Effects of real-time traffic information systems on traffic performance under different network structures

The effects of real-time traffic information system (RTTIS) on traffic performance under parallel, grid and ring networks were investigated. The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures. For traffic on a parallel network, the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%, and a proportion of RTTIS usage higher than 90% would actually deteriorate the performance. For both grid and ring networks, a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS. For all three network structures, vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.

Screening and reconstruction of real-time traffic data

The quality of real time traffic information is of the great importance, therefore the factors having effect on traffic characteristics are analyzed in general, and the necessities of real time data processing are summarized. The identification and reconstruction of real time traffic data are analyzed using Kalman filter equation and statistical approach. Four methods for ITS (Intelligent transportation system) detector data screening in traffic management systems are discussed in detail. Meanwhile traffic data examinations are discussed with solutions formulated through analysis, and recommendations are made for information collection and data management in future.

Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

this paper develops a real-time traffic signal timing model which is to be integrated into a single intersection for urban road, thereby solving the problem of traffic congestion. We analyze the current situation of the traffic flow with release matrix firstly, and then put forward the basic models to minimize total delay time of vehicles at the intersection. The optimal real-time signal timing model (non-fixed cycle and non-fixed split) is built with the Webster split optimal model. At last, the simulated results, which are compared with conventional model, manifest the promising properties of proposed model.

Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.

Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.

AN INTELLIGENT METHOD FOR REAL-TIME DETECTION OF DDOS ATTACK BASED ON FUZZY LOGIC

The paper puts forward a variance-time plots method based on slide-window mechanism tocalculate the Hurst parameter to detect Distribute Denial of Service(DDoS)attack in real time.Basedon fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoSjudgment mechanism is designed.This new method calculates the Hurst parameter quickly and detectsDDoS attack in real time.Through comparing the detecting technologies based on statistics andfeature-packet respectively under different experiments,it is found that the new method can identifythe change of the Hurst parameter resulting from DDoS attack traffic with different intensities,andintelligently judge DDoS attack self-adaptively in real time.

计及实时交通特征的电动公交车备用率与充电桩联合优化

为了提高电动公交车及充电站总投资和运营的经济性,需综合考虑公交车和充电桩的购置成本以及充电成本。以道路特征、实时交通特征、环境温度为主要影响因素,计算电动公交车的行驶时间和能耗;在满足公交车运营基本需求和保证车内温度的前提下,以日公交车购置成本、日充电桩建造成本、日充电成本之和最小为目标进行优化,建立电动公交车备用率与充电桩的联合优化模型,并采用改进的离散型自适应粒子群优化算法进行计算,得到公交车和充电桩的最优购置方案以及各季节和全年的日均投资及运营成本。以某公交线路上电动公交车年内多种运营条件为算例,验证所建优化模型的正确性。

基于实时交通流的上海市机动车碳污同源排放清单

为满足交通碳污减排政策多样化、差别化和精细化发展需求,扩大交通排放测算范围、提高测算准确度和时空分辨力,提出了一种基于实时交通流的高时空分辨力机动车碳污同源排放清单构建方法,综合交通模型、IVE模型和相关算法建立了覆盖上海全市的机动车碳污同源排放清单。结果显示,二氧化碳、氮氧化物周排放规律相近,呈现出明显的周末低工作日高的特征;二氧化碳分时变化规律呈现明显的双峰,而氮氧化物受重型车影响,峰值出现在夜间;由于各车型的碳污分担率不同且活动区域不同造成了上海市机动车二氧化碳和氮氧化物排放的空间分布差异明显;碳污同源排放清单中,NOx、PM、CO、VOCs、CO_(2)的月均排放量分别为5160t、440t、1.7×10^(4)t、732t和2.46×10^(6)t;排放分担率表明,NOx、PM等污染物主要来自重货车及大货车排放;CO、VOCs和CO_(2)排放来源以小客车排放占比最高。

智能行车记录仪图像去雾系统的FPGA设计

雾霾天气下,交通道路能见度低,导致所采集到的视频画面退化、图像信息模糊,同时考虑传统系统处理实时性不高等问题,基于ZYNQ平台设计了一种图像去雾系统,并应用于智能行车记录仪系统中.首先,针对传统暗通道去雾算法在天空区域存在失真等问题,提出了一种分割天空区域的策略来修正图像复原参数;然后,针对计算全局大气光值时,需对整幅图像的像素排序消耗大量资源的问题,利用现场可编程门阵列(FPGA)并行运算的优势,提出一种帧迭代方法优化求取大气光值,同时优化了引导滤波的硬件设计;最后,将双路高清多媒体接口(HDMI)资源中,一路作为视频输入,另一路作为视频处理输出,搭建实时交通图像视频处理试验平台.试验结果表明,系统针对雾霾天气下的交通视频具有较好的去雾效果,尤其是可以解决天空区域去雾的失真问题.在对分辨率为1280像素×720像素的交通视频去雾时,可以达到30帧/s的处理速度,满足实时性要求.

