Analysis and modeling of highway truck traffic volume variations during severe winter weather conditions in Canada

This paper investigates the impact of cold and snow on daily and hourly truck traffic volume on a primary highway in Alberta, Canada. This research is based on a detailed case study of 5 years of weigh-in-motion data recorded continuously at Leduc site on Highway 2A. Influence of the winter conditions on truck type distribution is examined by classifying trucks into single-unit trucks, single-trailer, and multi-trailer units. It is evident from the study that proportion of the three truck classes in the total truck traffic remained essentially stable over the study period （2005-2009）. Dummy-variable regression models are used to relate daily and hourly truck traffic volume with snowfall and categorized cold as independent variables. The statistical significance of all the independent variables used in the model is established by conducting tests such as R2, F test, incremental F test, and t test. The study results suggested that the truck volume is not significantly affected by the normal snowfall or the typical cold temperatures, i.e., average daily snowfall about less than 15 cm or temperatures higher than -25 ℃ are not likely to affect the truck traffic patterns. It is believed that the findings of this study can benefit highway agencies in developing programs and policies for efficient monitoring of truck traffic throughout the year and snow removal during the winter season in Canada.

Effect of Snow, Temperature and Their Interaction on Highway Truck Traffic

Based on statistical amount of traffic and weather data sets from three weigh-in-motion sites for the study period of from 2005 to 2009, permanent traffic counters and weather stations in Alberta, Canada, an investigation is carried out to study impacts of winter weather on volume of passenger car and truck traffic. Multiple regression models are developed to relate truck and passenger car traffic variations to winter weather conditions. Statistical validity of study results are confirmed by using statistical tests of significance. Considerable reductions in passenger car and truck volumes can be expected with decrease in cold temperatures. Such reductions are higher for passenger cars as compared to trucks. Due to cold and snow interactions, the reduction in car and truck traffic volume due to cold temperature could intensify with a rise in the amount of snowfall. For passenger cars, weekends experience higher traffic reductions as compared to weekdays. However, the impact of weather on truck traffic is generally similar for weekdays and weekends. Interestingly, an increase in truck traffic during severe weather conditions is noticed at one of the study sites. Such phenomenon is found statistically significant. None of the past studies in the literature have presented the possibility of traffic volume increases on highways during adverse weather conditions;which could happen due to shift of traffic from parallel roads with inadequate winter maintenance programs. It is believed that the findings of this study can benefit highway agencies in developing such programs and policies as efficient monitoring of passenger car and truck traffic, and plan for efficient winter roadway maintenance programs.

Microscopic and macroscopic diagnosis of the effect of winter weather conditions on distribution pattern of truck types in regional commuter road

It has been reported in the past literature that total traffic volume and passenger car volume are affected by snowfall and cold temperature, but trucks are not sig- nificantly affected. This paper aims to confirm that the distribution of truck types is not affected by winter weather conditions through combined statistical analysis in the framework of microscopic and macroscopic impact anal- ysis. A micro-level analysis was conducted to investigate the effect of snowfall and temperature on changes in truck type distribution. A macro-level analysis was also con- ducted to investigate the effect of the months （or seasons） on changes in truck type distribution. Truck traffic data were collected for 5 years at the weigh-in-motion site in Highway 2A, which is used for regional commuters near the City of Leduc, Alberta, Canada. The trucks were sub- divided into straight unit truck, single trailer and multi trailer unit and then analyzed by applying a nonparametric Chi-squared test combined with the binomial probability test. As a result of the nonparametric test, the truck type distribution was stable irrespective of the severity of winter weather conditions, which are microscopic factors such as snowfall and temperature, and was not influenced by changes in months and seasons, which are macroscopic factors.

基于车联网大数据的重型货车载重估计方法

针对当前货车载重计算方法普遍存在的成本高昂及泛化性能不明确的问题,提出一种创新的重型货车载重估计方法,方法融合了车辆行驶动力学理论与机器学习算法,通过有监督学习,利用高速通行大数据对模型进行训练与验证.首先采用聚类分析,确定车辆空、半、满载判断阈值,为后续的计算提供了重要依据.随后,利用随机森林算法训练分类模型,用以判断车辆在一段行驶过程中的基本载重情况.在此基础上,进一步在车辆行驶数据中筛选出稳定行驶的小片段,根据车辆系统动力学理论,对这些小片段车重进行计算.最后,根据载重状态的判断结果,对小片段车重结果进行筛选与计算,得到最终车辆载重计算结果.研究表明,在高速通行大数据的验证下,该方法对于空载及满载状态下趟次车重计算结果的整体平均绝对百分比误差(mean absolute percent error,MAPE)均可控制在10%以内,展现了较高的准确性.相比于现有技术,由于该方法无需安装额外传感器,对数据采集、存储、运算设备的要求也相对较低,因此在成本方面具有显著优势.在交通监管、物流运输、基于大数据的产品开发方面具有快速广泛推广的潜力.

