城市道路交通数据可视分析综述

目的 交通是困扰现代大都市的世界性难题.近年来,可视分析技术在分析和利用交通大数据中扮演了越来越重要的角色,成为一项重要的智能交通技术.本文将全面回顾自信息可视化和可视分析兴起以来城市交通数据可视分析领域的研究现状.方法 从道路交通流量分析和其他交通问题分析两个方面,按照数据的类型及问题的分类探讨交通领域的可视化技术和可视分析系统,简单回顾近年来出现的研究新趋势.结果 早期研究注重对道路流量的可视化展示方案,主要方法有箭头图、马赛克图和轨迹墙等.随着可视分析手段的丰富,对城市道路交通流量的分析层次上升到交通事件层面,但是交通事件的定义仅局限于交通拥堵.应用可视分析的其他交通问题领域包括公共交通、交通事故和人群出行行为等.近年出现了挖掘和利用交通轨迹或交通事件的社会属性或称环境上下文信息的研究新趋势.结论 从对交通流量的可视化到交通事件的可视分析,从面向道路交通状况到与交通相关的其他社会性问题,从单纯反映路况的交通数据到富含社会性语义的多源数据,从传统的PC端可视化和交互范式到新型的可视化展示介质,交通数据可视化领域的研究在深度和广度上都得到大大拓展,未来该领域的研究趋势也体现于其中.     

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.     

多组件化微观交通仿真系统

为了模拟和再现城市路网交通环境,提高交通模型的通用性、可重用性,本文结合城市交通流特性和计算机仿真技术,对车辆、车道、道路、交叉口、信号灯等交通组成元素进行抽象和组件化,建立了具有良好人机交互界面的多组件化微观交通仿真系统．最后结合仿真实例说明多组件化微观交通仿真系统可以用于交通控制策略的测试、评估和对比分析．     

基于多智能体分群同步的城市路网交通控制

在城市交通网络中,经常出现某些路段较为拥堵,而其它路段内仍有空间未被充分利用.为了解决该问题,本文提出一种路网分群一致算法.首先,以路段的空间占有率为状态,建立交通网络状态空间模型,描述路网中车流的传递关系;进一步,将路网中的各条路段抽象为智能体,提出路网多智能体分群一致算法,将路段集划分为若干个非空不交子集,每个子集内的路段状态可达一致,不同子集的一致状态不同.从而,该算法可使各路段的空间占有率达到均衡,减轻局部拥堵,减少车辆延误时间.最后,采用VISSIM交通仿真软件对北京市望京地区的实际网络进行仿真,仿真结果说明了本文方法的可行性和有效性.     

Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery

Urban simulation models and their visualization are used to help regional planning agencies evaluate alternative transportation investments, land use regulations, and environmental protection policies. Typical urban simulations provide spatially distributed data about number of inhabitants, land prices, traffic, and other variables. In this article, we build on a synergy of urban simulation, urban visualization, and computer graphics to automatically infer an urban layout for any time step of the simulation sequence. In addition to standard visualization tools, our method gathers data of the original street network, parcels, and aerial imagery and uses the available simulation results to infer changes to the original urban layout and produce a new and plausible layout for the simulation results. In contrast with previous work, our approach automatically updates the layout based on changes in the simulation data and thus can scale to a large simulation over many years. The method in this article offers a substantial step forward in building integrated visualization and behavioral simulation systems for use in community visioning, planning, and policy analysis. We demonstrate our method on several real cases using a 200 GB database for a 16,300 km2 area surrounding Seattle, Washington.     

Visual perception modelling for intelligent virtual driver agents in synthetic driving simulation

This paper introduces a vision model used within a synthetic (or purely software-based) driving simulation framework. This framework represents driver decision-making, individual vehicle movement and emergent traffic flow, and is intended to aid the integration of driver psychology, traffic management and vehicle engineering. The aims of developing the vision model discussed here are twofold: Firstly, to remove the unrealistic availability of ‘perfect knowledge’ concerning the positions and velocities of vehicles in a simulation and secondly, to provide a means of introducing deeper cognitive models of driver reasoning and behaviour. The paper presents the essential mechanisms of the vision model, along with the results of initial validation experiments conducted with Leicestershire Constabulary, Traffic Division in the UK. In these experiments, subjects' visual perception of positions and speeds of moving vehicles were measured and compared with estimations of the agent based driving simulator. The results have demonstrated the feasibility of modelling driver vision within an agent based traffic simulation using concepts derived from AI and ALife systems. The paper is completed by a short discussion on the future development of cognitive models enabled through more detailed vision modelling.     

Using Raster-Based GIS and Graph Theory to Analyze Complex Networks

Disruptions to transportation networks can be very costly. However, managing disruptions and the costs associated with these events, poses some challenges. Transport networks are, in many cases, large and complex. This paper develops a method, based on complex network theory, to analyse transportation networks. It provides a way, through the use raster-based geographic information system (GIS) techniques, to identify critical nodes or links in a network that reflect spatial interdependencies with other networks and to assess how resilient the networks are to failures of these locations. For purposes of illustration, the method is applied to the network of major roads and rail in the State of Florida.     

