The Adaptive Road Routing Recommendation for Traffic Congestion Avoidance in Smart City

A fuzzy neural network (FNN) calculates the traffic-light system and extends or terminates the green signal according to the traffic situation at the given junction while also computing from adjacent intersections. In the presence of public transports, the system decides which signal(s) should be red and how much of an extension should be given to green signals for the priority-based vehicle. Using fuzzy logic, we propose a model with a neural network for public transport, normal cars, and motorcycles. The model controls traffic-light systems to reduce traffic congestion and help vehicles with high priority pass through. The system also monitors the density of car flows and makes real-time decisions accordingly. In order to verify the proposed design algorithm, we adapted the simulations of Simulation of Urban MObility, ns2, and green light district simulation method to our model, and further results depict the performance of the proposed FNN in handling traffic congestion and priority-based traffic. The promising results present the efficiency and the scope of the proposed multi-module architecture for future development in traffic control.     

基于模糊控制理论的交通信号控制算法研究

造成交通拥挤往往突出表现在道路的交叉口处,在综合了模糊控制技术和城市信号交叉口交通信号控制技术基础上,针对多路口交通控制的特征及实际交通状况,对已有模糊控制算法进行了改进,在单路口模糊控制研究的基础上,研究了基于相序优化模糊控制的城市区域交通信号控制系统。此方法不需要建立复杂的交通流模型,对城市交通控制系统实施模糊控制,可以有效地解决交通信号控制过程中复杂性和随机性难题,从而对绿灯信号的调整做出更合理的匹配,提高交叉路口的通行率近20%。     

PLC在交通信号控制系统中的应用

随着我国城市化和现代化建设的迅速发展,交通问题成为社会经济发展的一个大问题。原有的信号灯定时控制方法已经无法满足越来越拥堵道路的需求。城市道路交通信号PLC智能控制系统运行可靠、操作简便、应用范围广,能够有效降低车辆延误及停车的次数,缓解道路交通拥堵情况。本文基于S7-200系列PLC控制系统,根据时间指令将24小时分为:白天控制与夜间控制两个时间段,并运用了实时检测、判断延时,实现交通灯信号的智能控制,有效缓解交通高峰期的压力和主干道路口的拥挤现状,推动我国城市道路交通信号灯控制系统的科学化、智能化发展。     

An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems

Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.     

基于多Agent强化学习的危险车辆预警算法

针对目前行人易受到车辆撞击,且缺乏主动保护手段的问题,文中设计了一个包括雷达等模块的智能可穿戴设备来保护行人免受车辆的冲击。在此基础上,提出了基于模糊综合评价的安全智能算法,从行人的角度出发,综合考虑将雷达探测的车辆数据、当地道路交通状况、天气、行人状态等多种影响因素作为评价指标。为提高算法的准确性和适应性,提出了基于BP神经网络和多Agent强化学习的方法赋予模糊综合评价的各指标动态权重。仿真验证结果显示,相较于AHP等取权重方法,该预警算法的警报准确率提高了55%以上;相较单Agent强化学习,该方法学习效率提高了近28倍,说明该智能穿戴设备可以对车辆撞击行人进行有效地预测和警告。     

同步电动机智能励磁调节器的仿真研究

针对同步电动机励磁系统的非线性等特点,提出智能励磁调节器。文章对模糊控制和神经网络控制以及两者的结合进行了分析研究,提出神经网络模糊控制器,并进行了带参数自调整的神经网络模糊控制器的设计。最后对理论进行了对比仿真验证,证明了其优越性。     

对交通信号灯模糊控制的自主创新性设计

针对日益严重的交通拥堵问题设计出一种新型两级模糊控制方案来对四相位三车道单交叉口的交通信号灯进行实时智能控制。该新方案沿用了当前各种交通信号灯控制方案的优点,同时针对其不足之处进行了弥补与完善,是一种同时具有自适应控制、分级模糊控制、相位繁忙优先和准确显时等优势的控制方案,更适用于实际的交通情况。对新型模糊控制方案进行了仿真研究,仿真结果表明该方案明显优于传统控制方案。最后还对该新方案进行了动态模拟演示,使其更具可观性和真实性,更易于运用到交通现场。     

