基于抽样车辆轨迹数据的信号控制交叉口排队长度分布估计

排队长度是评价信号控制交叉口运行状态的重要参数之一。现有大多数基于抽样车辆轨迹数据的排队长度估计方法可以实现周期级排队长度估计，但是需要信号配时、渗透率或车辆到达分布等实践中难以获取的输入信息。此外，这类方法在低渗透率条件下往往难以确保估计结果的准确性和可靠性，极大地限制了其实用性。因此，提出一种抽样车辆轨迹数据驱动的时段级信号控制交叉口排队长度分布估计方法，可不依赖任何交通流理论模型和前述输入信息实现排队估计。首先，通过理论推导可以证明时段内抽样车辆的停车位置分布和排队长度分布之间可互相转化；然后，提出一种扩展的核密度估计方法来拟合并平滑抽样车辆停车位置分布，从而有效地适应不同日期和周期的轨迹叠加所带来的波动，提高方法的适用性；最后，基于前述推导和拟合的停车位置分布实现时段排队长度分布、平均排队长度和百分位排队长度估计。分别采用仿真和实证数据对上述方法进行验证和评价。结果表明，通过叠加5 d相同时段的抽样轨迹数据，15 min的平均排队长度估计误差仅为1.59 veh，相对误差仅为9%。同时，面向不同分析时长，只要给定超过100 veh抽样车辆的观测样本，无论渗透率高低，所提出的方法在定时或自适应信号控制交叉口都可实现时段排队长度分布的准确估计，其成果可进一步用于信号控制交叉口运行可靠性评估以及多时段定时信号控制的鲁棒优化。     

基于动态清空距离的特殊车辆与CAVs混合车道控制

特殊车辆的优先通行是道路交通管理的一项重要工作,而目前相关控制措施存在实施难度较大、道路空间利用率低和道路通行能力下降等问题。为解决这些问题,结合智能网联汽车（CAVs）技术特点,提出考虑特殊车辆优先通行的CAVs专用车道控制方法,按应急车辆、一般优先级车辆和CAVs的优先通行顺序设计车辆通行规则。通过预测特殊车辆到达下游交叉口时的路口排队长度,建立“满足不同优先级特殊车辆通行需求”的动态清空距离模型,其中应急车辆以速度损失最小化为优化目标,一般优先级车辆以均衡车辆通行需求为优化目标。针对CAVs在专用道上可能成为其他车辆通行障碍的情况,考虑换道安全和不同换道动机,设计CAVs进入和离开专用道的规则,建立换道决策控制模型;在此基础上,提出适用于不同优先级车辆的专用车道通行控制策略。通过仿真实验对所提方法的控制效果予以分析验证。实验结果表明:与不考虑特殊车辆优先通行的控制方法相比,虽然该方法的车均出行时间和人均出行时间分别增加了3.9%和2.8%,但特殊车辆的车均延误时间减少了59.6%以上;与IBL控制方法相比,该方法的车均出行时间和人均出行时间分别减少16.7%和14.6%,特殊车辆的车均延误时间减少13.5%,专用车道利用率提高36.3%以上,并且在CAVs渗透率大于0.4时获得最佳控制效果。该控制方法在特殊车辆优先通行方面,减少了单一控制策略的局限性,为交通控制和管理提供理论支撑。      

