estimate the number of factors to include in the model; (2) better documentation of the algorithms in the open literature; and (3) user-friendly software to enlarge the user community. Future trends will likely center on the development of models based on self-modeling curve resolution (SMCR) and constrained weighted least squares methods such as positive matrix factorization.     

Less is more: the influence of traffic count on drinking and driving behaviour

Drinking and driving road checks are often organized with either a clear prevention or repression objective in mind. The objective of a prevention strategy is to make as many people as possible believe that police officers are enforcing drinking and driving laws and that drinking drivers will most likely be caught. As such, targeting high traffic count road sites with high-visibility road checks is a priority because it serves to increase awareness of the enforcement activity. An alternative to this prevention approach is the “repression” approach that involves targeting times and places where the highest number of drinking drivers are to be expected. Rather than attempting to affect the subjective chance of getting caught, this approach seeks to increase the objective likelihood of getting caught; the aim is to apprehend as many drinking drivers as possible. Regardless of the chosen strategy, there is a need to understand how traffic count influences drinking and driving behaviour as traffic count may play a role in a police officer's choice of sites for a road check. The objective of this paper is to shed some light on this relationship between drinking and driving behaviour and traffic count. In this paper, data from a roadside survey, carried out in British Columbia in 2003, are used. A two-level logistic regression analysis was carried out with data from 2627 drivers coming from 48 different road sites to replicate a model that was previously obtained with comparable data from a Belgian roadside survey, also carried out in 2003. The present study successfully replicated the findings of the Belgian model, substantiating that the probability for drivers to be drinking and driving significantly decreases with an increasing level of traffic count. This supports the suggestion that drinking drivers avoid high traffic count road sites. The relevance of these findings with respect to organizing preventive or repressive road checks and possible confounding variables are discussed at the end of this paper.