基于多源数据的城市道路网络行程时间预测模型

针对基于单一数据源、利用卡尔曼滤波理论建立行程时间预测模型存在的不足,采用多源数据进行行程时间预测以提高精度。浮动车、固定检测器是常用的交通信息采集方法,在信息种类、数据精度等方面存在一定的互补性。因此,选择2种检测器的实时交通数据作为模型输入参数。利用卡尔曼滤波理论,以流量、占有率、行程时间作为输入量构成参数矩阵,建立城市道路网络行程时间预测模型。并通过Vissim仿真实验验证了模型的有效性。结果表明:基于多源数据的行程时间预测模型平均绝对相对误差为5.45%,其精度比单独采用固定检测器检测数据预测提高了14.4%,比单独采用浮动车数据预测提高了7.5%。      

Improved Speed Estimation From Single-Loop Detectors With High Truck Flow

Single-loop detectors are the most common sensors employed by freeway traffic management agencies. The data are used for traffic management and traveler information. Single-loop detectors can only measure flow and occupancy. Although speed is often the most useful metric, it can only be estimated at conventional single-loop detectors. Typically this estimate comes from the quotient of flow and occupancy multiplied by the fixed, assumed average effective vehicle length. This conventional approach is limited because the actual average effective vehicle length will vary from sample to sample. Many researchers have proposed alternatives to address this problem, and although many of the methods work well under normal conditions, there has been limited research into methods that yield reliable estimates under heavy truck traffic. Heavy truck flows may arise as a function of location or time of day, for example, with proximity to a trucking facility or in early mornings when the number of passenger vehicles drops, respectively. This article presents a new methodology to estimate speed from single-loop detectors in conditions where trucks comprise a large percentage of the fleet. While the focus is on single loop detectors, the work is equally applicable to side-fire microwave radar detectors that emulate single-loop detectors.     

面向实时交通状态参数估计的浮动车车速置信优化算法

针对浮动车数据稳定性问题,在浮动车及其数据特性分析的基础上,设计了考虑车速置信权重的交通状态参数算法。以车速、样本数和时间为衡量基准得到置信权重,据此计算交通状态参数。在出现异常数据时进行去噪处理;在数据量不足或数据连续性不好的情况下融合历史数据及临近时段数据,以反映真实交通状态。通过编程仿真和实地实验,对优化算法进行数值分析和测试,证明该算法可有效消除异常数据波动和数据量不足的影响,对交通状态参数估计具有较高的准确度和平稳性。     

Fusion de données pour l'estimation des temps de parcours via la théorie de l'évidence

The emergence of new information technologies and the transformation that has occurred in traffic management have both increased drivers' already considerable need for road traffic information. The travel time is one of the forms in which this information is presented, and a number of systems are based on its dissemination. In this context, this indicator is used as a measure of the impedance (or cost) of routes on the network and/or a congestion indicator. This raises the problem of estimating travel times with an acceptable degree of accuracy, which is a particularly difficult task in urban areas as a result of difficultes of a theoretical, technical and methodological nature. Thus, in order to find out the traffic conditions that prevail on an urban road, the traffic sensors that are usually used to measure traffic conditions are ineffective under certain circumstances. New measurement devices (cameras, GPS or cellphone tracking, etc.) mean that other sources of data are increasingly used in order to supplement the information provided by conventional measurement techniques and improve the accuracy of travel) time estimates. As a result, travel time estimation becomes a typical data fusion problem. This study deals with a multisource estimate of journey times and attempts to provide a comprehensive framework for the utilization of multiple data and demonstrate the feasibility of a travel time estimation system based on the fusion of data of several different types. In this case two types of data are involved, data from conventional induction loop sensors (essentially flow and occupancy measurements) and data from probe vehicles. The selected modelling framework is the Dempster-Shafer Evidence Theory, which has the advantage of being able to take account of both the imprecision and uncertainty of the data. The implementation of this methodology has demonstrated that, in each case, better results are achieved with fusion than with methods based on a single source of data and that the quality of the information, as measured by correctly classified rates, improves as the degree of precision required of the estimate is increased.     