基于网联车辆轨迹数据的周期排队长度估计

近年，基于网联车辆轨迹数据的交通管控与服务研究方兴未艾。其中，信号控制交叉口排队长度估计备受关注。然而，在低渗透率条件下，单个周期内轨迹稀少且提供的交通信息十分有限。现有研究仅以当前周期内网联车辆轨迹数据为输入，难以获得准确且可靠的周期级排队长度估计结果。因此，融合利用历史网联车辆轨迹数据提供的车辆到达和停车位置信息以及当前周期内实时观测的网联车辆排队信息，提出一种基于最大后验概率的周期最大排队长度估计方法。首先，依据历史轨迹数据的停车位置信息，估计排队长度的先验分布；其次，依据历史轨迹数据的车辆到达信息，估计周期内车辆的历史到达分布，并结合周期内最后1辆排队网联车辆的到达时刻与停车位置，构建排队长度似然函数；最后，基于贝叶斯理论，结合前述先验分布与似然函数，推导周期排队长度的后验分布，并采用最大后验概率方法实现周期最大排队长度的估计。仿真结果表明：所提方法在不同饱和度和渗透率条件下，均优于现有的方法；即使在车辆轨迹数不超过1 veh·周期^-1的低渗透率条件下，所提方法的平均绝对估计误差也不超过2 veh·周期^-1。实证结果表明：在渗透率仅为8.96%的条件下，所提方法的平均绝对误差为2.12 veh·周期^-1，平均相对估计误差为12.4%，同样优于现有同类方法。     

Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data

With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation; however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source detection data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue dynamics. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data were collected from numerical experiments conducted at two intersections in Shanghai, China, to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28%, respectively, for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper.     

人机混驾交通流交织区换道模型切换控制策略

因交织区的强制换道存在紧迫性, 车辆换道行为在交织区后半段会出现因换道意愿强烈而产生的激进换道行为, 这种微观的换道行为将给交通流带来一定影响; 在人机混驾情形下, 不同类型换道切换控制模型同样可能影响交织区通行能力。在分析人机混驾交通流交织区换道行为特性的基础上, 将换道类型分为保守型换道和激进型换道; 在可接受安全间隙模型的基础上结合自动驾驶车辆间的协同行为, 构建自动驾驶车辆在保守状态下的协同换道模型; 以及在激进型状态下考虑目标车道后车类型影响下, 构建激进型换道模型。通过分析津保立交桥实地调研轨迹数据和NGSIM中US-101交织路段轨迹数据, 分别拟合了保守型、激进型换道模型切换点分布函数; 考虑不同车辆驾驶行为特性及其相互作用, 提出人机混驾条件下换道模型切换控制逻辑决策。以SUMO仿真软件搭建实验平台, 考虑人工驾驶车辆换道模型切换点分布特性, 以优化最大流率、交织区整体车辆运行速度、换道车辆速度等为目标, 确定不同自动驾驶车辆渗透率下自动驾驶车辆的最佳保守型-激进型换道模型切换点。仿真结果显示: 在交织区长度为250 m, 自动驾驶渗透率分别为0.2, 0.5, 0.8时, 自动驾驶换道模型切换点分别在180, 80, 50 m处达到最佳, 即随着自动驾驶渗透率的提高, 换道切换点最佳位置将向交织区入口处逐渐移动, 且在自动驾驶渗透率较低时这种换道切换点的变化较为明显; 在较高渗透率下, 由于协同换道出现频率增高, 自动驾驶强制性换道行为比例降低, 换道模型切换点对交织区通行能力的影响逐渐变小。本项研究对人机混驾条件下高速公路交织区自动驾驶车辆的换道控制提供决策依据      

公交与右转混合型专用道仿真分析及效益评价

为解决设置公交专用道所带来的道路资源利用率低，相邻车道交通压力增大，专用道分时段开启致使社会车辆行驶混乱等问题，基于车种分离思想，提出一种公交车辆与右转车辆混合专用道的组织方式，允许公交车辆与右转社会车辆共用一条车道，以寻求保持公交优先与减少对社会车辆影响的平衡点。为论证该方案的可行性，首先，针对所研究的道路环境，提出了基于流量生成模型与配时优化模型的车道组仿真流程；随后，在考虑红灯时右转车辆行驶特性的前提下，建立了人均延误和车均延误的双指标评价矩阵模型；最后，分别在MATLAB和VISSIM仿真平台上，实现了对传统车道组、公交与右转混合型专用道车道组和公交专用道车道组3种方案的效益评价，并对其中的关键影响因素进行分析。仿真结果表明：所提出的公交与右转混合型专用道车道组的总体车均延误与人均延误在大多情况下处于较低水平，而公交专用道车道组和普通车道组也具有各自的优势区域；公交与右转混合型专用道的车道组织方式可以在保证社会车辆延误不明显增加的情况下，有效确保公交车辆的优先性，在一定条件下具有适用性，在工程实践中可作为公交专用道的过渡或替代方案。     

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).     

