Driver Celeration Behavior and the Prediction of Traffic Accidents

A study was undertaken to investigate whether driver celeration (overall mean speed change) behavior can predict traffic accident involvement. Also, to test whether acceleration, deceleration or the combined celeration measure was the better predictor. Bus driver celeration behavior was measured repeatedly in real traffic, driving en route, and correlated with accidents for which the drivers were deemed at least partly responsible. Correlations around .20 were found in several samples between celeration behavior and culpable accidents for a 2-year period. The results show that although celeration behavior is only semi-stable over time, it predicts with some accuracy individual accident involvement over 2 years. The predictive power of acceleration and deceleration was slightly lower than the combined measure, in accordance with theory. The correlations found were strong enough to warrant the use of celeration behavior as a predictive variable for transportation companies in their safety work.     

Speed choice versus celeration behavior as traffic accident predictor

INTRODUCTION: The driver celeration behavior theory predicts that this variable is superior to all other variables as a predictor of individual traffic accident involvement, including the ever-important speed parameter. The study was undertaken to test this prediction. Also, it was expected that most variables would associate fairly strongly. METHOD: The use of speed choice as a predictor of individual traffic accident record was discussed, and four different variants of this variable (maximum, net mean, gross mean, and standard deviation of speed) identified. These variables were then compared to celeration behavior as predictors of accident record of bus drivers in the same set of data. RESULTS: Celeration behavior was found to be slightly superior, in accordance with the prediction made from the driver celeration behavior theory, although the differences were not significant. Furthermore, the predictor variables were found to associate fairly strongly between themselves, with the exception of gross mean speed, and to have fair stability over time, especially when aggregated. CONCLUSIONS: These results tentatively confirm some of the predictions made from the driver celeration behavior theory. As the results for accidents were in the expected direction, but not significant, and the maximum speed variable may have suffered from a ceiling effect, the conclusion is provisional. Impact on industry: The correlations found were strong enough to warrant the use of celeration behavior as a predictive variable for transportation companies in their safety work.     

Aggregation of driver celeration behavior data: Effects on stability and accident prediction

Predictions about effects of aggregating driver celeration data were tested in a set of data where bus drivers’ behavior had been measured repeatedly over three years in a city environment. For drivers with many measurements, this data was correlated with the drivers’ accident record at various levels of aggregation over measurements. A single measurement (one sample) was seldom a significant predictor, but for each drive added to a mean, the variation explained in accident record was increased by about 1%. Also, correlations between measurements increased when these were aggregated, and the association with number of passengers (a proxy for traffic density) decreased somewhat, all as predicted. These results show that although driver celeration behavior is only semi-stable across time and environments, aggregating measurements increases both stability and predictive power versus accidents considerably. The celeration variable is therefore promising as a tool for identifying dangerous drivers, if these can be measured repeatedly, or, even better, continuously.     

Effects of passengers on bus driver celeration behavior and incident prediction

PROBLEM: Driver celeration (speed change) behavior of bus drivers has previously been found to predict their traffic incident involvement, but it has also been ascertained that the level of celeration is influenced by the number of passengers carried as well as other traffic density variables. This means that the individual level of celeration is not as well estimated as could be the case. Another hypothesized influence of the number of passengers is that of differential quality of measurements, where high passenger density circumstances are supposed to yield better estimates of the individual driver component of celeration behavior. METHOD: Comparisons were made between different variants of the celeration as predictor of traffic incidents of bus drivers. The number of bus passengers was held constant, and cases identified by their number of passengers per kilometer during measurement were excluded (in 12 samples of repeated measurements). RESULTS: After holding passengers constant, the correlations between celeration behavior and incident record increased very slightly. Also, the selective prediction of incident record of those drivers who had had many passengers when measured increased the correlations even more. CONCLUSIONS: The influence of traffic density variables like the number of passengers have little direct influence on the predictive power of celeration behavior, despite the impact upon absolute celeration level. Selective prediction on the other hand increased correlations substantially. This unusual effect was probably due to how the individual propensity for high or low celeration driving was affected by the number of stops made and general traffic density; differences between drivers in this respect were probably enhanced by the denser traffic, thus creating a better estimate of the theoretical celeration behavior parameter C. The new concept of selective prediction was discussed in terms of making estimates of the systematic differences in quality of the individual driver data.     

Long-Term Prediction of Traffic Accident Record From Bus Driver Celeration Behavior

Driver celeration (speed change) behavior of bus drivers measured a number of times was used to predict their culpable accidents over increasing time periods. It was found that predictive power was considerable (>.30 correlation) over 5 years of time with aggregated celeration (mean of repeated measurements) as independent variables, and there were also indications that power reached even further, although too low Ns made these results unreliable. Similarly, there were indications of even stronger correlations with increased aggregation of celeration values. The results were discussed in terms of the methodology needed to bring out such results, and the stability of accident-causing behavior over time.     

