An Approach to Dynamical Classification of Daily Traffic Patterns

This article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.     

关于单向交通通行能力的探讨

1前言随着国民经济的迅猛发展和城市人口的不断增加，我国各大城市的机动车保有率急剧增加，全国各城市机动车年平均增长率为20％，仅以沈阳为例，目前机动车已达30余万辆，比80年代初增加10倍多。由于各种原因，城市道路的发展速度严重滞后于机动车的发展，各大城市都出现了程度不同的交通拥挤、阻滞和延误，伴随而来的交通事故率也逐年上升，给国家和人民造成巨大的经济损失，不同程度地影响着改革开放和城市经济的发展。为改变上述状况，全国各地都在采取相应的措施，一方面大力发展公共交通，修建地铁。轻轨，拓宽旧路、交叉口，建设立…     

Kernel Smoothing Method Applicable to the Dynamic Calibration of Traffic Flow Models

Abstract: Real time traffic flow simulation models are used to provide traffic information for dynamic traffic management systems. Those simulation models are supplied by traffic data in order to estimate and predict traffic conditions in unobserved sections of a traffic network. In general, most of recent real time traffic simulators are based on the macroscopic model because the macroscopic model replicates the average traffic behavior in terms of observable variables such as (time–space) flow and speed at a relatively fast computational time. Like other simulation models, an important aspect of the real time macroscopic simulator is to calibrate the model parameters online. The most conventional way of the online calibration is to add a random walk to the parameters to constitute an augmentation of the traffic variables and the model parameters to be estimated. Actually, this method allows the parameters to vary at every time step and, therefore, describes the adaptation of the model to the prevailing traffic conditions. However, it has been reported that the use of the random walk results in a loss of information and an increase of the covariance of parameters, which consequently leads to posteriors that are far more diffuse than the theoretical posteriors for the true parameters. To this end, this article puts forward a Kernel density estimation technique in the calibration process to handle the covariance issue and to avoid the information loss. The Kernel density estimation technique is embedded in the particle filter algorithm, which is extended to the calibration problems. The proposed framework is investigated using real‐life data collected in a freeway in England.     

Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow

Abstract:   This article addresses the problem of the accuracy of short-term traffic flow forecasting in the complex case of urban signalized arterial networks. A new, artificial intelligence (AI)-based approach is suggested for improving the accuracy of traffic predictions through suitably combining the forecasts derived from a set of individual predictors. This approach employs a fuzzy rule-based system (FRBS), which is augmented with an appropriate metaheuristic (direct search) technique to automate the tuning of the system parameters within an online adaptive rolling horizon framework. The proposed hybrid FRBS is used to nonlinearly combine traffic flow forecasts resulting from an online adaptive Kalman filter (KF) and an artificial neural network (ANN) model. The empirical results obtained from the model implementation into a real-world urban signalized arterial demonstrate the ability of the proposed approach to considerably overperform the given individual traffic predictors .     

城市路网交通流动态模型研究

从交通流的内在机理出发,通过对路网各交叉道口、路段目点和陷点的流量等参数的数量关系描述,导出用路段端口流量、速度、密度等参数表示的路段流量净增量方程组、路段间流量转移方程组、路网边界方程组以及路网总流量方程组等方程组群,全面描述路网各路段流量、速度、密度等交通参数的动态数量关系。只要路段端口流量、速度、密度连续可观,则可通过联立相关方程组,求出各路段产生的动态流量、邻接路段间流量动态转移系数以及路网动态总流量等动态参数,且边界方程的定义使路网可任意分割,有利于局部交通流动态分析。     

Temporal Evolution of Short-Term Urban Traffic Flow: A Nonlinear Dynamics Approach

Abstract:   Recognizing temporal patterns in traffic flow has been an important consideration in short-term traffic forecasting research. However, little work has been conducted on identifying and associating traffic pattern occurrence with prevailing traffic conditions. We propose a multilayer strategy that first identifies patterns of traffic based on their structure and evolution in time and then clusters the pattern-based evolution of traffic flow with respect to prevailing traffic flow conditions. Temporal pattern identification is based on the statistical treatment of the recurrent behavior of jointly considered volume and occupancy series; clustering is done via a two-level neural network approach. Results on urban signalized arterial 90-second traffic volume and occupancy data indicate that traffic pattern propagation exhibits variability with respect to its statistical characteristics such as deterministic structure and nonlinear evolution. Further, traffic pattern clustering uncovers four distinct classes of traffic pattern evolution, whereas transitional traffic conditions can be straightforwardly identified .     

Bi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization

Abstract:   Although dynamic traffic control and traffic assignment are intimately connected in the framework of Intelligent Transportation Systems (ITS), they have been developed independent of one another by most existing research. Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi-level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time-dependent demand and stochastic route choice. In the bi-level programming model, the upper level problem represents the decision-making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real-time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed.     

一种建立古旧建筑物动力分析模型的方法

提出了用实验与理论分析相结合来建立古旧建筑物动力分析模型的方法,以一古塔为例介绍了这一实施过程,通过实验与理论分析和对理论分析模型的修正,获得了该塔较准确的动力分析模型,从而解决了建立古塔动力分析模型的困难.     