基于流计算和大数据平台的实时交通流预测

目前交通流预测实时性差,很难满足在线分析和预测任务的需求,基于此提出一种Flink流计算框架和大数据平台结合的实时交通流预测方法。基于流计算框架实时捕捉和预处理数据,包括采用Flink的transform算子对数据进行校验和处理,将处理后的数据sink到大数据的HDFS文件系统,交由下一步的大数据并行框架进行分析建模与训练,实现基于流计算和大数据平台的实时交通流预测。实验结果表明,Flink能够实时捕捉和预处理交通流数据,把数据准时无误送入分布式文件系统中,在此基础上借助大数据框架下的并行分析和建模优势,在实时性数据分析与预测方面取得了较好的效果。

高速公路实时交通流模糊逻辑优化控制系统设计

针对高速公路交通流特征复杂多变,并受到天气、道路状况和车辆类型等多种因素影响的问题,设计了一种新的高速公路实时交通流模糊逻辑优化控制系统,包括硬件和软件两部分;在硬件方面,设计了车流控制模块、无线传输车载终端、实时交通流逻辑调度模块和模糊控制器模块,并确定了它们之间的实时连接关系;在软件方面,求解了交通流逻辑控制条件,联合了高速公路实时通行相位条件,调节了模糊控制器,并根据高速公路交通路网模型推导了交通流优化控制条件的求解表达式;实验结果表明,该系统可以解决逻辑协调错误的问题,并有效控制高速公路车流量。

交通救援应急中转路径调度数学模型构建仿真

由于交通状况往往是动态变化的,包括交通拥堵、道路封闭等情况,救援应急时导致车辆中转路径调度救援应急难度加大。为进一步提升应急救援时救援车的救援效果,提出交通救援应急中转路径实时调度数学建模方法。根据城市交通中信号交叉口车辆的微观运行特征,确定车辆应急救援时道路交叉路口应急安全状态;设计救援应急中转路径调度模型,将模型分为上下两个层级,结合混合布谷鸟算法求解模型,完成救援应急中转路径实时调度。实验结果表明,利用上述方法开展救援应急中转路径实时调度时,路径中转次数最多仅为10次,耗时为150ms,调度效果好、性能高。

多传感器融合下道路交通车辆碳排放量实时监测方法

面对日趋复杂的道路交通情况,在车辆碳排放量监测时,容易受到干扰因素影响,导致监测结果不精准,提出了多传感器融合下道路交通车辆碳排放量实时监测方法。使用多传感器阵列采集车辆碳排放量信息,利用卡尔曼滤波法计算采集信息各个样本与融合中心欧式距离,以该距离为半径画圆,将全部采集的车辆碳排放量信息融入其中。使用多传感器融合技术的卡尔曼滤波法构建监测点滤波处理模型,结合多传感器融合框架感知多维数据,剔除噪声后监测点预测值。根据线性微分方程累减白化方程时间,构建基于多传感器融合技术的监测模型。根据多传感器双层融合过程,实现车辆碳排放量的实时监测。由实验结果可知,该方法监测结果与实际统计数据存在最大为0.05 Mt的误差,监测所需最短时间为0.1 s,为交通车辆绿色通行提供技术支持。

基于神经网络的实时事故预测方法研究进展

道路交通事故预测作为道路主动安全管理的重要组成部分,在降低事故发生概率、帮助管理者制定安全决策等方面起着重要作用。随着数据需求的不断增加,传统方法已无法满足大数据的需求,机器学习和人工智能算法在动态、实时和复杂情况下的道路交通事故预测领域显示出强大的潜力。文章从数据获取和特征变量选择开始介绍,详细叙述了基于机器学习的神经网络及与深度学习结合后该方法在国内外的相关研究,分析了使用神经网络相关方法在建模时会面临的优缺点,最后对基于神经网络的交通实时事故预测方法进行了总结及展望,给出未来的发展趋势。