Application of multinomial and ordinal logistic regressionto model injury severity of truck crashes, using violationand crash data

In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.

基于货车启动延误的交叉口信号优化模型

为研究货车停车启动行为对信号控制交叉口车辆延误的影响,根据货车的行驶特性给出了货车启动延误定义。基于概率统计原理及道路交通流特征,推导了欠饱和、过饱和两种状态下货车启动延误的计算方法。进一步以最小车均延误为目标,建立了基于货车启动延误的交叉口最小延误信号周期计算模型。通过实例仿真,对实测数据、理论数据和仿真数据进行对比分析。结果显示,与现有延误计算模型相比,该文延误计算模型理论值与仿真值的平均误差降低了40.9%。且设计配时方案与Webster最佳信号周期方案相比,车均延误降低了10.7%;与传统最小延误方案相比,车均延误降低了5.6%,有效验证了模型的适用性。

货车-两轮车碰撞事故典型场景提取及关联规则分析

基于视频信息提取了货车-两轮车碰撞典型事故场景,对事故场景中不同事故因素间的相关性进行了挖掘以补充代表事故遗漏的事故特征。应用210例带视频信息的货车-两轮车碰撞事故案例获取相关事故信息,采用K-modes聚类方法对事故案例进行聚类得到典型事故场景,采用关联规则方法对每个事故场景中事故因素之间的关联度进行分析。结果表明:货车-两轮车事故可分为4种典型场景,即2种十字路口事故,1种直行道路事故以及1种丁字路口事故。十字路口事故场景中事故特征与存在视觉障碍关联度较高,且在十字路口事故中“货车左转”所带来的风险也比较高;直行道路事故中货车制动规避与道路上是否有信号灯存在关联性;丁字路口事故中型货车转向规避与骑车人遭到碾压之间存在关联性。该研究结果可以为两轮车骑车人安全对策和安全测试场景提供参考。

高速公路客货混行交通流元胞传输模型

针对客货混行引发的交通流稳定性下降、交通安全风险增加以及运行效率降低等问题,构建一个多车道客货混行交通流元胞传输模型,从宏观角度研究不同货车渗透率下的高速公路客货混行交通流特性.首先,利用微观车辆跟驰模型,分析均质小汽车和货车交通流的基本图特征,提出不同货车渗透率下的客货混行交通流基本图模型.然后,采用具体参数表征换道、有限加速度造成的道路交通容量下降效应,刻画客货混行交通流中货车对直行和换道流量的影响,采用Logit模型计算多因素影响下的换道交通流量,构建多车道客货混行交通流元胞传输模型.最后,通过高速公路多场景仿真实验验证模型的有效性.研究结果表明:所提出的模型可准确模拟高速公路上的客货混行交通流演变过程;货车渗透率是影响混行交通运行的关键因素,在交通事件导致车道数减少时,货车渗透率超过0.6会导致交通拥堵难以消散.研究成果可为高速公路客货混行交通流特性研究提供分析工具,为客货混行下的交通管理和疏导策略制定提供理论方法.

城市出入口连接段处货车专用道研究

为缓解物流业迅速发展导致的大型物流园区与高速公路的国道连接路段客货拥堵问题,从城市出入口连接段的交通流特性出发,根据连接段交叉口货车与客车的转向数量及比例,建立货车专用道交织区长度计算模型,并且对不同车道数的交叉口进口道进行渠化,将专用道交织区长度计算模型和进口道渠化方案进行了仿真,验证了交织区长度计算模型与交叉口渠化方案的可行性。

大货车-行人及两轮车交通事故严重程度致因分析

为探究大货车-行人/两轮车交通事故严重程度影响因素,文章以成都市双流区322起该类交通事故为样本,将伤者是否死亡作为因变量,选择14个潜在影响因素作为自变量,构建二元logistic回归模型进行分析。结果表明:“车辆类型”“是否交叉口”“行驶状态”对事故严重程度影响显著。结合相关研究及成都实际情况得出这三类因素影响显著的原因,并针对性地提出事故预防措施。

长隧道路段货车限道措施的数值模拟——以甬莞高速角洞岙隧道路段为例

基于实际交通录像分析得出的边界条件和货车影响距离,提出了含有长隧道的双车道元胞自动机交通流模型.模拟结果与实际交通数据对比,吻合较好,表明模型能够准确反映实际交通流的特性.数值模拟显示,货车的存在增加了隧道路段交通拥堵的发生概率,且这种影响随货车比增加而增强.为降低货车对隧道交通的影响,提出了货车限道措施,并开展数值模拟,结果表明,货车限道措施能显著提升左车道的车速,在大进车率时也能提升右车道的车速,在高货车比时流量提升效果显著.

