綠色節能最佳化運輸路徑之規劃

隨著經濟發展,自然環境惡化程度持續加深,肇因為小客車數量日益增加,於是近年來綠色交通概念被大力提倡,如能以最佳節能路徑為運輸進行改造,將能為綠色環保盡一份心力。以最短路徑演算法（Dijkastra＇s Algorithm）為基礎,撰寫出最佳節能路徑之演算法,即每台車皆能以最低油耗知目標行駛,並將其與最短距離和最少旅行之路徑進行結果比較,結果發現該最佳節能路徑相較另兩條目標路徑時最能節省燃油消耗,此成果可提供小客車車主進行最佳節能路徑之規劃。另外也以路網均衡之概念進行交通量指派,使用者均衡和系統均衡分別於時間成本和油耗成本目標下之比較。     

基于神经网络的冗余信息交通事故再现可靠性分析

针对实际道路交通事故中客观存在的不确定性因素,将可靠性理论和随机摄动法应用于道路交通事故再现分析,给出了相应的交通事故再现可靠性分析方法,提高了再现分析结果的精度.对于同一起事故,由于采用不同的信息和计算模型,得到的分析结果必然会存在不同程度的差异,从而产生冗余信息下的交通事故再现分析问题.针对交通事故中的冗余信息问题...     

基于广义线性模型的高速公路交通事故预测

为分析高速公路交通流状态参数对交通事故的影响,建立了基于广义线性模型的高速公路交通事故预测模型。采用美国8号州际高速公路的实时交通流数据和交通事故数据,建立了实时交通流状态下的高速公路泊松分布预测模型和负二项分布预测模型,并给出了模型变量弹性系数的计算方法,用以确定高速公路交通事故的突出诱导因素。研究结果表明:广义线性模型能够很好的拟合高速公路交通事故,并且负二项分布预测模型的预测精度高于泊松分布预测模型;交通量、占有率、大车比例和速度标准差是高速公路交通事故的显著影响参数,并且与之呈正向关系;交通量是诱导高速公路交通事故发生最突出因素,交通量增长1%,可导致交通事故增长5.8%。     

基于加权关联规则的交通事故分析

考虑到各种交通事故因素对交通事故属性本身的影响有轻重之分,基于传统的关联规则挖掘模型,提出了一种加权关联规则的模型,设计与实现了加权关联规则的挖掘算法,并应用到道路交通事故属性的分析中。     

高速公路交通事故预测研究

为判别高速公路交通流状态参数对交通事故的影响,提取了美国I-10号州际高速公路的实时交通流数据和交通事故数据,建立了实时交通流状态下的高速公路交通事故泊松分布预测模型,并对模型中显著变量进行了弹性分析。结果表明:泊松分布模型能够很好地拟合高速公路交通事故;平均交通量、平均占有率、大车比例和速度标准差4个参数显著影响高速公路交通事故,并且与之呈正相关关系;交通量是诱导高速公路交通事故发生最突出的因素,交通量增长1%,可导致交通事故增长7.96%。     

灰色聚类法在交通事故影响因素分析中的应用

以某高速公路交通事故影响因素分析为例,以万车事故率作为评价指标,按照灰色聚类评价的方法和过程,计算该路段道路交通事故影响因素的聚类评估值,得出各因素对道路交通安全的影响等级,为下一步采取管理措施提供理论依据.     

基于CA模型的交通事故叠加风险量化分析方法

考虑多种风险因素的共同作用,探讨了多风险因素叠加作用下的交通事故风险量化方法.在交通事故风险因素分类和基于事故统计数据的交通事故风险量化分析的基础上,将分心行为(手机通话)、违法行车(超载)、不利环境(雾霾天气)3种风险因素加入到基于N S模型规则的元胞自动机(CA)模型中,通过设定危险情境,量化了各风险因素组合下的交通事故风险,然后利用多因素方差分析方法计算了多风险因素下各风险因素组合的叠加作用所产生的交通事故风险.仿真结果表明,驾驶员手机通话时的事故风险是没有手机通话时的14倍,同时,当雾霾天气因素与手机通话因素同时作用时,出现了显著的叠加作用,产生的叠加风险约为雾霾天气单独作用所产生风险的2倍,表明了多风险因素下交通事故风险具有非线性叠加性.      

