无信号交叉口人车冲突严重程度影响因素分析

为提高无信号交叉口通行效率并改善行人过街安全性,利用K-means++算法聚类冲突指标以判定人车冲突严重程度,通过多重共线性检验和皮尔逊相关性分析剔除严重共线性和影响不显著的因素,并进行影响因素强弱程度排序,采用多元有序Logistic回归算法,对比分析各显著独立因素对人车冲突严重程度的具体影响。结果表明：以碰撞时间、间隙时间、安全减速度为冲突指标,可将人车冲突严重程度分为严重、一般和轻微冲突;行人特征中,性别、分心行为、等候时间、平均穿越速度以及是否有人陪同对冲突严重程度的影响强弱程度依次减小;车辆特征中,车头时距、行驶意图和车辆类型对冲突严重程度有显著影响。     

基于Ordered Probit模型的人车冲突安全影响因素研究

为探究和定量分析人车冲突严重程度的影响因素,通过采集3个信号交叉口的人车冲突视频数据,利用T-Analyst标定人车轨迹并计算冲突指标(PET),采用85%位累计频率曲线法划分人车冲突等级;从人、车、路及环境特征中选取10个因素作为变量,构建Ordered Probit模型,以确立人车冲突严重程度的显著影响因素,并通过边际效应定量分析不同显著因素的影响程度。研究结果表明：行人闯红灯情况、年龄、行人交通量、人行道占用情况、人行道起点终点、车辆速度变化及车流量是人车冲突严重程度的显著因素,相较于各自参考量,行人闯红灯、车辆加速通过冲突点、人行道起点及老年人造成严重冲突的概率分别增加8.2%,5.9%,5.1%,4.4%;相较于低流量的交通流,较高流量的车流和行人交通流使得严重冲突的概率分别增加3.7%,2.5%,但人行道占用使得严重冲突的概率下降6.8%。研究结果可为信号交叉口行人过街交通安全设施的设计和实施提供理论依据。     

基于时空指标的人车冲突等级评价及影响分析

无信号控制路段人车冲突评估对于实施交通安全策略具有重要意义。采用交通调查和数据分析的方法，通过Tracker软件处理试验视频获取人、车、路3方面数据和15个解释变量信息，并将考虑时空的复合T-DI(Time-Distance Evaluation Indicator)作为人车冲突评价指标。随后，利用一维K-means聚类算法确定边界值，以对人车冲突等级进行划分。建立基于Odered Probit回归分析的人车冲突量化模型，利用Stata软件标定模型参数。结果显示，基于时空指标的人车冲突中，行人“结伴行为”“使用电子设备”“一阶段过街”对冲突等级影响显著，且呈正相关。其中，女性比男性冲突更严重。“过街等待时长”“行人过街速度”与冲突等级呈负相关，“大客(货)车”对冲突等级影响显著(0.407 2),且均在95%的置信区间中显著。     

信号控制交叉口行人过街等待时间研究

为保护行人过街时的安全及减少行人违章行为,通过分析行人过街的等待规律,构建了信号控制行人过街等待的生存分析方法。对上海市典型交叉口的行人过街等待行为的观测及分析,得到了行人等待的生存曲线,建立了行人违章危险度随行人自身及环境因素影响的COX风险回归模型。结果表明:行人在路边等待过街最大可忍受等待时间为90s,在中间安全岛的最大可忍受等待时间仅为50s;行人等待时间与行人年龄、红灯时间、穿越车道数、冲突方向车流量、是否二次过街相关,与行人性别无关;合理的交叉口设计和信号控制能提高行人的遵章率、降低违章危险度。笔者建议:信号控制交叉口行人红灯时间不超过90s,行人在安全岛最大等待时间不超过50s。     

无信号控制路段行人过街决策研究

为更深入研究无信号控制路段行人过街的交通特性,通过激光雷达采集行人过街过程中来车的运动状态数据,考虑外部因素对行人过街决策的影响及行人对安全过街的心理需求,利用人车间距、车辆速度和可穿越间隙等参数分析直接过街和等待过街2种决策结果下的数据特性。基于上述参数建立行人过街安全心理距离模型和多元Logistic回归决策模型,并验证模型有效性。结果表明:当人车间距越大、车速越低、可穿越间隙越大时,行人过街概率越大;相同可穿越间隙下,车速越快,行人过街概率越大;所建立二模型均有较高有效性。研究成果可为车载行人预警系统的优化提供依据。     

