We can all get along: The alignment of driver and bicyclist roadway design preferences in the San Francisco Bay Area

Two trends in the United States—growth in bicycling and enthusiasm for complete streets—suggest a need to understand how various roadway users view roadway designs meant to accommodate multiple modes. While many studies have examined bicyclists’ roadway design preferences, there has been little investigation into the opinions of non-bicyclists who might bicycle in the future. Additionally, little research has explored the preferences of the motorists who share roads with cyclists—despite the fact that motorists compose the vast majority of roadway users in the United States and similarly developed countries.This paper presents results from an internet survey examining perceived comfort while driving and bicycling on various roadways among 265 non-bicycling drivers, bicycling drivers, and non-driving bicyclists in the San Francisco Bay Area. Analysis of variance tests revealed that both drivers and bicyclists are more comfortable on roadways with separated bicycling facilities than those with shared space. In particular, roadways with barrier-separated bicycle lanes were the most popular among all groups, regardless of bicycling frequency. Striped bicycle lanes, a common treatment in the United States, received mixed reviews: a majority of the sample believed that they benefit cyclists and drivers through predictability and legitimacy on the roadway, but the lanes were rated significantly less comfortable than barrier-separated treatments—particularly among potential bicyclists.These findings corroborate research on bicyclists’ preferences for roadway design and contribute a new understanding of motorists’ preferences. They also support the U.S. Federal Highway Administration’s efforts to encourage greater accommodation of bicyclists on urban streets.     

Influence of priority taking and abstaining at single-lane roundabouts using cellular automata

Existing roundabout simulation models fail to consider all types of driver behavior which compromises their accuracy and ability to accurately evaluate roundabout performance. Further, these non-compliant driver behaviors, including priority taking and priority abstaining, are inconsistent with existing traffic flow theories. In this paper, a new cellular automata model, C.A.Rsim, is developed and calibrated with field data from five single-lane roundabouts in four northeastern states. Model results indicate that approximately 20% of the individuals in the driver population are inclined to priority taking and approximately 20% are inclined to priority abstaining behavior, though the observed levels of these types of behavior are naturally lower and vary with traffic volume. The model results also corroborate other research indicating that current models can overestimate capacity at higher circulating volumes, possibly a result of the jamming effect produced by priority taking behavior. The reduction in priority abstaining behavior, which is observed at older roundabouts, significantly reduces delay and queue length in certain traffic volumes. C.A.Rsim is also more parsimonious than many existing microsimulation models. These results provide insight on how variations in conflicting flow (i.e., traffic volume and turning movement balance) impact the amount of observed non-compliant behavior.     

先进驾驶辅助系统的接受程度及其影响因素分析

研究驾驶人对驾驶辅助系统(ADAS)的接受程度及其影响因素,有利于ADAS的推广和功能改善.招募46名被试,分2次驾驶未安装和安装有ADAS的车辆,行驶于武汉市典型道路各105km,并完成驾驶人基本信息和关于ADAS接受程度问卷.基于技术接受模型(TAM)分析驾驶人对ADAS的接受程度,应用方差分析方法研究驾驶人对ADAS接受程度的影响因素.结果表明,43名驾驶人对ADAS的接受程度均值为80.9%(SD=0.191);驾驶人的性别、年龄和驾驶经验对ADAS的接受程度没有显著性影响;ADAS类别对驾驶人的接受程度有显著性影响,驾驶人对于FCW系统的接受程度较高,而对LDW系统接受程度较低;道路类型对驾驶人的接受程度也有显著性影响,驾驶人在城市道路上对ADAS的接受程度最低.     

驾驶员手伸及界面与驾驶室尺寸综合因子G

驾驶员手伸及界面与驾驶员的手伸、能力及驾驶室内部尺寸有关。驾驶员手伸及能力的统计性可通过身材百分位和男女比来反映。而驾驶室内部尺寸对手伸及界面的影响应以多元统计分析理论加以分析。本文介绍如何利用因子分析法来求取对手伸及界面具有综合影响的驾驶室尺寸综合因子G。     

动车组人机控制界面背景色对识别效率的影响

分别以红、橙、黄、绿、青、蓝、紫、灰、白和黑10种颜色作为人机控制界面的背景色,测试动车组司机的视觉识别效率.首先采用对称截尾法估计测试者对颜色组合的识别效率,然后采用Wilcoxon样本秩和检验法比较不同背景色时颜色组合识别效率的显著差异性,最后采用U统计量法为存在识别效率显著差异的背景色进行排序.计算结果表明:用黑色作背景色,颜色组合识别效率有显著差异,且优于其他9种颜色.因此动车组控制系统人机控制界面的背景色设置成黑色能提升司机的视觉识别效率.     

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.     

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.     

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.     

From behavioral psychology to acceleration modeling: Calibration,validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

We investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices. The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (i.e. gain in travel times) and subjective dis-utilities for decelerations (i.e. loss in travel time) with a perceived probability of being involved in rear-end collision crashes. Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory – prospect theory – into an efficient microscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment.After model formulation, we explore different model disaggregate and aggregate characteristics making sure that fidelity is kept in terms of equilibrium properties. Significant effort is then dedicated to calibrating and validating the model using microscopic trajectory data. A modified genetic algorithm is adopted for this purpose while focusing on capturing inter-driver heterogeneity for each of the parameters. Using the calibration exercise as a starting point, simulation sensitivity analysis is performed to reproduce different fundamental diagram regions and to explore rear-end collisions related properties. In terms of fundamental diagram regions, the model in hand is able to capture traffic breakdowns and different instabilities in the congested region represented by flow-density data points scattering. In terms of incident related measures, the effect of heterogeneity in both psychological factors and execution/perception errors on the accidents number and their distribution is studied. Through sensitivity analysis, correlations between the crash-penalty, the negative coefficient associated with losses in speed, the positive coefficient associated with gains in speed, the driver’s uncertainty, the anticipation time and the reaction time are retrieved. The formulated model offers a better understanding of driving behavior, particularly under extreme/incident conditions.