高速公路交通事故时空分布特征与影响因素分析

本文通过对国内某高速公路交通事故、非现场违法原始数据进行处理和分析,对高速公路交通事故的组成、原因和空间分布进行分析,探索事故与非现场执法设备建设之间的关系,实证分析交通流量、时间、天气等因素对高速公路交通事故以及交通违法的影响。通过分析,得出影响高速公路交通事故潜在的分布规律和较为显著的影响因素,可有效指导高速公路交通管理部门根据不同情况有针对性地提出科学有效的事故预防对策,对优化高速公路交通安全形势产生积极影响。     

基于 Logistic 回归模型的三线城市道路事故数据分析

根据乌鲁木齐市2006~2010年的交通事故统计资料,分别以城市道路中9类不同的交通事故形态为因变量,从道路设施、道路环境等方面选取了9个因素作为自变量,通过二项logistic模型进行事故形态分析,建立事故形态与9个影响因素间的线性相关模型,对模型参数进行了估计,并对模型的拟合程度、可靠性进行了分析,研究了所有自变量单独/组合等不同情况下对因变量的影响。再通过多项Logistic模型对不同道路条件下,各种形态的事故发生几率进行了预测,并与实际情况进行对比,检验了模型拟合效果。      

道路交通事故致因分析方法综述

为改善我国的道路交通安全状况,对交通事故致因分析方法进行了综述.首先介绍了当前全球以及中国的道路交通事故情况,分析了交通事故致因分析的意义.通过文献阅读,了解到交通事故致因分析方法的主要种类有：计数数据模型,离散选择模型,路径模型和结构方程模型,决策树,贝叶斯网法;并对各个方法的优缺点和适用性加以分析.最后,指出交通事故数据质量是决定交通事故致因分析结果的重要因素,并应详细分析交通事故的影响因素,针对不同的研究对象采取适当的分析方法加以研究.     

高速公路出入口危险行车事件监控与预警研究

危险行车事件的监控与预警是预防高速公路交通事故发生的有效措施。围绕着高速公路出入口区域事故预防问题,针对车辆违法变换车道、违法停车2种主要行车危险事件进行监控与预警。结合实例分析,以减少交通冲突风险和行车延误为评价指标,对高速公路出入口区域危险行车事件监控与预警系统进行效果评价。结果表明,此监控与预警系统能够明显地减少事件发生次数和危害程度,有利于事件的快速处理和执法管控。     

Exclusive motorcycle lanes: A systematic review

Road accidents are the main leading cause of death, and more than half of people killed in road accidents are motorcyclists, pedestrians, and bicyclists. In developing countries, the share of motorcycles is very high in the traffic stream, which results in an increased number of accidents. Nonetheless, a high share of motorcycles and risky riding behavior of motorcyclists raise notable threats to other road users as well as themselves. Segregating motorcycles from the main traffic stream by providing an Exclusive MotorCycle Lane (EMCL) is reported as one of the strategies to improve motorcyclist's safety and overall road safety. Although EMCLs are successfully running in some Asian countries like Malaysia, Taiwan, and Indonesia, and have reported reducing motorcycle accidents, existing literature related to such lanes is limited and lacks combined contemplation of various design factors. Therefore, a systematic review of studies related to EMCL was carried out in the present paper using the PRISMA approach. The review was majorly divided into the following three categories viz., geometric design elements of EMCL, motorcyclist's flow characteristics on EMCL, and traffic control measures for EMCL to highlight the research lacunas in this field. The present study aims to cast light on the need for specific geometric design standards for EMCL and to assess the safety impact of segregating motorcyclists from other mixed traffic. The review highlights that the design standards for EMCL should be based on the motorcycle's characteristics, as it varies from other vehicle categories in terms of riding behavior and the physical properties of the motorcycle.     