基于模糊神经网络的交通干线分层递阶控制

针对城市交通干线协调控制的要求,提出了利用模糊神经网络分层递阶控制的方法。采用两层结构,第一层为控制层。针对单个路口,对下一时间段内路口各个方向的车流量进行预测。并在此基础上计算出下一时间段内各个路口的周期、相序、各个方向上的绿信比;第二层是协调层,综合主干方向的车流状况及各个路口的情况,采用模糊神经网络对各个路口的周期、相位及主干方向的绿信比进行调整。仿真结果表明,该方法优于定时控制,达到了减少车辆的停车次数和延误时间的目的。     

基于连通可靠度的交通网络重要路段评价算法

如何提高交通网络的连通可靠度,是交通领域的一个重要问题,通过提高重要路段的连通度来提高交通网络的连通可靠度是一个重要的方法。引入复杂网络理论中接近度、关键度等概念,结合交通网络中的流量分布,利用图的对偶理论识别出交通网络的重要路段,提出评价交通网络中路段重要度的算法,通过实例验证所提方法的有效性和应用价值。     

基于复杂网络的路网交通拥堵评估仿真模型

为了能够深入分析路网交通拥堵动态演进过程, 为交通拥堵治理提供决策分析工具, 提出了一个基于复杂网络的路网拥堵评估仿真模型。通过将路段阻抗概念引入复杂网络理论, 实现了路网拓扑模型和流量模型的结合; 此外, 还提出了虚拟测试车辆遍历的路网通行能力评估方法, 对不同路网条件下的道路通行能力进行评估。最后在PC系统上实现了完整的路网拥堵评估仿真系统, 并利用该系统对拥堵路段数量、交通流量和路网拓扑结构等因素对路网通行能力的影响进行了仿真分析。仿真实验结果与经典交通理论分析结果一致, 并能反映更多的动态过程信息, 表明该模型能够准确有效地进行交通路网拥堵评估, 可以为相应的交通拥堵管理决策提供依据。     

Critical street links for demand responsive feeder transit services

This paper presents a methodology to identify and locate critical links in a grid street network system for feeder transit services. A ‘critical’ link can be defined as a link that—when eliminated from or appended to an existing network—would cause the largest change in the network connectivity and consequently in the transit performance. The most significant contribution of this study is to present a simple analytical approach to locating the critical link(s) for a grid street network system of any size with uniform passenger demand across the service area. The distance between demands points have been used as the basic measure of impedance in the final derived closed-form equations. Easily computable formulas for identifying the critical links have been validated by simulation analyses with the street network system from the City of St. Joseph, Missouri. Useful insights from the analytical derivations and simulation results indicate a monotonic decrease in link criticality when moving from the centrally located links to those located at the periphery in a grid street network system.     

基于高分辨率遥感影像的城市群交通路网监测技术研究

为了推动高分辨率遥感影像在交通行业的应用,提升城市群交通路网规划、调查与管理水平,将高分遥感应用于城市群路网监测。应用分区人机交互式SNAKES算法实现了高分辨率遥感影像道路信息提取,利用边缘检测结合支持向量机算法实现车辆信息提取。在此基础上,阐述了应用路网和车辆矢量信息开展城市群交通状态判别、新建住宅公交站点需求分析等交通行业应用技术方法,为今后深入应用奠定了基础。     

Comparative study on simulation performances of CORSIM and VISSIM for urban street network

With the progress of simulation technologies, many transportation simulation packages were developed. However, little information is available to the users in applying these models to the most appropriate situations, or even seldom with the simulation accuracy of the individual model. This study conducts a comparative analysis of two popular simulation models (VISSIM and CORSIM), based on their simulation performances on an urban transportation network. Road network and field traffic data from North Bund, Hongkou District, Shanghai, China were used as the simulation background and input. Sensitivity analysis was carried out to compare the performance of both models based on four key indices, namely software usability, average control delay, average queuing length, and cross-sectional traffic volume. Advantages of each simulator were identified based on comparison analyses of simulations with different levels of congestion and intersection geospatial scales. The main performance difference was found lying in the default parameter configuration within the models, including driver behavior settings, traffic environment settings, and vehicle types, etc. Consequently, it was recommended that analysts should choose their appropriate tools based on intersection type and level of saturation within the simulation case.     

