基于模糊控制的入口匝道智能控制方法

在建立高速路交通流模型的基础上,结合模糊控制理论设计出了基于模糊逻辑的入口匝道控制器,该控制器根据期望密度和实际车流密度的误差和误差变化量,决定入口匝道的调节率。仿真结果表明,该控制器具有较好的稳态和动态性能,能避免交通拥挤,从而提高主线的通行能力。     

Review of road traffic control strategies

Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. After illustrating the main reasons for infrastructure deterioration due to traffic congestion, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance. Selected application results, obtained from either simulation studies or field implementations, are briefly outlined to illustrate the impact of various control actions and strategies. The paper concludes with a brief discussion of future needs in this important technical area.     

Freeway Traffic Control Using Iterative Learning Control-Based Ramp Metering and Speed Signaling

In this paper, an iterative learning approach for the freeway density control under ramp metering and speed regulation is developed in a macroscopic level traffic environment. Rigorous analyses show that the proposed learning control schemes guarantee the asymptotic convergence of the traffic density to the desired one. The two major features of the learning-based density control are: 1) less prior modeling knowledge required in the control system design and 2) the ability to reject exogenous traffic perturbations. The control schemes are applied to a freeway model, and simulation results confirm the efficacy of the proposed approach     

Dependable Traffic Control Strategies for Urban and Freeway Networks

Traffic congestion is a growing problem in many countries around the world. It has been recognized that instead of constructing more roads and freeways to counter this problem it is prudent to improve the utilization of existing road network through a judicious combination of advances in control engineering, communication and information technology. The traffic control architecture proposed in this paper is a combination of communicating Urban Traffic Control Architecture (UTCA) and Freeway Traffic Control Architecture (FTCA). The UTCA combines context-awareness, Cyber-Physical Systems (CPS) principles, and Autonomic Computing System (ACS) principles to optimize traffic congestion and enforce safety in urban traffic network. The UTCA includes a network of adaptive intersection traffic controllers and their immediate supervisory systems, who are also networked. The central piece of each traffic controller is an arbiter, which is a mini CPS. It is aware of the traffic dynamics at the intersection managed by it, by virtue of continuous input from monitoring sensors. Due to this context-awareness ability and its communication ability to exchange traffic information with its neighbors, it can execute policy-based reactions in order to enable safe and efficient traffic throughput at its intersection. Each urban traffic supervisory system is designed with ACS principles in order to minimize downtime caused by environmental emergencies and maximize security of the subsystem under it. A supervisory subsystem will also collect global traffic flow information and contextual constraints from its neighbors. Based on this input it will modify policies and communicate them to its traffic controller for timely adaptation. The urban traffic flowing into freeway traffic will be mediated by Intelligent Ramp Meters (IRM). An IRM interacts with the urban traffic control system and its nearest Intelligent Roadside Unit (IRSU) to regulate the flow of traffic from urban to freeway network. The FTCA consists of a network of mutually interacting IRSUs which monitor traffic flow, communicate with IRMs for providing traffic guidance for freeway drivers. An IRSU will communicate with the vehicles in the zone managed by it in order to provide information on rerouting when road and weather conditions warrant it. It also facilitates exchange of information between vehicles, guide them in lane changes and maintaining safe distance in order to avoid collision.     

Tenth International Conference on Road Transport Information andControl (IEE Conf. Publ. No.472)

The following topics were dealt with: route guidance; driver information systems; traffic information systems; network management; incident management; access ramp control; road user charging; urban traffic management; road traffic control; public transport information; and public transport priority     

基于XDR大数据分析的高架用户识别技术

城市高架路场景下,用户行驶速度相对较快、人流密集,客户对通信体验要求更高。首先,介绍了网络质量管控大数据平台体系架构;其次,设计了基于XDR大数据分析的高架用户识别的算法;最后,通过XDR大数据分析处理,与高架路段指纹库匹配,输出高架用户运动轨迹,提升了高架用户识别准确性,为高架路无线网络质量监控提供辅助支撑。     

电子监控技术在高速公路中的应用

电子监控技术是保障高速公路实现"安全、快速、舒适、方便"管理目标的重要手段.分析了高速公路电子监控技术的层次,分别介绍了高速公路电子监控系统3个层次的具体内容,简要阐述了高速公路系统中电子监控技术的发展趋势.     

基于单周期控制的功率因数校正研究

利用单周期控制（One-Cycle Control,OCC）的功率因数校正器,无需传统功率因数校正电路所需的模拟乘法器、输入电压采样、固定斜波发生器。因此,大大简化了功率因数校正（Power Factor Correction,PFC）电路的设计,缩小了装置体积。文章对OCC PFC原理进行了详细的分析,并研制了一台基于ICE2PCS02的500W PFC校正器,验证了其有效性。     

Linear quadratic state feedback and robust neural network estimatorfor field-oriented-controlled induction motors

A field-oriented control scheme for an induction motor with a linear quadratic optimal regulator and a robust neural network estimator is proposed. The state feedback is designed by using the synchronous frame motor model. The number of the states is increased in order to take into account the presence of two integrators on the flux and torque errors. The resulting model is suitably simplified and the corresponding approximations are discussed. The procedure proposed is shown to be suitable also for the design of the state feedback via the pole placement technique. A comparison with standard proportional integral regulators is provided. The rotor flux is estimated by using a robust neural network observer. The network training set is suitably designed in order to preserve the drive effectiveness also in the presence of large parameter uncertainties. The robust neural observer is compared with an extended Kalman filter and a standard neural network observer. Using a 250 kW induction motor as a case study, the simulation results show the effectiveness of the proposed solution, both during transient and steady-state operating conditions