Distributed and cooperative fuzzy controllers for trafficintersections group

This paper presents a fuzzy traffic controller for a set of intersections and its simulation results. The controller of an intersection controls its own traffic and cooperates with its neighbors. It gets information from its traffic detectors and its neighbors. Using this information, the fuzzy rule base system gives optimal signals. It manages phase sequences and phase lengths adaptively to its neighbors' as well as its own traffic conditions. To carry out the performance evaluation of the controller, a simulator for intersection groups has been developed. The proposed method is compared with the vehicle actuated method which is one of the typical conventional methods. The average delay time of a vehicle is used as a performance index. The simulation results show good performance in the case of time-varying traffic patterns and heavy traffic conditions     

Traffic accident prediction using 3-D model-based vehicle tracking

Intelligent visual surveillance for road vehicles is the key to developing autonomous intelligent traffic systems. Recently, traffic incident detection employing computer vision and image processing has attracted much attention. In this paper, a probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3-D model-based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Finally, vehicle activity is predicted by locating and matching each partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments show the effectiveness of the proposed algorithms.     

An Arterial Green-Wave Synchronous Coordination Model for Bus and Non-bus Lanes Based on Platoon Dispersion Theory

This paper presents an arterial green-wave synchronous coordination model for bus and non-bus lanes based on platoon dispersion theory. As the traffic light at an upstream intersection change from red to green, the dispersive characteristics of these vehicles moving from upstream to the downstream were analyzed by assuming velocities of two platoon following a normal distribution pattern. The model aims at analyzing relationship between traffic flow, distance between adjacent intersections, and signaling time in order to achieve arterial green-wave synchronous coordination in both the bus and non-bus lanes. To facilitate coordination in a traffic signal control system, the number of vehicles forced to stop at the head of the platoon as well as the number of vehicles trapped at the tail of the platoon were determined and presented in a tabular form for use in the proposed traffic light coordination model. Finally, a numeric computation for the coordination of successive signals is presented to illustrate the validity of the proposed model.     

一种新型校车智能监控终端的设计

近年来不断发生的校车事故引发了全社会对校车安全的普遍关注,本文设计了一种校车智能监控终端的设计方案,该系统要包括GPS定位模块设计、RFID无线射频识别模块设计,音视频模块接口模块设计、CAN总线接口模块、3G无线传输接口模块设计、存储模块设计、提醒及报警模块设、电源模块设计以及基于ARM9的控制平台等。本设计采用实时性高的ARM9处理器芯片作为控制器的核心,提高车载智能终端的同时,维护预期目标的设计理念,能够保证各个终端信息同时满足客户需求。     

Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion

Previous research has shown that current driving conditions and driving style have a strong influence over a vehicle's fuel consumption and emissions. This paper presents a methodology for inferring road type and traffic congestion (RT&TC) levels from available onboard vehicle data and then using this information for improved vehicle power management. A machine-learning algorithm has been developed to learn the critical knowledge about fuel efficiency on 11 facility-specific drive cycles representing different road types and traffic congestion levels, as well as a neural learning algorithm for the training of a neural network to predict the RT&TC level. An online University of Michigan-Dearborn intelligent power controller (UMD_IPC) applies this knowledge to real-time vehicle power control to achieve improved fuel efficiency. UMD_IPC has been fully implemented in a conventional (nonhybrid) vehicle model in the powertrain systems analysis toolkit (PSAT) environment. Simulations conducted on the standard drive cycles provided by the PSAT show that the performance of the UMD_IPC algorithm is very close to the offline controller that is generated using a dynamic programming optimization approach. Furthermore, UMD_IPC gives improved fuel consumption in a conventional vehicle, alternating neither the vehicle structure nor its components.      

A QoS-provisioning neural fuzzy connection admission controller formultimedia high-speed networks

This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract     

基于多智能体技术的城市智能交通控制系统

本文中提出一种城市交通智能控制系统,针对城市交通网络中相邻交叉口的交通流可能相互冲突,即局部交通流的优化可能引起其他区域交通状况的恶化的问题,采用多智能体协调控制方法来协调相邻交叉口处的控制信号消除网络中的交通拥塞.提出以一个智能体的方式实现一个信号灯交叉口控制,对多个信号灯交叉口形成的交通网络采用多智能体协调控制的方式实现网络流量优化来消除拥塞.文中提出由递归建模和改进的贝叶斯学习相结合的多智能体系统来使智能体可以确定其他智能体的准确模型并实时更新信息,并基于上述方法在简单的交通网络模型上建立了多智能体交通控制系统,仿真结果表明了方法的有效性,对实现智能交通系统有重要意义.     