Long-term prediction of traffic accident record from bus driver celeration behavior.

Driver celeration (speed change) behavior of bus drivers measured a number of times was used to predict their culpable accidents over increasing time periods. It was found that predictive power was considerable (>.30 correlation) over 5 years of time with aggregated celeration (mean of repeated measurements) as independent variables, and there were also indications that power reached even further, although too low Ns made these results unreliable. Similarly, there were indications of even stronger correlations with increased aggregation of celeration values. The results were discussed in terms of the methodology needed to bring out such results, and the stability of accident-causing behavior over time.     

The relation of non-culpable traffic incidents to bus drivers' celeration behavior

PROBLEM: The driver celeration behavior theory predicts that celerations are associated with incidents for which the driver has some responsibility in causing, but not other incidents. METHOD: The hypothesis was tested in 25 samples of repeated measurements of bus drivers' celeration behavior against their incidents for two years. RESULTS: The results confirmed the prediction; in 18 samples, the correlation for culpable incidents only was higher than for all incidents, despite the higher means of the latter. Non-culpable incidents had correlations close to zero with celeration. DISCUSSION: It was pointed out that most individual crash prediction studies have not made this differentiation, and thus probably yielded underestimates of the associations sought, although the effect is not strong, due to non-culpable accident involvements being few (less than a third of the total). The methods for correct identification of culpable incident involvements were discussed.     

从道路交通事故统计分析对比谈预防措施

笔者通过对比我国与世界上部分发达国家近十年来在道路交通事故方面的统计数据 ,并根据经济发展水平与机动车保有量的正比例关系及当前我国道路交通事故现状 ,分析得出道路交通安全事故的发生 ,与人、车辆、道路、环境信息及管理等因素具有密切关系 ,其中人 (尤其是驾驶员 )作为交通行为的主体 ,是道路交通事故诱因中一个关键性因素。由此 ,作者提出了要有效预防和减少道路交通事故的发生 ,必须将人、车、路、环境信息和管理等诸因素作为一个有机整体系统思考 ,且在未来的道路交通发展中应引入交通稳静化理念 ,以实现道路交通的安全、畅通与高效     

高速公路交通安全现状分析及管理对策

为了分析高速公路交通安全现状,提出高速公路交通安全综合管理对策,采用对比分析法,研判当前高速公路交通安全形势,统计分析高速公路交通事故特征规律,提出基于“6E”的高速公路交通安全系统管理对策。以1994～2013年期间的高速公路交通事故统计数据为分析对象,采用事故起数、事故死亡人数、事故率、致死率、重大事故起数等作为评价指标,对比交通事故总量和不同等级公路事故情况,分析了当前我国高速公路交通安全形势。从交通安全执法管理的角度,对高速公路交通事故发生时间、事故形态、肇事车辆类型、事故原因、肇事驾驶人驾龄等进行了特征分析,在立法、教育、工程、执法、急救、评价6个方面,提出了高速公路交通安全管理系统化对策,为进一步认识当前高速公路交通安全形势,完善高速公路交通安全管理提供技术支持。     

Social desirability effects in driver behavior inventories

Problem

The use of lie scales to control for common method variance in driver behavior inventories has been very limited. Given that such questionnaires often use self-reported safety variables as criteria, and have social implications, the risk of artefactual associations is high.

Method

A questionnaire containing scales from several well known driver inventories that have been claimed to predict traffic accident involvement was distributed three times to a group of young drivers in a driver education program, as well as a random group twice. The Driver Impression Management scale (DIM) was used to control for socially desirable responding.

Results

For all behavior scales, the correlation with the DIM scale was substantial. If a scale correlated with self-reported crashes, the amount of predictive power was more than halved when social desirability was controlled for. Results were similar for both samples and all waves. The predictive power of the behavior scales was not increased when values were averaged over questionnaire waves, as should have been the case if the measurement and predictive power were valid. Results were similar for self-reported penalty points. The present results indicate that even the most well-known and accepted psychometric scales used in driver research are susceptible to social desirability bias.

Discussion

As social desirability is only one of a number of common method variance mechanisms that can create artefactual associations, and the great popularity of the self-report methodology, the problem for traffic research is grave.

Impact on industry

Organizations that fund traffic safety research need to re-evaluate their policies regarding what methods are acceptable. The use of self-reported independent and dependent variables can lead to directly misleading results, with negative effects on traffic safety.     

