Do driver training programs reduce crashes and traffic violations? — A critical examination of the literature

This paper reviews the evaluation literature on the effectiveness of classroom and behind-the-wheel driver training. The primary focus is on North America programs as originally taught in high schools but now also by private instructors. Studies from the United Kingdom, Australia, New Zealand and Scandinavia are also included.By far the most rigorous study to date was the experimental study in DeKalb, Georgia, U.S.A. This study used a randomized design including a control group and a very large sample size to provide reasonable statistical precision. I reexamine the DeKalb data in detail and conclude that the study did show evidence of small short-term crash and violation reductions per licensed driver. However, when the accelerated licensure caused by the training is allowed to influence the crash and violation counts, there is evidence of a net increase in crashes.The other studies reviewed present a mixed picture but the better designed quasi-experimental evaluations usually showed no effects on crash rates but almost all suffer from inadequate sample size. I show that as many as 35,000 drivers would be required in a two group design to reliably detect a 10% reduction in crash rates.The advent of GDL laws in North America and other countries has largely remedied the concern over accelerated licensure of high risk teenage drivers by delaying the progress to full licensure. Conventional driver training programs in the U.S. (30 h classroom and 6 h on-the-road) probably reduce per licensed driver crash rates by as little as 5% over the first 6-12 months of driving. The possibility of an effect closer to 0 cannot be dismissed.Some GDLs contain an incentive for applicants to complete an advanced driver training program in return for shortening the provisional period of the GDL. The results of Canadian studies indicate that any effects of the driver training component are not sufficient to offset the increase in accidents due to increased exposure.There is no evidence or reason to believe that merely lengthening the number of hours on the road will increase effectiveness. Programs directed toward attitude change and risk taking better address the underlying cause of the elevated crash risk of young drivers but these behaviors are notoriously resistant to modification in young people.     

汽车仪表板设计及制造

概述汽车仪表板的分类、使用要求及其总布置设计和造型设计;简单介绍其制造常用的工艺及材料。     

驾驶员不良情绪状态监测及舒缓系统开发

本系统集监测与舒缓功能于一身实现驾驶员不良情绪状态实时、非接触式地监测及舒缓.该系统首先使用肤色模型算法迅速定位人脸,然后利用累积差分帧和 Hough 变换等实时图像处理技术进行检测、跟踪眼睛和嘴巴的状态并提取相对特征参数,进而与表情模板匹配,以相似度衡量相似程度并最终判定驾驶员的实时情绪状态.如果判定的结果为不良情绪状态,系统将自动播放与之相对应的舒缓曲目,最终达到舒缓驾驶员情绪、保证安全行车的目的.     

用于列车网络的司机显示单元的系统设计与实现

地铁列车司机显示单元(DDU:Dirver Display Unit)作为列车网络的重要部分,是完成人机对话和系统信息显示的重要系统.为驾驶员的操作及地铁车辆安全运行提供信息保证.本设计选择X86架构计算机作为DDU单元的硬件平台,嵌入Linux操作系统,构建了基于CANopen通信协议的地铁列车司机显示单元.本文详细介绍了DDU的系统原理和软件架构设计.     

An interdisciplinary study to explore impacts from policies for the introduction of low carbon vehicles

Driven by concerns of climate change, governments across the world are introducing a number of policies to accelerate the uptake of low carbon vehicles (LCVs), with a specific focus on electric motors. However, there is uncertainty in the effectiveness of such policies and technology pathways, which are inherently interlinked. This article considers the short-term situation to 2020 and focuses on the concern that these policies may bring about some disproportionate impacts in society due to changes in mobility. An ethical framework is established that seeks to balance obligations to reduce greenhouse gas (GHG) emissions and rights to car ownership, then selected policies are modelled within this framework to assess acceptability of implementation. Although these policies are successful in introducing LCVs and reducing GHG emissions, findings also indicate uneven cost burdens and reduced affordability of car ownership. Following this, recommendations for policy amendments and model improvements are made.     

A hybrid Bayesian Network approach to detect driver cognitive distraction

Driver cognitive distraction (e.g., hand-free cell phone conversation) can lead to unapparent, but detrimental, impairment to driving safety. Detecting cognitive distraction represents an important function for driver distraction mitigation systems. We developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering—to detect cognitive distraction using eye movement and driving performance measures. In this study, the algorithm was trained and tested with the data collected in a simulator-based study, where drivers drove either with or without an auditory secondary task. We calculated 19 distraction indicators and defined cognitive distraction using the experimental condition (i.e., “distraction” as in the drives with the secondary task, and “no distraction” as in the drives without the secondary task). We compared the layered algorithm with previously developed DBN and Support Vector Machine (SVM) algorithms. The results showed that the layered algorithm achieved comparable prediction performance as the two alternatives. Nonetheless, the layered algorithm shortened training and prediction time compared to the original DBN because supervised clustering improved computational efficiency by reducing the number of inputs for DBNs. Moreover, the supervised clustering of the layered algorithm revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that the layered algorithm can capitalize on the best attributes of component data mining methods and can identify human cognitive state efficiently. The study also shows the value in considering the supervised clustering method as an approach to feature reduction in data mining applications.     

疲劳驾驶检测方法的研究进展

全世界每年因驾驶员疲劳驾驶而导致的死亡人数占交通灾难性事故的57%,故针对疲劳驾驶检测方法的研究具有现实意义.阐述了目前国内外各类疲劳驾驶检测方法的研究难点,介绍并对比分析了基于驾驶员生理参数、驾驶员行为特征、车辆行为特征的各类客观检测方法;最后对疲劳驾驶检测方法的发展趋势和应用前景进行了论述.     

托运人在不同时空下的选择行为差异揭示

某一因素对托运人选择行为的影响会随着空间和时间变化. 既有研究多关注特定空间或时间的托运人选择行为,较少进行横向(空间)和纵向(时间)比较. 为探索托运人选择行为差异及变化规律,采集不同时空的5 个数据集,构建多类线性或非线性效用函数的多项 Logit 模型及混合Logit 模型. 结果表明：在空间维度,某一地区托运人选择行为与其他地区显著不同;在时间维度,伴随货运业快速发展,货运方式选择模型适用年限呈缩短趋势.     

Variability of the Bohai Sea circulation based on model calculations

The circulation and the hydrography of the Bohai Sea are simulated with the Hamburg Shelf Ocean Model (HAMSOM). The model is three-dimensional, prognostic baroclinic and has a resolution of 5 min in latitude and longitude and 10 layers in the vertical. It is initialised and forced with the five main tidal constituents, temperature and salinity distributions taken from the Levitus database, monthly mean river run-off values and European Centre for Medium Range Weather Forecast (ECMWF) re-analysed data of air pressure, wind stress and of those parameters relevant for the calculation of heat fluxes. The simulation period covers 14 years from 1980 to 1993 due to the availability of the time-dependent ECMWF forcing.The results are analysed by means of time series and EOFs focussing on the interpretation of fluctuations with periods above the tidal cycle. Furthermore, tracer simulations are carried out and turnover times are calculated in order to evaluate the importance of these fluctuations on the renewal and transport of water masses in the Bohai Sea.One of the major outcomes of the investigation is the overall dominance of the annual cycle in all hydrographic parameters and the importance of stochastic weather fluctuations on the transport of water masses in the Bohai Sea.