A parallel algorithm for detecting traffic patterns using stay point features and moving features

In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.

Comprehensive Analysis of Caching Performance under Probabilistic Traffic Patterns for Content Centric Networking

The phenomenon of data explosion represents a severe challenge for the upcoming big data era.However,the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in redundant content transmission and the end-point-based communication model.Information-centric networking(ICN)is a paradigm for the future Internet that can be utilized to resolve the data explosion problem.In this paper,we focus on content-centric networking(CCN),one of the key candidate ICN architectures.CCN has been studied in various network environments with the aim of relieving network and server burden,especially in name-based forwarding and in-network caching functionalities.This paper studies the effect of several caching strategies in the CCN domain from the perspective of network and server overhead.Thus,we comprehensively analyze the in-network caching performance of CCN under several popular cache replication methods(i.e.,cache placement).We evaluate the performance with respect to wellknown Internet traffic patterns that follow certain probabilistic distributions,such as the Zipf/Mandelbrot–Zipf distributions,and flashcrowds.For the experiments,we developed an OPNET-based CCN simulator with a realistic Internet-like topology.

Traffic Patterns in the Silk Road Economic Belt and Construction Modes for a Traffic Economic Belt across Continental Plates

Plans for the Silk Road Economic Belt （SREB） include construction of two axes, two belts and two radiated areas. As a significant national strategy for China, the emphasis is on the construction of roads and achieving economic agglomeration and radiation through the construction of traffic axes. Using published literature and data analyses, this paper studied current traffic patterns between China and other regions within the SREB from the perspective of rail, ocean and air transportation. With regard to existing problems and development prospects of these three types of transportation, we propose construction modes for the traffic economic belt across continental plates and that future construction within the SREB should consider key cities as joints and arterial traffic lines as development axes, promote connecting of joints by lines, advance deep construction through joints and axes, connect lines into net, and develop informationalized traffic economic belt on the basis of trans-regional cooperation.

Fast Multi-Pattern Matching Algorithm on Compressed Network Traffic

Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.

Factors and Pattern of Injuries Associated with Road Traffic Accidents in Hilly District of Nepal

Introduction: In 21st century, road traffic accidents (RTA) are considered as increasing epidemic of non-communicable disease which is abandoned and needs special attention to prevent them. The aim of this study was to assess the factors and pattern of injuries associated with road traffic accidents. Methods: A cross sectional study was conducted among 112 RTA victims and 56 drivers in Palpa District of Nepal. The association of factors and pattern of injuries with exposure to accidents was assessed using Fisher’s exact test. Bivariate logistic regression examined the association between driving and socio-demographics factors and exposure to road accidents. Results: Of 112 RTA victims, 50% were in the age group of 21 to 40 years and 71.4% were male. Drivers who were in the age less than or equal to 30 years were more likely (OR: 3.6;95% CI: 1.0, 14.3) to expose to an accident than those who were above 30 years. Similarly, those having driving speed less than 40 km/hr were less likely to expose to an accident than those with speed 40 - 60 km/hr (OR: 6.0;95% CI: 0.8, 73.5) and those with speed more than 60 km/hr (OR 7.8;95% CI: 1.0, 100.1). Moreover, the driving experience was also found positively associated (OR: 5.6;95% CI: 1.1, 35.5) with the exposure to an accident. Conclusion: Being in younger age group, male gender, morning time, the driving speed, driving experiences, and driving hours on the road were positively associated with RTA. The efforts should be made to enforce laws in control of speed targeting experienced drivers and those with younger age groups.

Safer Design and Less Cost Operation for Low-Traffic Long-Road Illumination Using Control System Based on Pattern Recognition Technique

The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.

Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management

Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.