基于决策融合的城市道路交通事件自动检测算法研究

为了提高城市道路交通事件自动检测算法的性能,引入算法性能可靠度概念对基于浮动车数据和感应线圈数据的事件自动检测算法检测结果进行决策融合。决策融合算法包括3个模块：①感应线圈数据算法模块：选择流量、占有率、路段长度、前一个检测周期的检测参数作为输入参数,训练BP网络进行事件判别;②浮动车数据算法模块：使用误差分析理论确定满足数据精度要求的最小浮动车样本量,选择路段行程时间、行程速度作为BP网络输入参数,进行事件判别;③决策融合模块。引入算法性能可靠度概念,计算模块一和模块二判别结果的权重值,使用加权平均法进行决策融合。通过Vissim仿真获得数据,在Matlab中编程实现算法的计算,仿真结果表明决策融合算法的性能优于单数据源事件自动检测算法。     

固定检测器和移动检测器的交通信息融合方法

固定检测器和移动检测器在检测参数、数据精度、覆盖范围、采集成本等方面存在较大差异,具有很强的互补性.分析了固定检测器与移动检测器进行信息融合的必要性,提出了交通信息融合的总体框架.在分别阐述了基于固定检测器和基于移动检测器的区间平均速度估计方法基础上,采用BP神经网络对区间平均速度进行信息融合.以上海市南北高架道路为对象,利用Vissim仿真软件对基于BP神经网络的交通信息融合方法进行了实例分析,结果表明,该方法可以明显提高区间平均速度的精度.     

Improving the Performance of Loop-Sensor-Based Traffic-Information System by (M+PLL) Circuit

Conventionally a phase-shift detection method (PSDM) and a frequency-shift detection method (FSDM) have been used in loop detectors. The PSDM has a fast response time and is very effective in detecting vehicles traveling at normal speeds. However, it is well known that the detection results are erroneous for vehicles traveling at low speeds in heavy traffic conditions. On the other hand, the FSDM greatly improves the detector performance for heavy traffic conditions. However, this method is not effective in fast and normal traffic conditions. Thus, in order to collect accurate traffic data for all traffic conditions, this paper proposes combining two methods using the digital OR logic. In the developed circuit, a phase-locked loop (PLL) circuit is used for measuring the phase change. This paper also develops a new loop detector instrumentation method using the so-called M circuit for detecting frequency change. The developed method has been tested for various traffic conditions. Experimental results show that the new combined M and PLL detection method greatly improves the accuracy in all traffic conditions, reducing the error rate in measuring traffic flow by more than 83%, when compared to the PSDM.     

Improving the Performance of Loop-Sensor-Based Traffic-Information System by (M+PLL) Circuit

Conventionally a phase-shift detection method (PSDM) and a frequency-shift detection method (FSDM) have been used in loop detectors. The PSDM has a fast response time and is very effective in detecting vehicles traveling at normal speeds. However, it is well known that the detection results are erroneous for vehicles traveling at low speeds in heavy traffic conditions. On the other hand, the FSDM greatly improves the detector performance for heavy traffic conditions. However, this method is not effective in fast and normal traffic conditions. Thus, in order to collect accurate traffic data for all traffic conditions, this paper proposes combining two methods using the digital OR logic. In the developed circuit, a phase-locked loop (PLL) circuit is used for measuring the phase change. This paper also develops a new loop detector instrumentation method using the so-called M circuit for detecting frequency change. The developed method has been tested for various traffic conditions. Experimental results show that the new combined M and PLL detection method greatly improves the accuracy in all traffic conditions, reducing the error rate in measuring traffic flow by more than 83%, when compared to the PSDM.     

基于DTA的OD估计方法的交通检测器优化布置模型研究

论文在探讨了动态交通分配和动态0D估计背景下的交通检测器优化问题的基础上，提出了基于DTA的动态OD估计方法的交通检测器布置原则；从预算，对路网中交通流量信息的覆盖程度，对关键路段的检测、对重复检测器的剔除等方面对路网交通检测方案进行约束，建立了交通检测器优化布置模型；最后将遗传算法用于交通检测器优化布置模型的求解，证明了基于DTA的动态0D估计方法的交通检测器优化布置模型的有效性。论文方法概念清楚、操作简单，是交通检测器优化布置的一种可行方法。     

A hybrid model based method for bus travel time estimation

Providing accurate information about bus arrival time to passengers can make the public transport system more attractive. Such information helps the passengers by reducing the uncertainty on waiting time and the associated frustrations. However, accurate estimation of bus travel time is still a challenging problem, especially under heterogeneous and lane-less traffic conditions. The accuracy of such information provided to passengers depends mainly on the estimation method used, which in turns depends on the input data used. Hence, developing suitable estimation methods and identifying the most significant/appropriate input data are important. The present study focused on these aspects of development of estimation methods that can accurately estimate travel time by using significant inputs. In order to identify significant inputs, a data mining technique, namely the k-NN classifying algorithm, was used. It is based on the similarity in pattern between the input and historic data. These identified inputs were then used in a hybrid model that combined exponential smoothing technique with recursive estimation scheme based on the Kalman Filtering (KF) technique. The optimal values of the smoothing parameter were dynamically estimated and were updated using the latest measurements available from the field. The performance of the proposed algorithm showed a clear improvement in estimation accuracy when compared with existing methods.     