基于轨迹数据的危险驾驶行为识别方法

连续的跟驰行为和换道行为是驾驶行为的主要构成部分,对交通拥挤和交通事故有着重要影响。通过无人机视频拍摄和图像处理方式,提取了曹安公路沿线的2个交叉路口间正常交通流状态下共600条多车高精度轨迹数据。首先,考虑车辆类型对驾驶行为产生直接的影响,分析了大车和小车的车辆轨迹特征变量分布的差异性,包括速度、加速度、碰撞时间倒数、车头时距等,在标记危险驾驶行为的过程中考虑车辆类型的影响。其次,针对不同的车辆类型,利用修正碰撞裕度对跟驰行为和换道行为进行风险性评估,将其划分为安全型和风险型。根据风险型行为发生的顺序以及持续时间,评估驾驶人的整体驾驶状态是否危险,作为危险驾驶行为识别的样本标记。分别利用离散小波变换和统计方法提取车辆轨迹的关键特征参数,为了提高模型识别效率,将关键特征参数进行排序,从而确定最优判别指标;最后,利用轻量梯度提升机（LGBM）算法对危险驾驶行为进行识别,并与随机森林、多层感知器、支持向量机等算法在精度上进行比较。研究结果表明：在上述研究条件下,LGBM算法对危险驾驶行为的理论识别率最高可达93.62%,可以实现基于机器学习算法的危险驾驶行为的高精度自动识别,该结果对于智能驾驶辅助系统的设计、道路交通安全决策的制定具有显著的意义。     

Public transportation development and traffic accident prevention in Indonesia

Traffic accidents have long been known as an iceberg for comprehending the discrepancies of traffic management and entire transportation systems. Figures detailing traffic accidents in Indonesia, as is the case in many other countries, show significantly high numbers and severity levels; these types of totals are also evident in Jakarta, the highest-populated city in the country. While the common consensus recognizes that traffic accidents are the results of three different factor types, namely, human factors, vehicle factors, and external factors (including road conditions), human factors have the strongest influence—and figures on a worldwide scale corroborate that assertion. We, however, try to pinpoint the issues of non-human factors in light of increasing traffic accidents in Indonesia, where motorbike accidents account for the majority of incidents. We then consider three important pillars of action: the development of public transportation, improvement of the road ratio, and traffic management measures.     

基于驾驶模拟技术的道路行车安全性研究综述

对驾驶模拟技术在道路行车安全领域的研究及应用现状和存在的问题进行了分析。在广泛调研国内外相关文献的基础上,对驾驶模拟器进行了分类,并总结了国内外主要代表性科研型驾驶模拟器的发展历程,分析了典型驾驶模拟器的自由度、主要特征和应用领域。以“人-车-路-环境-事故”为主线,从不良驾驶行为特性分析、车辆主动安全技术研究、道路与交通设计、车辆驾驶环境以及道路行车事故研究5个方面,系统地梳理了驾驶模拟技术在国内外道路行车安全领域的应用研究现状、存在问题以及应用展望。在不良驾驶行为特性分析方面,重点研究了运用驾驶行为特性开展分心驾驶行为和疲劳驾驶行为的识别;在车辆主动安全技术研究方面,综述了运用驾驶行为开展车辆底盘一体化控制技术、安全辅助驾驶控制技术和自动驾驶接管行为的评价研究;在道路与交通设计方面,综述了道路几何和标志标线等的设计评价;在车辆驾驶环境方面,综述了不良气象、路侧景观和交通冲突等驾驶环境对驾驶行为的影响;在道路行车事故研究方面,总结了道路行车事故再现和事故影响因素分析等内容。此外,对驾驶模拟技术进行了应用展望,主要包括特殊人群的驾驶行为特性、智能网联汽车系统的测试及验证、混合交通流环境下的行车安全问题。对未来应对驾驶模拟器的有效性评价、不适性以及二次开发等问题进行探讨,以便更好地促进驾驶模拟技术的发展。      