Development and evaluation of bus lanes with intermittent and dynamic priority in connected vehicle environment

Exclusive bus lanes provide a very high level of priority for transit operations, especially for Bus Rapid Transit and Express service, but these lanes could be underutilized and be a source of extra capacity if they could be shared in an intelligent way. This article explores the benefits of providing intermittent priority, called bus lane with intermittent and dynamic priority, of these exclusive bus lanes. Intermittent and dynamic priority can be implemented by allowing vehicles to use the lane when Bus Rapid Transit or Express bus is not present. Drivers can be alerted when a bus is in the lane using either infrastructure-based signs, or in the future using infrastructure-to-vehicle, or connected vehicle communications. Some critical operating parameters for implementing bus lane with intermittent and dynamic priority system including clear distance, degree of saturation (volume-to-capacity ratio), connected vehicle penetration, and bus departure/headway frequency have been investigated in this paper.     

自动驾驶专用车道影响下的CACC车流管理策略

自动驾驶专用车道对混合交通流的作用与协同自适应巡航控制(Cooperative Adaptive Cruise Control,CACC)车流大小相关.为分析已存在自动驾驶专用车道场景下CACC车流在各车道上的分布情况对交通流的影响,利用已有的人工驾驶车辆(Human-driving Vehicle,HV)和CACC跟...     

基于车头时距与行程时间的排队长度算法

基于电子警察过车数据,分析该周期内车道通行车辆的车头时距变化规律,提出该周期车道排队长度值的实时计算方法。针对多次排队、绿波协调情况,结合通行车辆的行程时间对结果进行修正,得到该周期车道最大车辆排队长度值。通过桐乡市庆丰路实时监控视频对比计算结果,证明本算法具有较高准确性。算法便于工程实践,可为信号配时优化提供数据支撑。     

Towards lane-level traffic monitoring in urban environment using precise probe vehicle data derived from three-dimensional map aided differential GNSS

Today's urban road transport systems experience increasing congestion that threatens the environment and transport efficiency. Global Navigation Satellite System (GNSS)-based vehicle probe technology has been proposed as an effective means for monitoring the traffic situation and can be used for future city development. More specifically, lane-level traffic analysis is expected to provide an effective solution for traffic control. However, GNSS positioning technologies suffer from multipath and Non-Line-Of-Sight (NLOS) propagations in urban environments. The multipath and NLOS propagations severely degrade the accuracy of probe vehicle data. Recently, a three-dimensional (3D) city map became available on the market. We propose to use the 3D building map and differential correction information to simulate the reflecting path of satellite signal transmission and improve the results of the commercial GNSS single-frequency receiver, technically named 3D map-aided Differential GNSS (3D-DGNSS). In this paper, the innovative 3D-DGNSS is employed for the acquisition of precise probe vehicle data. In addition, this paper also utilizes accelerometer-based lane change detection to improve the positioning accuracy of probe vehicle data. By benefitting from the proposed method, the lane-level position, vehicle speed, and stop state of vehicles were estimated. Finally, a series of experiments and evaluations were conducted on probe data collected in one of the most challenging urban cities, Tokyo. The experimental results show that the proposed method has a correct lane localization rate of 87% and achieves sub-meter accuracy with respect to the position and speed error means. The accurate positioning data provided by the 3D-DGNSS result in a correct detection rate of the stop state of vehicles of 92%.     