Modeling and Characterizing Internet Backbone Traffic

With enormous growth of the number of Internet users and appearance of new applications, characterization of Internet traffic has attracted more and more attention and has become one of the major challenging issues in telecommunication network over the past few years. In this paper, we study the network traffic pattern of the aggregate traffic and of specific application traffic, especially the popular applications such as P2P, VoIP that contribute most network traffic. Our study verified that majority Internet backbone traffic is contributed by a small portion of users and a power function can be used to approximate the contribution of each user to the overall traffic. We show that P2P applications are the dominant traffic contributor in current Internet Backbone of China. In addition, we selectively present the traffic pattern of different applications in detail.

基于运动学片段的纯电动出租车行驶特征模式挖掘

在逐步推行出租车全面电动化的背景下,针对目前对纯电动出租车行驶状态评估的不足,建立一种基于运动学片段的纯电动出租车行驶特征模式挖掘方法,研究纯电动出租车行驶状态特征.首先,基于行驶轨迹GPS数据,从速度特征、加减速和行驶状态3个方面,确定超速比例、加减速频率、行驶速度、怠速时间占比等13个特征指标刻画运动学片段,建立纯电动出租车运动学片段提取方法,研究纯电动出租车行驶状态特征.然后,根据行驶特征指标主成分的特征值大小及累积贡献率,确定关键特征指标,结合K-均值聚类算法,生成多时空场景下的纯电动出租车行驶特征模式,综合评价车辆行驶状态.最后,以深圳市共计9天采样间隔为1 s的700万条纯电动出租车GPS行驶轨迹数据为驱动,提取了1 757条纯电动出租车运动学片段.根据安全性、效率性和舒适性8个关键特征指标进行聚类分析,生成包含主干路、次干路和支路在早高峰、平峰和晚高峰9种时空场景下27类纯电动汽车行驶状态的特征模式库.研究结果表明:综合安全性、效率性、舒适性3方面,早高峰期间的纯电动出租车综合行驶状态优于平峰和晚高峰时段;基于运动学片段、主成分分析及多时空场景聚类分析的纯电动出租车行驶特征模式挖掘方法,能够有效反映并评估纯电动出租车行驶状态,并向驾驶员提供合理的驾驶建议.

基于心率变异性的飞行员脑力负荷特征

为了探索五边飞行不同阶段飞行员脑力负荷水平,寻找表征脑力负荷的心电指标,对飞行学员展开五边飞行任务的六自由度模拟飞行实验,测量飞行过程中的脑力负荷水平,采集不同飞行阶段飞行员心电各指标数据,利用因子分析构建飞行员脑力负荷模型,分析和验证不同飞行阶段飞行员脑力负荷的变化特征。结果表明:相比心率指标,心率变异性指标在测量飞行员脑力负荷时具有更高的信度和效度;起飞和上升阶段的脑力负荷处于较低水平并呈下降趋势;巡航阶段的脑力负荷水平较低且变化趋势平滑;进近着陆阶段的脑力负荷水平最高。此外,突发事件会显著影响脑力负荷,引起飞行员脑力负荷的显著增加。

甘肃省铁路网高质量发展规划研究

为推动甘肃省铁路网现代化建设和综合交通高质量发展,立足新发展格局下甘肃省铁路发展现状和形势要求,对甘肃省铁路网规划布局进行系统研究。首先,对目前甘肃省铁路网布局结构、路网规模水平、运输效率、运输能力适应性等进行研究,剖析甘肃省铁路网发展存在的瓶颈弱项;其次,基于甘肃省铁路客货运输需求,以问题破解、需求支撑、发展引领为导向,提出甘肃省铁路网发展思路、发展目标和布局方案。研究表明,甘肃省将形成“一心两廊多放射”的铁路网格局,构建“1259小时交通圈”,实现区域内部市市通高速铁路、区域对外多径路快速通达、货运体系保障有力。