YOLO算法及其在自动驾驶场景中目标检测综述

自动驾驶场景下的目标检测是计算机视觉中重要研究方向之一,确保自动驾驶汽车对物体进行实时准确的目标检测是研究重点。近年来,深度学习技术迅速发展并被广泛应用于自动驾驶领域中,极大促进了自动驾驶领域的进步。为此,针对YOLO(You Only Look Once)算法在自动驾驶领域中的目标检测研究现状,从以下4个方面分析。首先,总结单阶段YOLO系列检测算法思想及其改进方法,分析YOLO系列算法的优缺点;其次,论述YOLO算法在自动驾驶场景下目标检测中的应用,从交通车辆、行人和交通信号识别这3个方面分别阐述和总结研究现状及应用情况;此外,总结目标检测中常用的评价指标、目标检测数据集和自动驾驶场景数据集;最后,展望目标检测存在的问题和未来发展方向。

Utility-based and fairness-aware radio resource allocation in OFDMA cellular relay networks with traffic prioritization

The combination of orthogonal frequency division multiple access(OFDMA) with relaying techniques provides plentiful opportunities for high-performance and cost-effective networks.It requires intelligent radio resource management schemes to harness these opportunities.This paper investigates the utility-based resource allocation problem in a real-time and non-real-time traffics mixed OFDMA cellular relay network to exploit the potentiality of relay.In order to apply utility theory to obtain an efficient tradeoff between throughput and fairness as well as satisfy the delay requirements of real-time traffics,a joint routing and scheduling scheme is proposed to resolve the resource allocation problem.Additionally,a low-complexity iterative algorithm is introduced to realize the scheme.The numerical results indicate that besides meeting the delay requirements of real-time traffic,the scheme can achieve the tradeoff between throughput and fairness effectively.

Priority-Based Resource Allocation for Downlink OFDMA Systems Supporting RT and NRT Traffics

Efficient radio resource management is essential in Quality-of-Service (QoS) provisioning for wireless communication networks. In this paper, we propose a novel priority-based packet scheduling algorithm for downlink OFDMA systems. The proposed algorithm is designed to support heterogeneous applications consisting of both real-time (RT) and non-real-time (NRT) traffics with the objective to increase the spectrum efficiency while satisfying diverse QoS requirements. It tightly couples the subchannel allocation and packet scheduling together through an integrated cross-layer approach in which each packet is assigned a priority value based on both the instantaneous channel conditions as well as the QoS constraints. An efficient suboptimal heuristic algorithm is proposed to reduce the computational complexity with marginal performance degradation compared to the optimal solution. Simulation results show that the proposed algorithm can significantly improve the system performance in terms of high spectral efficiency and low outage probability compared to conventional packet scheduling algorithms, thus is very suitable for the downlink of current OFDMA systems.

基于实时交通信息的PHEV模型预测控制策略研究

为提升插电式混合动力汽车(PHEV)的燃油经济性,提出了一种基于实时交通信息的车速预测方法,并以燃油经济性最优为目标,借助动态规划(DP)算法在预测时域内进行实时最优转矩分配,建立基于模型预测控制(MPC)的整车能量管理策略。MATLAB/Simulink仿真平台验证结果表明:与传统车速预测方法相比,基于实时交通信息的车速预测方法的车速预测精确度提高了13.5%;与基于历史车速的模型预测控制策略相比,基于实时交通信息的模型预测控制策略使整车燃油经济性提高了9.5%。

HYBRID INTERNET TRAFFIC CLASSIFICATION TECHNIQUE

Accurate and real-time classification of network traffic is significant to network operation and management such as QoS differentiation, traffic shaping and security surveillance. However, with many newly emerged P2P applications using dynamic port numbers, masquerading techniques, and payload encryption to avoid detection, traditional classification approaches turn to be ineffective. In this paper, we present a layered hybrid system to classify current Internet traffic, motivated by variety of network activities and their requirements of traffic classification. The proposed method could achieve fast and accurate traffic classification with low overheads and robustness to accommodate both known and unknown/encrypted applications. Furthermore, it is feasible to be used in the context of real-time traffic classification. Our experimental results show the distinct advantages of the proposed classifi- cation system, compared with the one-step Machine Learning (ML) approach.