高速公路ETC交易耗时估计与车道分区设计

大多数高速公路ETC车道的长度都是以停车视距计算方法为设计依据的,但这忽视了各省市ETC交易耗时的差异,且此依据与ETC系统可实现不停车自动收费的目标相矛盾。不合理的车道长度会导致部分车辆因时间不足而无法完成交易,或出现跟车干扰等现象,降低收费站服务水平。以陕西省170多万条交易数据为样本,采用随机过程与数理统计方法,对交易耗时进行数字特征分析和参数估计,并提出ETC车道划分为交易区和安全控制区,将置信度为99.9%的交易耗时估计结果和停车制动距离应用于区域长度设计中。陕西全省ETC系统建设实践证明,该方法提高了ETC车辆通过率,保障了行车安全,取得了良好效果。     

大型综合客运枢纽送站坪的车辆延误（双语出版）

为了准确评估大型综合客运枢纽送站坪的服务水平,改善其交通秩序,提高管理水平,针对送站坪车辆的常规落客行为和违规行为造成的延误,进行了量化分析研究。在大量调研数据的基础上,提取车辆轨迹,通过虚拟线圈的方法获取车辆的运动参数和交通流信息,基于车辆运行特征和车流波动理论,提出了落客车辆汇入行车道时等待可穿插间隙的延误模型、行车道车辆受穿插车辆影响的延误模型以及违规行为造成的行车道车辆延误模型等,验证结果表明,延误模型计算结果与实测结果近似。针对客运枢纽常见的两车道送站坪的交通特性,将车辆在落客车道的行驶距离、落客时长、速度、加减速度等参数作为自变量,基于高峰期间车辆到达分布推导出了送站坪车辆的平均延误,在此基础上给出了送站坪车辆行程时间的理论推导模型和多元线性回归模型。实例验证结果显示：2个行程时间模型计算结果与实测数据基本吻合,平均误差均为13%,回归模型的拟合优度为0.868;减少模型变量,以车辆在落客车道的行驶距离和落客时长为自变量,拟合优度也达到了0.853,表明这2个变量对车辆在送站坪系统的总延误影响最大,它们的值可以基本反映出车辆在送站坪系统的总延误,研究结果可为仿真模型的构建及通行能力的研究提供理论基础。     

信号交叉口可变导向车道应用方法研究——以无锡市蠡湖大道-周新路路口为例

该文以无锡市蠡湖大道-周新路路口为例,介绍可变车道在信号交叉口的应用方法,包含可变车道检测器的布置、信号灯控制机理,以及交通安全配套设施的设置等,最后通过饱和度指标评价可变车道方案的实施效果。结果显示,实施可变车道能够合理分配道路资源、缓解路口通行压力、减小车辆排队长度,对于潮汐车流明显的城市,具有推广的意义。     

基于路段平均速度的坡道变速车道长度修正研究

通过对7条国道交通量数据的统计分析,选出我国高等级公路上行驶的有代表性的车型作为研究对象,以道路上车辆行驶的路段平均速度为基础,利用汽车行驶理论推导出坡道上变速车道长度修正系数的计算公式。讨论道路坡度对车辆加速性能的影响,进而推导出道路坡度与变速车道长度的关系,同时计算出不同道路坡道时的变速车道长度的修正系数,并与现行道路路线设计规范进行对比分析,给出坡道上变速车道长度的修正系数参考值,供公路设计人员设计山区或丘陵区高等级公路时参考。     

基于车轴图谱的城市道路汽车运行轨迹特性分析

为探究城市道路行车轨迹与路侧之间的横向距离对车辆运行的影响,提高驾驶员行车安全,在某市滨海路进行汽车运行轨迹样本采集试验,使用AxleLight RLU11系列路侧交通数据采集系统分车道采集试验路段汽车运行轨迹样本,利用SPSS Statistics对试验路段不同车道车辆运行轨迹样本进行数据处理,绘制不同行车道运行车辆横向距离的累积频率曲线,计算得到汽车运行轨迹与路侧的横向距离D85,通过绘制行驶车辆距路侧的横向距离直方图,得到不同车道的车辆分布规律。结果显示,驾驶员大多数偏向选择在内侧车道运行。根据试验路段内外2条车道车辆横向距离和运行轨迹特性,可为城市道路交通安全设施的设置提供理论依据,以期提高城市道路交通运行安全。     