基于混沌理论的网络数据流RBF神经网络预测

应用相空间重构理论，研究了网络数据流的混沌特性，计算了实际网络数据流的关维数、Lyapunov指数，证实网络数据流存在混沌现象；据此建立了基于径向基函数(RBF)预测模型，对实际网络数据流进行预测。仿真实验表明，相对于反向传播(BP)神经网络预测，基于混沌理论的RBF神经网络预测方法学习速度快，预测精度高。     

贵阳公交复杂网络抗毁性分析

城市公共交通系统可以抽象为由公交线路和停靠站点构成的网络,公交网络构成了一个典型的复杂网络。复杂网络的研究在过去几年得到了迅速发展,其中复杂网络的抗毁性是研究焦点之一。在本文中,以贵阳市的公共汽车交通系统中的停靠站点网络为研究对象,测试了网络在随机攻击和选择性攻击情况下的抗毁性。结果表明,该网络对节点的随机攻击具有较强的抗击能力,而对选择性攻击的抗击能力较弱。     

On hierarchical modelling of motion for workflow analysis from overhead view

Understanding human behaviour is a high level perceptual problem, one which is often dominated by the contextual knowledge of the environment, and where concerns such as occlusion, scene clutter and high within-class variations are commonplace. Nonetheless, such understanding is highly desirable for automated visual surveillance. We consider this problem in a context of a workflow analysis within an industrial environment. The hierarchical nature of the workflow is exploited to split the problem into ‘activity’ and ‘task’ recognition. In this, sequences of low level activities are examined for instances of a task while the remainder are labelled as background. An initial prediction of activity is obtained using shape and motion based features of the moving blob of interest. A sequence of these activities is further adjusted by a probabilistic analysis of transitions between activities using hidden Markov models (HMMs). In task detection, HMMs are arranged to handle the activities within each task. Two separate HMMs for task and background compete for an incoming sequence of activities. Imagery derived from a camera mounted overhead the target scene has been chosen over the more conventional oblique views (from the side) as this view does not suffer from as much occlusion, and it poses a manageable detection and tracking problem while still retaining powerful cues as to the workflow patterns. We evaluate our approach both in activity and task detection on a challenging dataset of surveillance of human operators in a car manufacturing plant. The experimental results show that our hierarchical approach can automatically segment the timeline and spatially localize a series of predefined tasks that are performed to complete a workflow.     

Forecasting warranty performance in the presence of the ‘maturing data’ phenomenon

Forecasting of warranty performance helps car engineers to fine-tune their strategies for warranty cost reduction. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at a certain future time, but also future MIS values. However, the ‘maturing data’ phenomenon that causes a warranty performance measure at specific MIS values to change with time make such forecasting challenging. Although dynamic linear models have been used for forecasting warranty performance, the focus mainly has been to utilize previous-model-year vehicle data for the analysis. In this paper, we apply a neural network model to forecast year-end warranty performance in the presence of the ‘maturing data’ phenomenon. We use a special type of neural network, viz. radial basis function (RBF), and optimize its parameters by minimizing training and testing errors through planned experimentation. This application shows the effectiveness of RBF neural networks to forecast warranty performance in the presence of the ‘maturing data’ phenomenon.     

A system for the generation of synthetic Wide Area Aerial surveillance imagery

The development, benchmarking and validation of aerial Persistent Surveillance (PS) algorithms requires access to specialist Wide Area Aerial Surveillance (WAAS) datasets. Such datasets are difficult to obtain and are often extremely large both in spatial resolution and temporal duration. This paper outlines an approach to the simulation of complex urban environments and demonstrates the viability of using this approach for the generation of simulated sensor data, corresponding to the use of wide area imaging systems for surveillance and reconnaissance applications. This provides a cost-effective method to generate datasets for vehicle tracking algorithms and anomaly detection methods. The system fuses the Simulation of Urban Mobility (SUMO) traffic simulator with a MATLAB controller and an image generator to create scenes containing uninterrupted door-to-door journeys across large areas of the urban environment. This ‘pattern-of-life’ approach provides three-dimensional visual information with natural movement and traffic flows. This can then be used to provide simulated sensor measurements (e.g. visual band and infrared video imagery) and automatic access to ground-truth data for the evaluation of multi-target tracking systems.     

基于遗传算法的城市混合型路网优化设计研究

城市路网设计问题就是研究如何用定量的方法在已有交通网络上添加或扩容某些路段的问题。本文提出一种基于遗传算法的城市混合型路网设计的双层优化模型,可求出最优的用于道路网新建或改善的交通建设投资决策方案,并利用一个算例进行仿真试验,结果表明,该模型和算法是可行的,可为城市路网设计提供借鉴。     

智能交通系统消息交换的复杂度研究

针对道路上自动化管理的智能交通难题,利用车辆无线传感器网络对车位检测的辅助定位进行了研究,阐述了基于无线传感器网络的智能交通在提高车辆行驶的安全和效率方面的优势。首先讨论无线传感器网络技术在交通道路系统中的整体应用以及该技术在国内外的发展现状以及分析;然后讨论交通道路传感器网络系统的总体概述;接着对车辆传感器网络辅助定位的理论进行了深入的分析、数据的计算;最后详细地给出了计算车辆传感器网络广播协议的消息传输总时间复杂度的公式。