基于驾驶行为和信息熵的道路交通安全风险预估

为了精准有效地进行交通事故预防预警，基于车辆OBD驾驶行为数据及信息熵理论，提出了城市道路交通安全风险预估方法。首先，分析异常驾驶行为高发位置与道路交通事故发生位置的关联性；其次，构建以道路交通安全熵为一级指标，急加速率、急减速率、急转弯率、超速率、高速空挡滑行率为二级指标的道路交通安全风险预估指标体系，提出了基于改进熵权法的道路交通安全熵计算方法；然后，基于密度聚类、K-means聚类提出了道路交通安全风险等级数确定方法，并基于K-means聚类建立了风险等级阈值计算方法。研究结果表明：异常驾驶行为高发位置与交通事故发生位置具有一致性；通过对log对数底数选择优化、二级指标零值处理、指标权重分段计算3个步骤改进的熵权法，可弥补log对数函数无法计算零值指标熵值的缺陷，避免指标权重为负、指标熵值与权重反映信息不一致的现象；两步聚类避免了孤立数据点对安全风险等级划分的影响。以重庆市4条城市道路（总长约38 km）进行实例验证后得出，道路交通安全熵与交通事故表征的道路交通安全状态趋势一致；道路交通安全风险等级可划分为高、低风险2级，道路交通安全熵优化阈值为0.042，最后，风险等级划分准确率为87.88%。研究成果可为道路交通安全风险点辨识、交通事故预防预警提供有效的技术支持。     

Modeling effects of driver safety attitudes on traffic violations in China using the theory of planned behavior

Traffic violations are recognized as one of the main causes of traffic accidents and have been found to be closely associated with driver attitudes toward traffic safety. In this study, a modified theory of planned behavior (TPB) was used to model the effects of driver safety attitudes on traffic violations, based on a questionnaire survey of 1505 drivers in China. In light of the strong correlations between the observed items, the items of the TPB components were grouped into several parcels, using an item-parceling method. Parcel-based structural equation modeling was then used to operationalize the modified TPB. The results indicate that the proposed model can accurately predict the occurrence of traffic violations based on the observed items related to driver traffic safety attitudes. It was found that driver attitudes, subjective norm, and perceived behavior control significantly affect traffic violations. For predicting traffic violations, driver attitudes toward traffic safety policies had the greatest influence, followed by driver attitudes toward risky driving behaviors and the attitudes of others toward risky driving behaviors. Finally, suggestions on traffic enforcement and education to reduce traffic violations are proposed based on the results.     

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots.     

公路客车交通事故分析及安全性技术

道路交通事故的分析表明,造成“群死群伤”交通事故的主要形态是客车碰撞、翻滚和跌落,这与驾驶人员、车辆行驶环境以及车辆的安全性能密切相关。研究公路客车的安全性技术,制定符合我国国情的安全性法规,是减少公路客车道路交通事故的主要手段,同时可以推动公路客车制造技术的进步和发展。     

公路平面交叉口驾驶行为研究

面对目前公路平面交叉口交通事故严重的现状,为了公路平面交叉口交通事故致因分析和加深对交叉口驾驶行为的了解程度,探讨了公路平面交叉口驾驶行为理论,总结了目前道路交通系统交通事故原因及其导致事故所占比例,分析了交叉口交通系统,给出了影响交叉口驾驶行为的具体因素。在驾驶行为三阶段基础上,研究了交叉口驾驶行为模型,分析了交叉口驾驶行为特征和交叉口驾驶行为与交通事故发生的一般关系。并针对驾驶失误和不安全驾驶行为给出了防止措施,以减少交叉口交通事故。