拓宽的左转车道长度对延误影响的仿真分析

以提高拓宽进口道车辆的运行效率,减少进口道延误为目标,分析了交叉口进口道拓宽段车辆的运行特性,并运用Vissim仿真软件对不同长度的拓宽段进行仿真分析。交叉口进口道左转拓宽段的长度不仅与左转车量的排队长度有关,而且与其相邻车道的排队长度有关;根据仿真结果得出了拓宽左转车道长度与进口道延误之间的关系。     

Developing travel time estimation methods using sparse GPS data

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high-frequency data sources from specialist data providers, which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using “readily available” vehicle trajectory data from moving sensors such as buses, taxis, and logistical vehicles equipped with GPS in “near” real time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds, and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates, and time window lengths. Global positioning system (GPS) data from Interstate I-880 (California) for a total of 73 vehicles over 6 h were obtained from the University of California Berkeley's Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links, and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5-min time window length and for a GPS sampling frequency of 60 s.     

高速路网不停车收费车道优化布设方法

合理设置高速公路收费站ETC (Electronic Toll Collection)车道数量，对高速公路通行效率至关重要。针对目前路网中ETC与MTC (Manual Toll Collection)车辆混行的情况，考虑ETC的普及率，结合多用户路网均衡模型和排队论方法，建立基于双层规划模型的高速路网ETC车道优化布设方法。上层模型以车辆总通行时间最小为目标，优化设置进出收费站的ETC车道数量；下层模型为多用户路网均衡模型，反映ETC和MTC车辆的路径和收费车道选择行为。下层模型通过设计收费站的等价拓扑结构，表征收费站的车道使用规则及车辆的收费车道选择行为，并采用排队论方法估计ETC和MTC车道的收费排队时间。根据模型的特点设计了基于主动集的启发式算法，利用参数二进制与拉格朗日函数法确定迭代下降方向，解决了下降方向与步长难以计算的问题；通过内嵌优化函数的方式，保证在主动集转化过程中上层约束均不会失效，且避免了迭代过程中的模型解退化问题。基于上海市绕城高速进行实证分析，结果表明：随着ETC普及率的提升，收费排队时长按照负指数趋势下降；与按比例布设ETC车道的方法相比，所提方法最高可降低57.4%的收费排队时间，且该方法可以避免ETC车道布设过多对于MTC车道通行能力挤压造成的负面效果。研究成果可以有效指导高速路网ETC车道的布设，提高路网通行效率。     

大型营运客车在高速公路的换道安全性辨识

为了给大型营运客车换道预警系统设计提供参考,采用毫米波雷达、激光雷达、车道线识别传感器、GPS、视频监控系统以及控制器局域网（CAN）总线数据采集仪等设备,基于小型乘用车搭建浮动车采集平台。通过在试验线路上进行1.5×10⁴ km的驾驶试验,获取1 200余次营运客车的真实换道数据。以Jula提出的换道安全性模型为基础,结合营运客车的换道行为特征,通过分析换道进程结束后客车需要与周围车辆保持的安全距离,建立适合于营运客车的3类换道安全性识别模型（客车与自车道前方车辆、目标车道前方车辆、目标车道后方车辆）,并利用真实数据对3类模型进行验证。研究结果表明：客车换道持续时间均值为10.4 s,换道起始时刻与目标车道后方车辆的距离为10.0~40.0 m;所有换道样本中,73.3%的换道过程中客车速度要高于目标车道后方车辆,且超过90%的换道过程是由前方慢车引起;不同的速度区间下,车速和航向角联合变化情况下,驾驶人控制营运客车的横向偏移速度保持稳定,可认为客车驾驶人的心理预期换道进程存在固定经验模式,这与小型车换道的研究结论存在较大差异,传统的TTC预警算法识别率较低,在不同速度区间情况下,所提出的模型对客车与自车道前方车辆、目标车道前方车辆、目标车道后方车辆的换道安全识别评价准确率均超过了90%。