Using Electronic Toll Collection Data to Understand Traffic Demand

In this study, we explored the potential of using electronic toll collection (ETC)-derived data that are a part of intelligent transport systems (ITS). Dynamic origin–destination (OD) traffic volumes were estimated using ETC data on the Hanshin Expressway. A dynamic OD estimation model that was suggested in a previous study was used, and abundant ETC data were input to improve the estimation accuracy. The results of OD estimation were analyzed to understand traffic demand and its variation. External factors were clarified that have an influence on variances in the OD flows, and statistical analysis methods for the variations were proposed depending on the factors. Moreover, the improvements in traffic simulation accuracy and performance as a result of using ETC data as input variables in the simulation models were discussed. According to the results of this study, ETC data have potential to assist in understaningd traffic demand and its variation, and the results can be applied to network management.     

城市道路交通数据采集系统检测器优化布点研究

智能运输系统(ITS)被公认为是当前解决我国大城市交通拥挤和提高道路安全的有效手段。利用检测器收集交通数据是ITS的基本功能之一。如何在城市道路网中布设交通检测器是ITS建设时必须要研究的内容。本文通过对城市道路网基本路段交通流量的相似性分析,建立了路网交通检测器优化布点的数学模型。并通过一个算例,详细叙述了建模及求解的全过程。作者希望所述的检测器优化布点方法能为城市ITS建设中的此类研究提供一种新的思路。     

Estimating Missing Traffic Volume Using Low Multilinear Rank Tensor Completion

Traffic volume data have been collected and used for various purposes in some aspects of intelligent transportation systems (ITS) applications. However, the unavoidable detector malfunction can cause data to be missing. It is often necessary to develop an effective approach to recover the missing data. In most previous methods, temporal correlation is explored to reconstruct missing traffic volume. In this article, a new missing traffic volume estimation approach based on tensor completion is proposed by exploring traffic spatial–temporal information. The tensor model is utilized to represent traffic volume, which allows for exploring the multicorrelation of traffic volume in spatial and temporal information simultaneously. In order to estimate the missing traffic volume represented by the tensor model, a novel tensor completion algorithm, called low multilinear rank tensor completion, is proposed to reconstruct the missing entries. The proposed approach is evaluated on the PeMS database. Experimental results demonstrate that the proposed method is more effective than the state-of-art methods, especially when the ratio of missing data is high.     

Short-Term Traffic Flow Forecasting for Freeway Incident-Induced Delay Estimation

Freeway incidents not only threaten travelers’ safety but also cause severe congestion. Incident-induced delay (IID) refers to the extra travel delay resulting from incidents on top of the recurrent congestion. Quantifying IID would help people better understand the real cost of incidents, maximize the benefit-cost ratio of investment on incident remedy actions, and develop active traffic management and integrated corridor management strategies. By combining a modified queuing diagram and short-term traffic flow forecasting techniques, this study proposes an approach to estimate the temporal IID for a roadway section, given that the incidents occurs between two traffic flow detectors. The approach separates IID from the total travel delay, estimates IID for each individual incident, and only takes volume as input for IID quantification, avoiding using speed data that are widely involved in previous algorithms yet are less available or prone to poor data quality. Therefore, this approach can be easily deployed to broader ranges where only volume data are available. To verify its estimation accuracy, this study captures two incident videos and extracts ground-truth IID data, which is rarely done by previous studies. The verification shows that the IID estimation errors of the proposed approach are within 6% for both cases. The approach has been implemented in a Web-based system, which enables quick, convenient, and reliable freeway IID estimation in the Puget Sound region in the state of Washington.     