基于自动随行导向装置的物料车随行设计方法

自动随行导向装置在现代制造业中扮演着重要的角色,能够提高生产效率,减少人工成本,物料车的随行设计是实现自动化生产线高效运行的关键环节。基于此,文章计制作一条物料车自动上下随行线,以取代自动导向车牵引物料车前行,实现物料车与内饰滑板线的同步随行,完成以下流程:首先,自动导向车从物流分拣区出发,拖拉物料车至物料车上件位并停止,然后自动导向车的挂钩脱离,自行前进至物料车下件位等待。接着,利用自动导向装置,将物料车的随行挂钩与随行链条连接,使其与滑板线一同前进。员工可在随行的物料车上取料并进行装配。最后,空的物料车到达下件位时,会自动从链条上脱钩,并由等待的自动导向车牵引返回物流分拣区,如此循环,完成整个物料输送过程。

基于模型群的重型货车交通安全风险因素分析

为了深入探究重型货车交通事故的风险因素及发生机理,基于中国某省2016—2021年重型货车交通事故数据,构建集成随机森林、Logistic回归、地理加权Logistic回归和贝叶斯网络模型的模型群,对风险因素的影响程度、空间异质性及其因果路径进行分析。结果显示:①重型货车行驶状态、碰撞形态等10个因素对风险存在显著影响,其中农村交通参与者、正面和侧面碰撞在不同模型中的影响程度有轻微差异,追尾碰撞的影响程度在地理加权Logistic回归模型中较贝叶斯网络模型更高。②重型货车右转、存在违法行为、涉及弱势道路使用者时极容易发生亡人事故,分别使风险增加了41.9%,39.3%和39.0%。③以碰撞形态作为中介变量,重型货车行驶状态、事故另一方交通方式和年龄这3类因素与亡人事故风险的因果路径分析表明:当重型货车与弱势道路使用者发生侧面碰撞时,亡人事故风险比发生刮擦且事故另一方为其他类型机动车提高64.4%,为重型货车交通事故典型危险场景;对方年龄为30岁及以下时,追尾碰撞概率较30~60岁以及60岁以上分别增加10.3%和26.1%。④具有空间异质性的风险因素中,正面碰撞的空间异质性强度最大,右转的空间异质性强度最小。结论表明:基于模型群的分析框架可得到重型货车交通安全风险显著影响因素,可验证因素在不同模型中影响程度的差异性及空间异质性。

基于sEMG的渣土车驾驶员肌肉疲劳与恢复研究

目的 为了解渣土车驾驶员肌肉疲劳发展与疲劳恢复特征,避免疲劳累积,降低肌肉骨骼损伤风险.方法 设计并组织渣土车模拟驾驶实验.招募8 名被试参与实验,测量驾驶过程和恢复阶段驾驶员主观疲劳评分数据与颈部左右斜方肌、腰部左右竖脊肌、左右多裂肌共6 个肌群的肌肉活动信息,运用统计学分析方法分析肌肉疲劳随驾驶时间和恢复时间而变化的特征.结果 经过180 min模拟渣土车驾驶任务后,驾驶员达到非常疲劳状态,所测量6 个肌群肌肉疲劳,腰部CR-10 评分高于颈部;经过5 min静坐和30 min静坐且听音乐休息后,驾驶员恢复至较清醒状态,所测6 个肌群中,身体左侧相关肌群逐渐恢复,身体右侧相关肌群仍然处于疲劳状态.结论 渣土车驾驶过程肌肉疲劳明显,静坐恢复方式有利于疲劳恢复,但是身体右侧斜方肌、竖脊肌和多裂肌仍处于疲劳状态.

辅助驾驶系统对重型卡车驾驶员长途驾驶中疲劳程度的影响

乘用车领域的过往研究发现,相较于手动驾驶使用辅助驾驶系统的驾驶员会更容易出现疲劳,因此有必要探究辅助驾驶系统对重型卡车驾驶员长途驾驶疲劳程度的影响。基于超过120 h的自然驾驶实验,采用多项生心理指标(如心率、心率变异性等)对比了重型卡车驾驶员手动驾驶车辆时和在辅助驾驶系统帮助下驾驶车辆时的疲劳程度。辅助驾驶情况下驾驶员平均心率、呼吸频率、呼吸深度以及瞳孔直径均高于手动驾驶情况下,辅助驾驶情况下驾驶员连续二次心跳(RR)间隔均方根、低高频比例、眨眼频率、眨眼时长以及眼睑闭合百分比(PERCLOS)均低于手动驾驶情况下。当驾驶员处于手动驾驶情况时,驾驶时间每增加2 h,反应时间会增加0.032 s;当驾驶员处于辅助驾驶情况时,反应时间不随驾驶时间发生显著变化。重型卡车驾驶员在辅助驾驶系统帮助下驾驶车辆时的疲劳程度低于手动驾驶,本研究可以为辅助驾驶系统在重型卡车中的安全应用提供理论支持。