基于ADAS联网时空数据的路段交通参数估算模型

交通参数实时获取是道路交通管控的重要基础。针对固定检测器观测范围受限和浮动车数量需求大的问题，研究了1种利用车载ADAS联网数据进行路段交通参数估算的方法。通过分析车载ADAS感知的前向目标参数与交通参数的关系，结合广义交通量定义，并考虑多车道条件下ADAS车辆及其邻近前车的相对运动变化特性，建立了1种非稳态交通条件下的交通参数估算模型。在仿真实验环境下获得定参数据集和验证数据集，完成对模型的参数标定和验证，并探讨时空分辨率和ADAS车辆渗透率对模型估算精度的影响规律。基于实验数据分析，结果表明，时间分辨率降低5 min，所提模型估算误差平均减小3.4%，降低时间分辨率可以提升所提模型的估算精度；空间分辨率降低500 m，流量和密度的估算误差平均减小1.68%，却可能导致速度估算误差平均增加5.19%；ADAS车辆渗透率的增长可以增强估算交通参数和观测交通参数在路段时空区域的契合程度。在ADAS逐渐装车应用的背景下，所提的交通参数估算模型可快速、精准获取路段连续时空范围内的交通量信息。      

Assessing the performance of SpeedInfo radar traffic sensors

Traffic speed is a crucial input for real-time traffic management applications. Operating agencies typically deploy their own sensors to collect the measurements, e.g., loop detectors. Recently, SpeedInfo emerged with a different paradigm for traffic speed collection: instead of selling hardware to operating agencies, at each link the company deploys its own Doppler radar in a self-contained wireless unit to measure traffic speeds and then sells the speed data. This study uses well-tuned loop detector-based speed measurements to evaluate 15 of the Doppler radar sensors over several months while the two traffic data collection systems were operating concurrently. The extended study period includes potentially challenging and transient conditions for the radar sensors: both recurrent (rush hour congestion and late night low flow) and nonrecurrent (incidents and precipitation). The analysis took a broad overview, comparing speed measurements from the radar sensors against the concurrent loop detector data and then explicitly looked for any anomalous pattern in the radar data such as latency and system outages. The work found the radar measurements are generally good, but also identified several points that should be considered before deployment, including latency, different biases in free flow and congestion, vulnerability to precipitation, and sensitivity to mounting angle.     

基于单截面低频检测数据的信号交叉口排队长度估计

针对我国大多数中小城市信号交叉口交通检测数据条件及基于此数据条件下存在的信号交叉口排队长度估计精度不高问题,研究了基于单截面低频定点检测数据的信号交叉口排队长度估计模型.利用时间占有率与流量、速度之间的函数关系对长排队(排队长度超出检测器位置)进行识别.根据信号配时数据切分低频检测器数据,并与信号配时数据匹配.基于交通波理论,通过关键点的判别求取周期最大排队长度.以青岛市山东路-江西路南进口为例进行仿真和实证验证.结果显示,长排队的识别精度达到了90% 以上,不同饱和度下(低、中、高)的信号交叉口排队长度估计精度均达到了80% 以上,其中,中、低饱和度场景下排队长度平均绝对误差小于20 m/cycle,高饱和度场景下排队长度平均绝对误差小于45 m/cycle.      

基于时间序列相似性搜索的交通流短时预测方法

为了进一步提高交通流短时预测的效果,在分析现有预测模型存在问题的基础上,设计了1种基于时间序列相似性搜索的交通流短时多步预测方法.利用界标模型对交通流时间序列数据进行模式表示,在历史数据库中搜索与当前交通流时间序列相似度较高的历史时间序列,进而确定与预测时刻相对应的历史数据,利用回声状态网络模型实现交通流的短时多步预测.采用某特大城市快速路5 min采样间隔的交通流量数据进行实验验证和对比分析.实验结果表明,回声状态网络模型的预测精度分别比ARIMA模型和BP神经网络模型提高了6.25%和3.85%,以时间序列相似性搜索结果作为模型输入数据能够进一步提高交通流短时预测的精度.      