A Comparative Study of Three Multivariate Short-Term Freeway Traffic Flow Forecasting Methods With Missing Data

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in intelligent transportation systems (ITS) technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in the literature. However, most studies used univariate forecasting methods, and they have limited forecasting abilities when part of the data is missing or erroneous. While the historical average (HA) method is often applied to deal with this issue, the forecasting accuracy cannot be guaranteed. This article makes use of the spatial relationship of traffic flow at nearby locations and builds up two multivariate forecasting approaches: the vector autoregression (VAR) and the general regression neural network (GRNN) based forecasting models. Traffic data collected from U.S. Highway 290 in Houston, TX, were used to test the model performance. Comparison of performances of the three models (HA, VAR, and GRNN) in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.     

交通流预测方法综述

实时交通流预测是智能运输系统研究的一个重要问题。为此,建立了许多预测模型,有历史平均模型、时间序列模型、卡尔曼滤波模型、非参数回归模型、神经网络模型和组合模型等。总结评述现存的各类模型,提出交通流预测研究领域今后可能的发展趋势。     

西安市交通拥堵和空气污染的成因和政策研究

近年来,西安市交通拥堵和空气污染问题突出。文章旨在分析这两大问题的现状、成因以及现行政策的效果。对于改善拥堵和空气污染,限行政策在短期内有显著效果,燃煤整治明显改善了空气质量。而从长远来看,正面效果会随着机动车数量的增长而逐渐消失。文章将有助于政府进一步针对拥堵和雾霾的长期政策研究。     

基于卡尔曼滤波的城市快速路交通密度估计与拥堵识别

针对城市快速路网中只有部分路段检测器可用的情况,为准确地估计交通密度并基于此快速识别路网所有路段的交通拥堵情况,研究了基于宏观交通流模型的卡尔曼滤波器设计方法.结合动态图混杂自动机(DGHA)与元胞传输模型(CTM)对快速路网建模,在此基础上推导出分段仿射线性系统(PWALS)模型.基于所得到的模型设计出切换型卡尔曼滤波器进行交通密度估计,并通过将路段密度估计值与临界拥堵密度进行对比来对快速路网的拥堵进行识别.以京通快速路为例进行实验,结果表明,真实值与估计值的平均绝对误差为MAE=0.625 988,显示了所提方法的有效性.      

修正四阶段法在城市交通抗灾规划中的应用

根据地震环境下不同恢复阶段的社会经济活动及交通系统特性,对传统交通预测四阶段法———出行产生、出行分布、方式划分、交通分配的宏观模型进行了修正,讨论了不同模型的参数变化趋势,建议了参数的调整原则及方法,进而以修正四阶段法为基础进行震后交通需求预测。该方法简便易行,预测得到道路通行能力、流量、负荷度和平均运行时间等多个指标,为城市交通系统防灾规划提供决策依据。最后,结合我国东南沿海某城市的工程实践,有针对性的进行了应用和验证。     

Human body modelling for traffic accident analysis

A traffic accident is a complex phenomenon with vehicles and human beings involved. During a collision, the vehicle occupant is exposed to substantial loads, which can cause the occupant injuries that depend on the level of passive safety, as well as on the occupant's individual characteristics. Correct estimation of injury severity demands a validated human body model and known impact conditions. A human body modelling procedure for the purpose of accident analysis is introduced. The occupant body has been modelled as a multibody system with rigid body segments connected. Geometrical and inertial properties of individual body segments were estimated using computed tomography. Frontal impact conditions were simulated on a sled test facility, while the human body dynamic response was measured. Comparison of experimental data and computer simulation revealed an influence of joint resistive properties on the occupant motion in collisions. The difference between measured and simulated response was minimised using optimisation method. Individualised human body modelling procedure enabled better prediction of the occupant motion during vehicle collision and thus more precise estimation of possible injuries in real-life traffic accidents.     