考虑多模态数据的重载货车危险驾驶行为识别方法

综合考虑货车操纵数据、驾驶员眼动数据和心电数据,本文提出一种多模态的重载货车危险驾驶指标构建和行为识别方法。首先,设计自然驾驶实验,利用车辆惯导、眼动仪及心理数据记录仪这3种设备采集车辆运行、眼动及心电等多模态驾驶数据,通过多设备时间同步及数据清洗,构建多模态驾驶数据集。其次,将重载货车危险驾驶行为分为危险驾驶操纵行为和疲劳驾驶行为两类,通过提取数据特征,构建9种危险驾驶行为指标,表征超速、速度不稳、急变速、急换道及疲劳驾驶这5种危险驾驶行为。针对危险驾驶操纵行为,组合文献调研、指标计算及四分位差法确定危险行为特征参数阈值;针对疲劳驾驶行为,通过因子分析和K均值聚类法划分疲劳驾驶类型。最后,构建重载货车危险驾驶行为数据集,采用随机森林分类模型识别危险驾驶行为,并与BP神经网络、K近邻及支持向量机等分类模型对比。结果表明,随机森林模型对于5种危险驾驶行为的分类准确率均在90%以上,整体优于其他分类算法,能够较高精度地识别驾驶中出现的危险驾驶行为。本文的多模态重载货车危险驾驶指标构建和分类方法能够用于危险驾驶行为识别,为驾驶员多模态危险驾驶行为预警提供思路和理论依据。

大货车交通事故特征分析与预防对策研究

从大货车交通事故特征入手,分析大货车交通事故的形态、空间及时间分布特征,进而从人、车、路、交通环境4个方面解析大货车交通安全的影响因素,从而提出相应的预防对策。研究结果显示大货车交通事故具有一定的规律性,可以从交通管理与教育、车辆设计、道路设施改善等方面采取针对性措施,遏制交通事故的发生。

大货车驾驶员交通心理与交通安全的研究

笔者从交通心理学的角度,对大货车驾驶员在行驶过程中的心理状况、因攻击性驾驶行为、强烈的冒险动机的驱使、对道路期望心理的失衡及交通安全感偏差等不良心理因素而导致恶性交通事故进行分析。通过研究得出以下结论大货车驾驶员是一特殊的群体,应重视对该群体的教育、培训与管理,从而提高驾驶员的职业道德水平、性格品质、交通安全感及驾驶技能,以改善道路安全环境和交通安全状况、降低道路交通事故发生率。

运输结构调整政策对区域路网重型货车流量的影响评估

针对区域路网重型货车流量受运输结构调整政策影响的问题,依托重型货车交通流大数据,以交通需求预测四阶段法为建模思路,引入神经网络模型和遗传算法等数据驱动方法,建立了“2+26”城市区域交通发生吸引量反推模型和交通流分配算法,形成了区域货运交通流时空分布模型.结果表明:运输结构调整政策全面实施后路网流量与基础年流量(2017年)相比,“2+26”城市区域路网重型货车年平均日交通量减少100辆、年平均日行驶量减少751万车·km.实际观测数据显示,“2+26”城市港口、大型工矿企业等关键节点周边道路重型货车流量普遍下降.“公转铁”政策对于区域大气污染防治具有良好潜力,后续应加快完善相关制度,协同推进运输结构调整与柴油货车污染治理工作.

露天矿自卸卡车车速对粉尘扩散的影响研究

为准确描述露天矿自卸卡车运输扬尘的动态三维流场,采用动网格技术及离散相模型的粒子跟踪技术的耦合计算方法,模拟不同车速下自卸卡车周围气流与粉尘的分布特征,并进行现场实测。分析结果表明,粘附在路面的积尘是露天矿运输扬尘的发尘源,气流紊动扩散产生的剪切气流和诱导气流是粉尘颗粒飞扬的动因;随着时间的推移,大部分未来得及沉降的粉尘颗粒在卡车后方飞扬,道路粉尘质量浓度为418~956 mg/m^3;随着自卸卡车车速的提高,近地面粉尘质量浓度与扬尘高度均增大。胜利东2号露天矿现场实测显示,运输扬尘污染情况严重,粉尘质量浓度高达1 932.2 mg/m^3,各测试端面的实测值和模拟值相对误差小于6.64%。