驾驶人视觉注意力需求与驾驶行为特性的关系

采用驾驶模拟系统平台,以不同车道类型及不同交通流状态(自由流/稳定流/不稳定流/强制流)为虚拟试验场,应用心理试验设计方法,测试得到21名被试在不同交通流状态下的视觉注意力需求及驾驶行为。对5种车道类型及4种交通流状态下被试驾驶时的视觉注意力需求特性和驾驶行为特性数据进行分析,得到不同车道不同交通流状态下的视觉注意力需求变化情况。利用多元回归方法分析了不同交通流状态下驾驶人视觉注意力需求与驾驶行为之间的关系,并构建了基于驾驶行为特性的驾驶人视觉注意力需求预测模型。结果表明,驾驶人视觉注意力需求与制动次数、油门踏板位移和车辆轨迹偏差之间存在相关关系。     

Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model

ProblemThe severity of motorway accidents that occurred on the hard shoulder (HS) is higher than for the main carriageway (MC). This paper compares and contrasts the most important factors affecting the severity of HS and MC accidents on motorways in England.MethodUsing police reported accident data, the accidents that occurred on motorways in England are grouped into two categories (i.e., HS and MC) according to the location. A generalized ordered logistic regression model is then applied to identify the factors affecting the severity of HS and MC accidents on motorways. The factors examined include accident and vehicle characteristics, traffic and environment conditions, as well as other behavioral factors.ResultsResults suggest that the factors positively affecting the severity include: number of vehicles involved in the accident, peak-hour traffic time, and low visibility. Differences between HS and MC accidents are identified, with the most important being the involvement of heavy goods vehicles (HGVs) and driver fatigue, which are found to be more crucial in increasing the severity of HS accidents.Practical applicationsMeasures to increase awareness of HGV drivers regarding the risk of fatigue when driving on motorways, and especially the nearside lane, should be taken by the stakeholders.     

Personal exposure risk factors in pedestrian accidents in Bahrain

Pedestrians are involved in traffic accidents due to many reasons. It is generally thought that personal background of pedestrians has an effect on their involvement rate in the road traffic accidents. Identifying these characteristics would lead to a better understanding of pedestrian accident pattern so that the resources in the field of education, engineering, and enforcement could be used in better ways. This study attempts to test the hypothesis mentioned earlier. The investigated personal background includes the following characteristics: gender, type, age, nationality, and educational background. The data was reduced from vast number of pedestrian injury accident reports in the Kingdom of Bahrain. The actual accident records were categorized according to these characteristics and compared to their exposure risk. It was assumed that the exposure risk, which is the expected number of accidents for each category of the pedestrians, was in proportion to their presence in the pedestrian population. Another study was carried out in parallel to observe the pedestrian characteristics in Bahrain. The results of the two studies were analyzed statistically using Chi-square method to compare the actual to the expected accident frequencies. The whole Kingdom of Bahrain population statistics were used wherever the information on the pedestrian population was not available. The findings revealed that personal characteristics considered in this study have significant influence on pedestrian’s involvement in traffic accidents. The results also showed that pedestrians with the following characteristics were probably showing risk to exposure to accidents more than other categories: male, young (0–12 years) and old (50 years and over), non-local, and those with low educational background.     

基于APRIORI-TAN的交通事故伤害分析与预测*

为探究道路交通事故因素和事故伤害的相关性,以2 467起涉及人员伤亡的交通事故为数据集,运用Apriori算法分别挖掘事故伤害关联规则,并结合社会网络分析的可视化和核心-边缘分析构建受伤事故和死亡事故的关联规则网络。结果表明:事故伤害程度与事故时间、道路条件和交通环境等因素关系紧密,尤其死亡事故与碰撞固定物、人行横道事故、高速公路、高速道路、非市区、酒驾和超速存在高相关性。基于树型贝叶斯网络（TAN）构建事故伤害程度的预测模型,预测结果准确率可达87.56%。     

高速公路事故责任认定探讨

在事故责任认定中应同时考虑司机、交警、管理部门和道路条件等存在的问题,分析并量化各方面应承担的责任,而不能像目前,事故责任几乎仅由司机一方100%承担。以开阳高速(开平到阳江)“1·23”特大交通事故为例,着重论述了当前我国高速公路管理体制存在的弊端,事故责任认定和事故赔偿的不合理性,紧急救援体系的不完善性。并对高速公路事故责任的量化,交通警察现场指挥能力的提高,高速公路管理部门责任的落实与监督,应急救援及强制保险理赔体系的进一步完善提出相应的建议和措施,促进我国交通事故责任认定与落实全过程的科学、合理、公平及规范化。     