基于有向图卷积神经网络的交通预测与拥堵管控

为解决城市快速路正面临的日益严重的交通拥堵问题,提出了一种针对城市快速路的基于有向图卷积神经网络的交通预测与拥堵管控方法,该方法能够有效利用海量交通数据进行交通预测,实现拥堵的主动管控。首先,基于交通路网的空间有向性和交通流的时空特性,定义了有向的距离影响矩阵、修正欧式距离矩阵和自由流可达矩阵,构建出有向的图卷积算子,并将其应用于长短时记忆神经网络模型中,提出了能学习交通路网时空双重特性的有向图卷积-长短时记忆神经网络（Directed Graph Convolution-LSTM,DGC-LSTM）模型;其次,基于DGC-LSTM的交通预测结果识别出拥堵产生点并将其作为拥堵管控的对象;再次,采用控制进口匝道车辆输入快速路主线的手段,针对管控对象的时空特征,设计了全圈层分时段阶梯式拥堵管控策略;最后,基于上海市快速路网上布设的2 712个检测器在122个工作日每间隔5 min记录的速度、流量和占有率信息,开展实例分析,测试了DGC-LSTM模型的预测精度以及全圈层分时段阶梯式拥堵管控策略的有效性。结果表明：与传统的循环神经网络、长短时记忆神经网络相比,DGC-LSTM模型具有更高的预测精度,能将速度预测的平均绝对误差和误差标准差分别降低38%和20%以上;基于预测结果采用的全圈层分时段阶梯式拥堵管控策略能令拥堵产生点的速度提升14 km·h^-1以上,并能使拥堵的持续时长缩短40%,可阻止拥堵从产生点开始发生大范围的蔓延,降低整个路网的拥塞程度。     

Probe vehicle lane identification for queue length estimation at intersections

Vehicles instrumented with Global Positioning Systems, also known as GPS probe vehicles, have become increasingly popular for collecting traffic flow data. Previous studies have explored the probe vehicle data for estimating speeds and travel time; however, there is very limited research on predicting queue dynamics from such data. In this research, a methodology was developed for identifying the lane position of the GPS-instrumented vehicles when they are standing in the queue at signalized intersections with multiple lanes, particularly in the case of unequal queue. Various supervised and unsupervised clustering methods were tested on data generated from a microsimulation model. Among the tested methods, the Optimal Bayes Rule that utilizes probability density functions estimated using bivariate statistical mixture models was found to be effective in identifying the lanes. The methodology for lane identification was tested for queue length estimation. This research confirms that the lane identification is an important step required prior to the queue length estimation. The accuracies of the models for lane identification and queue length estimation were evaluated at varying levels of demand and probe vehicle market penetrations. In general, as the market penetration increases, the accuracy improves as expected. The result shows that 40% market penetration rate is adequate to reach about 90% accuracy.     

智能交通国际发展概况和国内优先考虑的课题

综述智能交通系统10年来在国际上的发展状况，讨论了我国研究开发智能交通系统的优先链。指出：在宏观上，我国应首先制定发展智能交通系统的战略规划以及相应的技术标准体系，以保证智能交通系统的兼容性、扩展性和互联性。并建议在我国及早成立智能交通协调机构，认为在我国优先发展的智能交通项目应首先考虑解决城市交通堵塞问题。     

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...     

山区道路弯道上车辆自动减速系统分析、建模与设计

受弯道离心力影响,在弯道处车辆易发生侧滑和侧翻,山区道路转弯车道为交通事故高发区.车辆自动减速系统运用ITS技术自动控制车辆,使其能以安全车速通过弯道,避免交通事故发生.阐述车辆在转弯道处的运行机理,并对其在直线路段和曲线路段的运行情况分别建立数学模型,且对模型进行解析,以展现车辆自动减速系统的控制过程.通过对数学模型...     