多车道路段无灯控人行横道的分车道让行模式研究

为研究多车道路段无灯控人行横道的行人过街难与事故多发的问题,调查了泸州市5处人行横道的行人过街及车辆让行情况,分析了行人不同过街方式的交通延误情况。研究结果表明:传统"车让人"模式存在非必要的车辆延误,易促使车辆放弃让行,使行人被迫等待车流间隙过街,存在较大的过街阻力和安全隐患。为此,运用交通冲突技术讨论了单位车道与人行横道交织的人车协同让行关系,提出一种基于行人优先与效率原则的分车道让行模式,通过设置让行识别区和行人驻足引导人车双方按照到达时序进行相互避让。VISSIM仿真显示,该让行模式可在不过分增加行人延误的同时显著降低车辆延误。     

基于累积Logistic模型的行人交通事故严重程度分析及对策研究

为了准确分析影响行人交通事故严重程度的各项因素,引入累积Logistic模型研究人、车、路、环境及事故特征与行人事故严重程度之间的关系.以美国北卡罗来纳州夏洛特市2016年的行人交通事故数据为样本,通过聚类压缩、共线性检验和预测力分析,从人、车、路、环境及事故特征5个方面选取16个因素作为自变量,事故严重程度作为因变量,构建行人交通事故严重程度预测模型来分析仅财产损失事故、受伤事故和死亡事故的影响因素,并引入弹性分析量化各显著因素的影响程度,检验结果显示模型拟合良好.结果表明,在0.05的显著性水平下,事故形式、事故发生时段、车辆类型、照明条件、行人年龄段、行人饮酒情况6个因素与事故严重程度显著相关.最后针对性地提出了行人事故预防对策.研究结果有助于交通管理者采取合理的策略来降低行人交通事故严重程度.     

行人违章过街行为心理成因分析及预测

为深入了解行人在交叉口违章过街行为,调查影响行人在长周期信号控制交叉口违章过街的心理成因,采用计划行为理论(TPB)制作了行人违章过街心理行为问卷。利用此问卷,对上海居民进行过街违章心理研究,得到250个覆盖各年龄层的有效样本。信度效度分析验证了行人违章过街心理行为问卷能够用来解析行人的违章过街心理成因。在对问卷中自述违章过街行为和心理要素的聚类分析筛除明显隐瞒自身违章过街行为的行人后,对心理要素和现实行为违章行为进行判别分析。结果表明:问卷中2个TPB的心理要素:知觉行为控制和行为意向能够用来推测行人的实际违章行为,准确率为73.9%。其中,代表行人对交叉口交通情况和自身时间约束的心理要素知觉行为控制对行人违章行为有关键的影响。     

无信号控制路段行人过街风险分析模型

为加强无信号控制路段的行人过街交通安全管理,分析行人过街过程,定量描述过街风险状态。探讨行人过街理想等待时间的概率分布,提出行人过街方式、步行速度、启动时间、对向干扰等过街风险的核心要素,建立行人过街时间计算方法。探讨车头时距的分布规律,并在此基础上确定行人可接受过街间隙的概率分布,构建行人过街安全度的计算模型。实例应用表明,路段的车流量大、车道多、无中途停驻设施等情况,会导致行人可获得的安全过街间隙小,实际等待时间较长,过街安全度值低。     

城乡结合部无信号交叉口行人过街时间研究

为提高城乡结合部无信号交叉口行人过街的安全性,在实测数据的基础上,利用生存理论模型建立这种特殊点段行人过街时间的生存公式,通过寿命表法分析行人过街时间的特征,并利用Cox比例风险模型对影响行人过街时间的因素进行回归分析。研究结果表明,一般情况下,城乡结合部无信号交叉口行人过街时间服从正态分布,最大过街时间大致为50 s,小于城区内无信号和有信号交叉口行人过街时间;过街人流量、冲突车辆数和道路宽度是直接影响行人过街时间的重要因素。     

二次过街路口行人违反交通法规规律的研究

为了解在不同类型二次过街路口行人违反交通法规闯红灯的违法率,找出二次过街路口不同参数对行人违法率的影响,从而找出降低路口行人违法率的对策,保障路口行人交通安全和人车流畅通,本文选取上海市5种类型的二次过街路口调查行人在路口的违法率.通过分析调查数据,行人在路口一次穿越车道数为2～7,两次穿越车道总数为6～12,违法率随车道数的增加而降低.在红灯等待时间90 s之内,行人违法率随等待时间的升高呈一定的下降趋势.红灯等待时间在90～150 s之间,行人违法率没有确定的规律.安全岛、引导护栏、中继信号灯等设施具有明显降低行人违法率的作用.根据二次过街路口设置安全岛、引导护栏、中继信号灯、车道数以及是否从路边穿越5个影响因素的安全评价系数之和建立综合安全评价系数,得到行人在二次过街路口违法率随综合安全评价系数的增大而降低.研究表明,完善和优化二次过街路口的设施参数可以降低路口行人违法率.     

An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor

Problem: Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle–pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events. Method: This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification. Results: The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation.     