基于状态空间模型的道路交通状态多点时间序列预测

以多点的道路交通状态为研究对象,把道路交通状态单点预测向多点同时预测扩展,提出了基于状态空间模型的道路交通状态多点时间序列预测方法。首先,利用道路交通状态的多点时间序列数据建立多维自回归模型,转化状态空间模型形式,接着利用EM算法估计状态空间模型参数,从而得到多点道路交通状态的状态空间模型;其次,根据时间序列数据估计系统状态,利用卡尔曼滤波算法进行一步预测,补充新的数据并更新系统状态递推预测;最后,利用某城市快速路上相邻6个交通检测器采集的多点时间序列数据验证模型的有效性,并与卡尔曼滤波单点预测方法相对比。结果表明:该模型是可行和有效的。     

基于单线圈的车速检测算法研究

简述了基于环形线圈车速检测的基本原理、目前利用单线圈检测量进行车速估计的方法。在此基础上，提出了一种利用单线圈进行车速实时检测的算法，并设计了一套基于单线圈的车速检测系统，通过采集系统在实验中的数据，运用Matlab6．5对该检测算法进行了仿真分析。结果表明，算法基本符合设计要求。     

基于地图匹配的城市道路实时交通状态模糊综合判别方法

依托于浮动车数据,基于地图匹配对城市道路交通状态模糊综合判别方法进行深入研究.首先根据浮动车数据特点和道路交通信息,基于Mapbasic编程对数据进行地图匹配,并进行MapInfo二次开发,通过相关模型计算指定时段内的道路交通参数.建立模糊综合评价判别模型,对判别结果量化处理,以最大隶属度原则确定道路交通状态.最后,选...     

基于行程时间估计的快速路检测器布设密度优化方法研究

随着智能交通运输系统对道路实时交通信息的迫切需求,布设高密度、全方位的交通检测器已成为共识,但是高密度的检测器布设带来的是硬件投资的急剧增加,因此有必要进行检测器布设密度的优化。利用快速路行程时间估计方法分析了检测器布设密度优化问题,认为从行程时间估计误差和合理投资的角度出发,检测器布设并不是密度越大效果越好,而是存在一个合理范围,该范围可以指导工程中实际检测器数目的选择。     

基于监控数据的高速公路实时事故风险模型

近年来,高速公路事故发生率高居不下.同时,对于高速公路而言,其交通流检测器安装又较为普遍.因此研究如何深入挖掘交通流检测数据以实现对高速公路事故风险实时预测很有必要.基于美国加州2012年发生事故最多的4条高速公路I5,I10,I405和I15的全年事故数据和交通流数据,以病例对照基本思路选取事故组和对照组数据,选定交通流数据研究范围,并选用ADASYN算法处理不平衡数据集问题.基于随机森林模型,利用事故发生前10~40 min内的事故地上游4个检测器、下游2个检测器的3种基本交通流数据构建高速公路实时事故风险模型,事故预测准确率可达到88.02%.选取重要性前十的变量作为事故重要诱导因素,对事故重要诱导因素进行调值,将调值后的测试集放入之前构建的随机森林模型进行分类预测,结果显示减少了41.82%的事故,故可认为利用事故重要诱导因素可进行事故先兆预警工作,从而减少事故的发生.      

基于浮动车移动检测与感应线圈融合技术的行程时间估计模型

综合考虑到浮动车检测技术与感应线圈检测技术的优缺点,为了提高道路行程时间估计的精度及完备性,提出基于浮动车与感应线圈的融合检测技术的行程时间估计模型。该模型利用神经网络技术对两种检测技术同一路段的检测数据进行融合,从而达到提高道路行程时间估计精度和完备性的目的。最后,以广州市7 000多辆装有GPS装置的出租车所提供的浮动车数据、100多个安装在广州市各主要道路口上的感应线圈检测器提供的感应线圈数据以及广州市交通电子地图为基础,在10条道路上分别随机选取的500个两种检测数据对提出的模型进行了验证,试验结果表明,此模型在道路行程时间估计的精度方面较浮动车移动检测技术与感应线圈技术有较大提高。