北京市交通信号管理与控制平台设计及应用

在分析现有交通信号系统的特性以及存在问题的基础上,以对现有信号系统进行有效整合、综合利用交通流数据、提高交通信号控制的管理水平为目标,设计了1种集成的交通信号控制平台,并给出了平台在北京市交通信号管理与控制系统中的应用。     

Estimation des impacts atmosphériques des projets de gestion de trafic : de l’application des modèles théoriques sur des cas concrets

In addition to its harmful economical and social effects, road traffic congestion is a key contributor of emissions of CO₂ and local pollutants. Many traffic management policies could be implemented to tackle it. However, the impacts of those strategies on air quality and greenhouse effect are poorly predicted by classical assessment procedures. Indeed, current assessment tools are based on aggregated traffic data, which fail to capture the dynamic effects of traffic management policies on traffic flow. This article presents an estimation tool capable of measuring all the effects of traffic management policies. It is based on a commonly used dynamic traffic flow model (AIMSUN) combined with different emission models, in order to identify modelling features leading or not to differences in results. Different models combination have been applied and compared for two traffic regulation strategies: a HOV lane dedicated only to buses and taxis created on the leftmost lane of A1 between Charles de Gaulle airport and Paris, and a reduction of speed limits on a section of motorway A86 in the northwest of Paris. Substantial differences in predictions of different emission models are highlighted. They cannot be entirely explained by differences in emission laws. Other insights with regard to traffic representation and emission models?? inputs are also investigated to explain them.     

Performance Evaluation of Handoff-Based Cellular Traffic Monitoring Systems Using Combined Wireless and Traffic Simulation Platform

Handoff-based cellular probe technologies have been proven to be a cost-effective solution for traffic surveillance due to their low cost, large sample size, and intensive spatial coverage. At the same time, the development of reliable simulation tools that can be used to conduct a feasibility study, performance evaluation, and estimation model testing for cellular probe technologies, especially in the arterial environment, is still limited. In this study, by simulating the real-world signal delaying and wireless signal communication protocols, we establish a simulation platform for evaluating cellular probe system. The simulation platform eliminates unrealistic assumption on static and regular cell boundaries in the existing simulation tools and can provide simulation results similar to field observations. Based on the platform, we compared the performance of cellular probe system under both freeway and arterial environment. We analyzed the impact of traffic conditions, the duration of cellphone calls, and penetration rate on the handoff efficiency, handoff link speed error, and information-disseminating link speed estimation error. In addition, we also provide a detailed evaluation of the accuracy of the intersection delay estimation and the potential error sources and mechanism. The results show that the handoff-based cellular probe technologies attain good performance in traffic monitoring and the proposed simulation platform can reflect the real-world condition on an acceptable level.     

Modeling arterial travel time distribution by accounting for link correlations: a copula-based approach

The estimation of urban arterial travel time distribution (TTD) is critical to help implement Intelligent Transportation Systems (ITS) and provide travelers with timely and reliable route guidance. The state-of-practice procedure for arterial TTD estimation commonly assumes that the path travel time follows a certain distribution without considering link correlations. However, this approach appears inappropriate since travel times on successive links are essentially dependent along signalized arterials. In this study, a copula-based approach is proposed to model arterial TTD by accounting for spatial link correlations. First, TTDs on consecutive links along one arterial in Hangzhou, China are investigated. Link TTDs are estimated through the nonparametric kernel smoothing method. Link correlations are analyzed in both unfavorable and favorable coordination cases. Then, Gaussian copula models are introduced to model the dependent structure between link TTDs. The parameters of Gaussian copula are obtained by Maximum-Likelihood Estimation (MLE). Next, path TTDs covering consecutive links are estimated based on the estimated copula models. The results demonstrate the advantage of the proposed copula-based approach, compared with the convolution without capturing link correlations and the empirical distribution fitting methods in both unfavorable and favorable coordination cases.     

浅析交通共用信息平台的发展对智能交通标准化进程的促进作用

随着智能交通发展的深入,以及人们对于交通信息需求的提高,解决交通信息集成、共享问题已成为当务之急。然而,智能交通标准化的相对滞后,成为信息集成、共享的重要壁垒。要想解决这种现状,必须探求发展与标准化之间相互促进,良性循环的办法。建设交通共用信息平台是解决信息集成与共享问题的重要途径之一。交通共用信息平台的建设和发展对于智能交通体系框架、通信协议、数据格式等方面的标准化提出了很高的要求,反过来也将对智能交通标准化进程起到积极的促进作用。