基于FDA事故致因模型的铁路行车安全管理研究

为了从安全信息视角深入分析铁路行车事故原因,基于信息学、管理科学和行为科学等相关理论知识,结合铁路行车事故致因原理,构建铁路行车FDA(Forecast-Decision-Action)事故致因模型。该模型由行车人员、铁路运输企业和国家铁路局3条事故致因子链和1条事故致因主链构成,进一步归纳总结出3条子事故域、1条总事故域及各阶段正常运行所需要的安全信息。研究结果表明:破坏事故致因主链或破坏3条事故致因子链,防止不安全物态,可以有效预防铁路行车事故发生。以"4·28胶济铁路特别重大交通事故"为例论证模型具有实用性。     

大货车驾驶员交通心理与交通安全的研究

笔者从交通心理学的角度,对大货车驾驶员在行驶过程中的心理状况、因攻击性驾驶行为、强烈的冒险动机的驱使、对道路期望心理的失衡及交通安全感偏差等不良心理因素而导致恶性交通事故进行分析。通过研究得出以下结论大货车驾驶员是一特殊的群体,应重视对该群体的教育、培训与管理,从而提高驾驶员的职业道德水平、性格品质、交通安全感及驾驶技能,以改善道路安全环境和交通安全状况、降低道路交通事故发生率。     

Behavior and lifestyle characteristics of male Kuwaiti drivers

Introduction: The high traffic accident risk among young drivers is a well-known and well-documented fact in most countries. Lifestyle has proven to affect driving behavior as well as accident risk. This study covers the lifestyle component of the problems related to young male Kuwaiti drivers’ accident risk. Methods: The purpose of the study is to measure the relationship between lifestyle and accident risk. Lifestyle is measured through a questionnaire, where 302 male Kuwaiti drivers (mean age = 28 years; range 25-35 years) answer 39 questions related to behavioral and social factors, road conditions, police enforcement, and life satisfaction. They also report their involvement in accidents and traffic violations. Results: The questionnaire's validity and reliability (Cronbach's alpha = 0.7) were achieved. Principal component analysis reduced the 39 items on the questionnaire to 5 factors. Inadequate police enforcement is strongly correlated (r = 0.862) to accident risk and traffic violations and is thus considered the best predictor of traffic accidents in Kuwait. Impact on Industry: As driving-related incidents (on-the-job and off-the-job) are a significant source of fatalities and lost-work-days, the study points to the importance of considering cultural factors in the design of comprehensive safety programs for industry.     

海底隧道交通事故风险耦合演化机理研究

为明晰海底隧道交通系统内部风险因素之间的耦合关联和事故演化机理,从驾驶人、道路、车辆、环境和管理5方面阐释海底隧道风险因素内涵,定性分析风险因素之间的耦合作用,建立海底隧道交通事故风险演化立体网络模型,提出海底隧道风险因素量化评价标准;并构建海底隧道交通事故风险耦合尖点突变模型,深入分析海底隧道交通系统风险状态演化的主要形式;结合应用实例,对海底隧道风险因素耦合进行计算评价。结果表明：多因素耦合对海底隧道交通系统产生的风险影响大于单因素耦合,驾驶人因素风险相对最大,车辆因素风险相对最小,并从人、物2个因素方面提出针对性的事故风险预防措施。研究结果可为海底隧道交通安全管控提供理论参考和借鉴。     

The Driver Behaviour Questionnaire as accident predictor; A methodological re-meta-analysis

IntroductionThe Manchester Driver Behaviour Questionnaire (DBQ) is the most commonly used self-report tool in traffic safety research and applied settings. It has been claimed that the violation factor of this instrument predicts accident involvement, which was supported by a previous meta-analysis. However, that analysis did not test for methodological effects, or include unpublished results.MethodThe present study re-analysed studies on prediction of accident involvement from DBQ factors, including lapses, and many unpublished effects. Tests of various types of dissemination bias and common method variance were undertaken.ResultsOutlier analysis showed that some effects were probably not reliable data, but excluding them did not change the results. For correlations between violations and crashes, tendencies for published effects to be larger than unpublished ones and for effects to decrease over time were observed, but were not significant. Also, using the mean of accidents as proxy for effect indicated that studies where effects for violations are not reported have smaller effect sizes. These differences indicate dissemination bias. Studies using self-reported accidents as dependent variables had much larger effects than those using recorded accident data. Also, zero-order correlations were larger than partial correlations controlled for exposure. Similarly, violations/accidents effects were strong only when there was also a strong correlation between accidents and exposure. Overall, the true effect is probably very close to zero (r < .07) for violations versus traffic accident involvement, depending upon which tendencies are controlled for.ConclusionsMethodological factors and dissemination bias have inflated the published effect sizes of the DBQ. Strong evidence of various artefactual effects is apparent.Practical applicationsA greater level of care should be taken if the DBQ continues to be used in traffic safety research. Also, validation of self-reports should be more comprehensive in the future, taking into account the possibility of common method variance.