龙凤山凝析气藏气井合理配产研究

龙凤山凝析气藏为一受岩性物性控制的特低渗凝析气藏,储层属特低孔、特低渗储层;孔喉结构复杂,属中孔、微-细喉道。地露压差小,凝析油含量高,极易发生反凝析伤害及井筒积液。如何科学、合理的确定单井合理产量,既满足天然气开发需求,又有效防止井筒积液显得十分重要。本文综合运用无阻流量、采气指数法、临界携液流量法和数值模拟法确定单井合理产量。通过优化气井配产,延长气井稳产期,提高单井开发效益。     

上倾管内油水两相流流型实验研究

国内多条成品油输送管道在投产和运行过程中,采用"水联运"投产方式所造成的上倾管道低洼处积水现象引起了严重的管道内腐蚀问题。利用上游来油将低洼处积水携出管道能有效缓解内腐蚀。采用0#柴油、去离子水在内径100 mm的上倾管道内观察油水两相流流型并测量油携水临界流速。结果表明,随油流黏性力增大和管道倾角增大,油水两相流依次呈现波状分层流、有水滴的波状分层流和油相占主导的分散流3种流型;同一流型下,油相能将水相携入上倾段的最低临界流速随倾角增大而增大;倾角从20°增大到25°使流型从波状分层流转化为有液滴的波状分层流时,油相能将水相携入上倾段的临界流速从0.203 m/s减小为0.187 m/s;倾角从30°增大至35°时,使初始流型从有液滴的波状分层流转换为水相在油相中的分散流,油相能将水相携入上倾段的临界流速从0.205 m/s减小为0.194 m/s;油相能将水相完全携出上倾段的临界流速随倾角增大而略有增大;发生流型转化的流速随倾角增大而减小。     

污秽绝缘子闪络的动态模型探讨

提出了计算污秽绝缘子污闪电压的分析动态模型,该模型主要针对局部放电电弧的发展。并从绝缘子的结构、电弧动态电阻和电弧传播速度等方面,对其原理进行较为详细地解释和分析,最后进行了讨论。     

城市交通综合整治规划体系探讨

近几年我国城市交通拥堵日益严重,城市交通问题也日趋复杂,而现状中解决城市交通问题的措施却缺乏系统性和整体性,不能从根本上缓解和改善城市交通.文章在对城市交通问题进行近期整治的实践和对现有研究成果的基础上,提出了交通综合整治规划的理念,明确了交通综合整治规划的基本概念和基本思路,探讨构建交通综合整治规划体系结构和体系框架,并总结交通综合整治规划的工作流程,最后以合肥市老城区交通综合整治规划研究为实例对所构建的理论体系进行了实证分析.     

膨胀土判别与分类方法评价与建议

对现有膨胀土判别与分类方法进行了评价,对反映和表征膨胀土胀缩机理和特性的指标进行深入探讨。最后建议用fisher判别分析方法进行膨胀土的判别与分类,并建议用反映膨胀土胀缩性、强度特征的指标来验证分类结果。     

单变量控制提高交通安全可靠度方法探讨

为探讨现有公路路段交通安全系统可靠度达不到预期要求的问题,利用事故故障树分析方法建立了道路交通安全系统模型,并以其为基础建立了交通安全系统的可靠度分配机制,通过提高少数变量可靠度的故障树方法,达到提高系统总体可靠度,并以道路交通事故发生火灾为例对可靠度进行了分配与计算,结果表明:少数子系统可靠度的提高实现了整个安全系统的可靠度提高,需要提高可靠度的子系统为整个系统中可靠性较低的子系统,在设计上,方法简单、易于操作控制,具有可行性,能够很好的在实际事故安全系统中加以运用。     

Patterns in Performance-Orientated Design: An Approach towards Pattern Recognition,Generation and Instrumentalisation

The question of pattern in architecture generally divides architects into two distinct groups: those with an aesthetic interest in man-made ornament; and those who take a deeper interest in the processes that underlie the formation of pattern in the natural world. An investigation of the performative, though, enables an exploration of pattern that arises out of the interaction of man-made interventions with the natural environment. Michael Hensel , Professor for research by Design at AHO, the Oslo School of Architecture and Design, and Achim Menges of the Emtech (Emergent Technologies and Design programme) at the Architectural Association in London, explore this arena based on their research and educational work at the AA and other international institutions, which focuses on aspects of performance in the built environment. Copyright © 2009 John Wiley & Sons, Ltd.     

Case-Based Reasoning for Real-Time Traffic Flow Management

Real-time traffic management is a promising approach for alleviating congestion. This approach uses real-time and predicted traffic information to develop routing strategies that optimize the performance of highway networks. This article explores the potential for using case-based reasoning (CBR), an emerging artificial intelligence (AI) paradigm, to overcome the limitations of existing traffic-management decision support systems. To illustrate the feasibility of the approach, the article develops and evaluates a prototype CBR routing system for a real-world network in Hampton Roads, Virginia. Cases for building the system's case base are generated using a heuristic dynamic traffic assignment (DTA) model specifically designed for the region. Using a set of 25 new independent cases, the performance of the prototype system is evaluated by comparing its solutions with those of the DTA model. The evaluation results demonstrate the feasibility of the CBR approach. The prototype system was capable of running in real time and produced high-quality solutions using case bases of reasonable size.     

电梯交通流预测算法研究

高效电梯群控系统需要合理地安排与分配梯群对呼梯信号的响应,而准确的交通流量预测又是决定实时动态交通分配的重要因素,交通流量预测结果的好坏直接关系到交通控制与调度的效果。一种好的电梯群控系统交通流预测方法,能够为电梯群控调度算法的进一步研究提供依据,实现电梯的优化调度。