Introducing a multi-variate classification method: Risky driving acceptance among different heterogeneous driver sub-cultures

Introduction: Heterogeneous driving populations with many different origins are likely to have various sub-cultures that comprise of drivers with shared driver characteristics, most likely with dissimilar traffic safety cultures. An innovative methodology in traffic safety research is introduced which is beneficial for large datasets with multiple variables, making it useful for the multi-variate classification of drivers, driving attitudes and/or (risky) driving behaviours. Method: With the application of multidimensional scaling analysis (MDS), this study explores traffic safety culture in the State of Qatar using a questionnaire and investigates the similarity patterns between the questionnaire items, aiming to classify attitudes towards risky driving behaviours into themes. MDS is subsequently applied to classify drivers within a heterogeneous driving sample into sub-cultures with shared driver characteristics and different risky driving attitudes. Results: Results show that acceptance of speeding is highest among the young Arabic students and acceptance of distraction and drivers’ negligence such as phone use and not wearing a seatbelt is highest among male Arab drivers. Acceptance of extreme risk-taking like intoxicated driving and red-light running is highest among South-Asian business drivers. Conclusion: It is important and practical to understand risky behavioural habits among sub-cultures and thereby focussing on groups of drivers instead of individuals, because groups are easier to approach and drivers within sub-cultures are found to influence each other. By indicating which groups of drivers are most likely to perform specific risky driving themes, it is possible to target these groups and effectively emphasise certain subsets of risky driving behaviours during training or traffic safety education. Practical Applications: This study provides guidance for the improvement of driver education and targeted traffic safety awareness campaigns, intending to make changes to attitudes and habits within specific driver sub-cultures with the aim to improve traffic safety on the longer term.     

Identification of aggressive driving from naturalistic data in car-following situations

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.     

Are older drivers’ driving patterns during an on-road driving task representative of their real-world driving patterns?

Abstract

Objective: The current study investigated whether older drivers’ driving patterns during a customized on-road driving task were representative of their real-world driving patterns.

Methods: Two hundred and eight participants (male: 68.80%; mean age?=?81.52 years, SD?=?3.37 years, range?=?76.00–96.00 years) completed a customized on-road driving task that commenced from their home and was conducted in their own vehicle. Participants’ real-world driving patterns for the preceding 4-month period were also collected via an in-car recording device (ICRD) that was installed in each participant’s vehicle.

Results: During the 4-month period prior to completing the on-road driving task, participants’ median real-world driving trip distance was 2.66?km (interquartile range [IQR]?=?1.14–5.79?km) and their median on-road driving task trip distance was 4.41?km (IQR?=?2.83–6.35?km). Most participants’ on-road driving task trip distances were classified as representative of their real-world driving trip distances (95.2%, n?=?198).

Conclusions: These findings suggest that most older drivers were able to devise a driving route that was representative of their real-world driving trip distance. Future research will examine whether additional aspects of the on-road driving task (e.g., average speed, proportion of trips in different speed zones) are representative of participants’ real-world driving patterns.     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

基于情感思维的安全管理模式研究

为促进安全管理,提高效率与效果,对基于情感思维的安全管理进行研究,将情感思维融于安全管理,基于情感思维的定义、内涵及功能,具体分析安全管理中情感思维的功能及其功能综合作用,建立安全管理中情感思维功能作用理论模型;根据该理论模型分析情感思维驱动力及强化作用及其对安全目标实现的影响;建立基于情感思维的安全管理设计模式,最后提出优化安全管理的措施与方法,以促进安全管理的实施。研究表明:基于情感思维的安全管理研究,能较好地体现安全管理人本原理,发挥情感的能动作用,将理性融入其中与情感进行平衡,实现安全管理的优化。     

丹阳市宅基地使用权流转意愿的影响因素及相互作用

通过丹阳市4个村320户农户实地问卷调查,分析了丹阳市宅基地使用权流转意愿的影响因素,由Logistic回归分析模型,给出了影响因素的驱动力、贡献率的数学表达。结果表明：文化程度、宅基地个数、家庭总收入等影响因素对流转意愿呈正相关;户主年龄、家庭人口数量等影响因素对流转意愿呈负相关。同一农户的影响因素在不同阶段对流转意愿度的贡献率不同,家庭收入和户主年龄是影响宅基地流转的主要因素,他们对宅基地流转分别起着推力和阻力作用,在相互作用过程中,两者之间彼此消长。实地抽样结果分析表明,丹阳农村户主文化程度若为初中且年龄在35岁时,家庭收入能达到34 000元的话,宅基地使用权流转意愿度超过50%。以后每年家庭收入能有一定增速的话,农户流转意愿度将持续保持在50%以上。     