行人违规过街行为的激励理论模型

为减少行人违规过街现象,提高行人过街的安全性,从社会心理学角度研究行人违规过街行为的动因。基于内容型激励理论和过程型激励理论对行人违规过街的行为机理进行研究。首先,将行人过街需求按照不同的层次和类别划分为安全需要、省时省力需要、社会认同需要、尊重需要和自我实现需要。然后,基于期望理论,对行人违规过街行为激励水平、效价、期望值进行建模,得出行人违规过街的行为机理。最后,对模型结果的应用分析发现,通过降低违规过街需要的效价、增加交通规则制裁的期望和增加危险感知的期望能够减少行人的违规过街行为。     

疲劳驾驶交通事故的严重程度影响因素分析

为探究和定量分析疲劳驾驶交通事故严重程度的影响因素,以广东省1 370条疲劳驾驶事故数据为基础,对比分析不同年份、时间段以及年龄段的疲劳驾驶交通事故特征;以交通事故严重程度为因变量,将其分为严重事故和非严重事故,从驾驶员年龄、驾龄、车辆类型等17个初步选择的自变量中筛选对疲劳驾驶交通事故严重程度具有显著影响的因素;采用二元Logistic回归模型分别对全体数据和不同道路类型下的数据建立疲劳驾驶交通事故严重程度预测模型,并对模型进行参数估计和检验。研究结果表明：模型拟合度良好,准确性高;对疲劳驾驶交通事故严重程度具有显著影响的因素有年龄、人员类型、车辆类型、道路类型、道路线形和能见度;车辆类型和道路线形是影响城市道路交通事故严重程度的重要因素,能见度是影响1,2级及其他更低级道路交通事故严重程度的重要因素。     

Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach

Introduction: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. Method: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. Results: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.     

倒计时信号灯绿灯间隔时间设置研究

就保障倒计时信号交叉口交通安全而言,适当设置倒计时信号灯绿灯间隔时间具有重要意义。选择南京一个道路交叉口,利用交通冲突理论,分析产生严重交通冲突的条件,考虑车流不饱和与饱和2种情况,得到在不发生严重交通冲突条件下的绿灯间隔时间,并通过实例说明。研究结果显示,在车流饱和状态下,头车与尾车均为大车时,头车以初速度零开始加速进入交叉口时绿灯间隔时间应最短;在不饱和状态下,头车与尾车均为大车时,头车以一定速度通过交叉口时绿灯间隔时间应最长;车流饱和时绿灯间隔时间应较短,车流不饱和时绿灯设置间隔时间应较长。     

Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis

Introduction: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. Methods: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. Results: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. Conclusions: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle–pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control.     

Modeling pedestrian gap crossing index under mixed traffic condition

IntroductionThere are a variety of challenges faced by pedestrians when they walk along and attempt to cross a road, as the most recorded accidents occur during this time. Pedestrians of all types, including both sexes with numerous aging groups, are always subjected to risk and are characterized as the most exposed road users. The increased demand for better traffic management strategies to reduce the risks at intersections, improve quality traffic management, traffic volume, and longer cycle time has further increased concerns over the past decade.MethodThis paper aims to develop a sustainable pedestrian gap crossing index model based on traffic flow density. It focusses on the gaps accepted by pedestrians and their decision for street crossing, where (Log-Gap) logarithm of accepted gaps was used to optimize the result of a model for gap crossing behavior. Through a review of extant literature, 15 influential variables were extracted for further empirical analysis. Subsequently, data from the observation at an uncontrolled mid-block in Jalan Ampang in Kuala Lumpur, Malaysia was gathered and Multiple Linear Regression (MLR) and Binary Logit Model (BLM) techniques were employed to analyze the results.Results and conclusionsFrom the results, different pedestrian behavioral characteristics were considered for a minimum gap size model, out of which only a few (four) variables could explain the pedestrian road crossing behavior while the remaining variables have an insignificant effect. Among the different variables, age, rolling gap, vehicle type, and crossing were the most influential variables. The study concludes that pedestrians’ decision to cross the street depends on the pedestrian age, rolling gap, vehicle type, and size of traffic gap before crossing.Practical applicationsThe inferences from these models will be useful to increase pedestrian safety and performance evaluation of uncontrolled midblock road crossings in developing countries.     

基于Logistic的不同车型碰撞行人事故严重程度致因分析

为定量分析不同车型碰撞行人事故严重程度影响因素,以美国北卡罗来纳州2007-2016年人车碰撞事故数据为样本,将其分为小轿车、SUV、货车碰撞行人事故3类,以事故严重程度为因变量,交通参与者属性、道路、环境条件和事故特征为候选自变量,分别建立累计logistic模型进行对比分析,探究人、车、路和环境因素对人车碰撞事故严...