城市信号交叉口公交优先及模糊控制策略

城市公共交通优先(公交优先)是公交优先发展的一个重要措施,对缓解交通压力及城市的可持续发展具有重大意义,公交优先信号控制的关键是信号交叉口的公交优先控制策略。概述了交叉口公交优先技术研究的现状,介绍了一种信号交叉口的公交优先模糊控制方法,引入相序优化器,并着重分析了基于相序优化的交叉口公交优先模糊控制策略。     

A Hybrid Neuro-Fuzzy System for Adaptive Vehicle Separation Control

The primary purpose of this paper is to develop a robust adaptive vehicle separation control in the increasingly important roles of intelligent transportation system (ITS). A hybrid neuro-fuzzy system (HNFS) is proposed for developing the adaptive vehicle separation control to minimize the distance (headway) between successive cars. This hybrid system consists of two modules: a headway identification (prediction) module and a control decision module. Each of these modules is realized with a distinct neuro-fuzzy network that upgrades hierarchical granularity and reduces the complexity in the control system. Given the current headway and relative velocity between the two consecutive cars, the headway identification module predicts the headway of the next time instant. This identified headway, together with the desired velocity are input to the control decision module whose output is the actual acceleration/deceleration control output. The HNFS encapsulates the adaptive learning capabilities of a neural network into a fuzzy logic control framework to fine-tune the fuzzy control rules. On the other hand, rules derived initially from well-defined fuzzy phase plane accelerate the training of the neural network. Simulation results are very encouraging. It is observed that the headway decreases significantly without sacrificing speed. Furthermore, both stability and robustness of HNFS are demonstrated.     

十字路口模糊交通控制器的设计

介绍了一种基于模糊控制理论的十字路口模糊交通控制器的设计。针对当前城市交通系统中的被控对象具有较大的随机性和偶然性,引入了不需要建立精确数学模型的模糊控制。首先根据进道口的检测器测得车量的多少,由第1级模糊控制器确定下一执行相位;然后根据当前相位车量的多少以及下一相位车辆的多少由第2级模糊控制器确定当前执行相位的绿灯延长时间。较好地解决了交通流量不均衡、不稳定所带来的车辆延误等问题,提高了道路的通行能力。     

基于ARM的六相位交通信号机设计

针对传统信号机相位少,运算性能低的缺陷,设计了以LM3S8962为控制核心的六相位交通信号机。在设计平面交叉口六相位模型的基础上,给出信号机控制模式、信号配时参数。根据信号机功能要求及LM3S8962的资源特性,给出信号机的硬件设计方案。利用模块化设计方法,完成各功能模块软件设计,并进行了系统整体测试。测试结果表明,设计的基于LM3S8962的六相位交通信号机功能完善、运行稳定,对于城市复杂平面交叉口,其控制效果优于传统信号机。     

基于视觉传感器的自主车辆地面自动辨识技术研究

该自主车辆地面自动辨识技术是以Leobot—Edu自主车辆作为试验载体,并应用DH—HV2003UC-T视觉传感器对常见的5种行车路面（石子路面、水泥路面、土壤路面、草地路面、砖地路面）进行图像信息的采集,应用Matlab图像处理模块对其依次进行压缩编码、复原重建、平滑、锐化、增强、特征提取等相关处理后,再应用MatlabBP神经网络模块进行模式识别。通过对模式识别结果分析可知,网络训练目标的函数误差为20％,该系统路面识别率达到预定要求,可以在智能车辆或移动机器人等相关领域普及使用。     

基于路口V2P的避撞算法与验证

近年来交通事故数量逐渐攀升,无信号灯路口的人车事故尤其严重。首先,基于行人、车辆在路口的穿插延误支付、碰撞风险支付,建立人车通行的博弈模型,给予双方合理的优先通行决策;进一步地,在此基础上引入驾驶员的交通奖罚支付,改进博弈模型,并通过实例进行对比分析,改进后的博弈模型增加了车辆的让行率,更有利于道路交通安全;此外,改进传统的病毒传播模型,分析交通参与者的违章心理,通过数值模拟仿真,证明了违章处罚的必要性。为今后过街行人与车辆避撞系统的开发提供了理论上的参考。