1994~2006年上海市土地利用时空变化特征及驱动力分析

快速发展的都市地区是区域土地利用变化研究热点。基于上海市1994、2000、2003和2006年4期航空遥感影像资料数据,运用人机交互目视解译方法建立上海土地利用空间数据库,并结合GIS空间分析、数理统计方法与土地利用动态度模型,对上海市1994~2006年土地利用时空变化特征进行了深入分析。结果表明：12 a间,上海市综合土地利用动态度为207%;在各种用地变化中,耕地面积变化最大,共减少了94 00772 hm2,减幅达233%;从耕地转移来看,转变为城镇建设用地（包括工业仓储用地、交通用地、城镇居民点用地和其他用地）面积共74 82586 hm2,占耕地面积总流出量的6712%;可见城镇建设用地急剧扩张而大量占用耕地,是上海市土地利用变化的主要特征;上海市土地利用变化的另一个重要特征是城市绿地的大幅增加,12 a间城市绿地面积共增加了9 29241 hm2,单一动态度为1876%。从上海土地利用变化的区域特征来看,各区的综合土地利用动态度差异明显,且表现出明显的中心城区-近郊-远郊递减趋势,其中浦东新区综合土地利用动态度最大,崇明县最小,这种差异与各区（县）的区位条件、经济发展状况以及政策等因素密切相关。通过对上海LUCC的驱动力定性定量分析表明,社会经济因素包括人口分布及变化、经济增长、人民生活水平的提高、政策以及国际大型活动的举办均对上海土地利用格局及其变化产生了重要的影响     

耕地集约利用影响因素的协整分析

耕地利用的集约度实质上是农户在其所面临的各种经济和制度环境约束条件下,所采取的不同耕种行为的直接结果和体现.本文试借助分析影响农户耕种行为的相关因素来探讨其对耕地集约利用的影响.利用Eviews 6.0统计软件,综合应用协整理论和误差修正模型,以湖北省为例,剖析耕地集约利用与其影响因素的长期均衡关系和短期波动效应.研究表明:在长期,户均农业劳动力人数、农业比较收入和耕地的产权保障对耕地利用的集约度存在正向影响,其增加1个百分点,将导致耕地利用的集约度分别提高0.166、0.430和0.035个百分点;农民家庭人均农业生产支出和农业生产资料价格指数对耕地利用的集约度存在负向影响,其增加1个百分点,将导致耕地利用的集约度分别下降0.004和0.008个百分点.而在短期,各影响因素对耕地利用集约度的作用方向相同而作用力大小不同.户均农业劳动力人数和农业比较收入对耕地集约利用的作用程度在短期与在长期是相似的;但耕地的产权保障和农业生产成本的变化对耕地集约利用的影响却更多体现在长期.因此,立足于稳定产权和降低农业生产成本的农业政策导向更有利于在长期保护耕地,提高耕地资源的配置和利用效率.     

模拟车用工况下5kW燃料电池尾气净化系统

模拟了车用工况下燃料电池发动机脉冲式排氢的特点,设计开发了一套质子交换膜燃料电池的尾气净化系统,并以5 kW燃料电池的尾气排放为例,研究其净化效果。研究了电池阳极排放氢气缓冲前后尾气中氢气浓度的变化,以及不同空速条件下氢气的去除效果。结果表明：电堆阳极排放氢气经过缓冲处理后,尾气中氢气浓度趋于平稳,大部分工况下处在2%以下,达到了安全处理的要求;尾气中氢气的去除效果与空速密切相关。对自制的整体式催化剂而言,当空速低于20000 h^-1时,氢气的去除率能达到约95%,而当空速达到39270 h^-1时,氢气的去除率仅为10.9%。鉴于这一问题,提出通过采用尾排空气的分流手段调整催化燃烧反应器对空速的要求,以提高尾气中氢气的去除率。     

中国碳排放强度的时空演进及跃迁机制

面对气候变化所带来的生存危机以及环境治理的复杂状况,对中国碳排放强度时空演进的动态监测与预警治理的研究是实现碳排放强度下降目标的关键。文章测算1997—2015年中国大陆30个省区碳排放强度的空间面板数据,采用探索性时空数据分析(ESTDA)方法对中国碳排放强度的空间相关性、集聚特征及其时空跃迁进行空间统计分析,借助分位数回归与时空跃迁嵌套模型,揭示在时间和空间推移的双重作用下中国各省区碳排放强度的时空跃迁机制。研究结果表明:(1)中国30个省区的碳排放强度在时空分布上并不是完全随机状态,各个省区碳排放强度之间具有显著的空间相关性特征,碳排放强度的变动趋势会受到其相临近省区碳排放强度的影响,省域间的碳排放强度在空间分布上呈现"集聚"与"分异"并存的时空演进特征。(2)中国碳排放强度空间集聚趋势增强,具有高度的凝固性和较低的流动性,10个高碳排放强度省区碳排放强度的稳定性将成为制约中国碳排放强度整体跃迁的重点省区,相关省区的跃迁性将成为驱动中国碳排放强度整体跃迁的关键省区。(3)各省区的碳排放强度空间集聚过程中存在时空跃迁的驱动模式和制约模式,分位数回归模型能够很好地解释各驱动因素对碳排放强度时空跃迁的驱动机制,不同响应阶段的驱动因素的分位数与碳排放强度时空跃迁类型之间具有很强的嵌套性。(4)根据各省区碳排放强度时空演进及其跃迁机制的分析结果,进一步提出加强对关键省区碳排放强度的有效监测与治理,加大碳排放的约束力度等差异化的碳减排调控措施。