GIS支持下的城市三维建筑格局对道路交通噪声的影响

道路交通噪声具有极强的时空特征。该文基于地理信息角度,选取西南城市某复杂环线道路为研究对象,构建环线道路交通噪声地理信息系统(GIS)预测系统,通过相关性分析和构建地理加权回归模型探究城市三维建筑格局与道路交通噪声污染的关系。结果表明:环线道路交通噪声GIS预测结果符合正态分布,预测值与实测值之间不存在显著性差异,预测误差可控。街区网格最小研究单元内,平均GIS预测值(Ave_GIS)解释变量为建筑物平均周长(Ave_C)、建筑基底面积与总用地面积的比值(BCR),平均投诉密度(Ave_TSMD)解释变量为高度>24 m的建筑物所占的比例(HBR)、高度>24 m的建筑物数量总和(NHB)和所有建筑中最大高度与最小高度差(LHR)。地理加权回归拟合效果优于普通最小二乘法,不同三维建筑格局要素对Ave_GIS和Ave_TSMD两种噪声污染指数空间影响分布均存在不均衡性,相关影响作用BCR>Ave_C;NHB>LHR>HBR。在分析影响程度及原因后对城市道路交通噪声防治提出相关建议以降低噪声污染影响,提高人居环境质量。

大型站房临近既有线路施工智慧监测监控技术

新建厦门北站项目在5、6号新建地铁出入口施工的同时,需确保既有地铁1号线及4号线结构安全和正常运营,对施工技术和管理层面提出了更高的要求。为解决上述问题,采用智慧监测监控技术,能够实现既有线路的结构位移、隧道结构收敛等自动化监测,从而实现现场监测数据的实时传输和处理。经过项目的实践验证,该技术能够为相关方及时提供准确可靠的监测数据和预警信息,对于保障既有线路安全性和稳定性具有重要意义。

A Fast Multi-Pattern Matching Algorithm for Mining Big Network Data

The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource allocation and provide customized services to users. The first step of analyzing user behaviors is to extract information of user actions from HTTP traffic data by multi-pattern URL matching. However, the efficiency is a huge problem when performing this work on massive network traffic data. To solve this problem, we propose a novel and accurate algorithm named Multi-Pattern Parallel Matching(MPPM) that takes advantage of HashMap in data searching for extracting user behaviors from big network data more effectively. Extensive experiments based on real-world traffic data prove the ability of MPPM algorithm to deal with massive HTTP traffic with better performance on accuracy, concurrency and efficiency. We expect the proposed algorithm and it parallelized implementation would be a solid base to build a high-performance analysis engine of user behavior based on massive HTTP traffic data processing.

Predicting the Geographic Traffic Distribution in Cellular Networks

Early detection and rapid resolution network congestion can considerably improve network capacity. Consequently, much research has been carried out on predicting traff ic patterns in 3G networks. This paper introduces an access point centric approach that is implemented by two prediction models, the traffic abstraction model and the order-k Markov model. Traffi c predictions are carried out to support the congestion control in the semi-smart antenna systems. The simulation result shows that the cumulative error rate is below 25% even carrying out multi-step-ahead predictions.

珠三角城际陆地公共客运交通联系格局和子结构特征分析

探讨珠三角城际陆地公共客运交通联系总体特征和内部结构,对于提升珠三角交通网络韧性,合理规划都市圈,实现可持续协调发展具有重要意义。利用城际陆地公共客运交通数据,结合联系指标和社会网络分析方法,对珠三角内各城市之间联系的空间格局和结构特征进行分析。结果表明:(1)客流走向发生改变,但是首位联系和高联系强度的城市基本不变。不同年份的客流走向稍有变化,但是以广州、深圳为中心的入射首位联系格局基本不变。对外联系强度最高的城市集中在广州、深圳、东莞,与城市的综合经济实力基本相符。(2)珠三角城市联系均呈现出核心-边缘的内外圈层联系结构。广州、深圳、东莞的核心地位始终保持不变,边缘区域内部的城市交通联系得到加强。(3)东部深圳东莞惠州结构稳固,符合都市圈规划,其他子群结构发生改变。中部的广州佛山肇庆都市圈规划与凝聚子群运行结果相差较大,需加强这三个城市间的客运联系,使之能真正形成一个强联系的子群。除广州外的3个子群间相对独立,客运联系较少。

Travel time prediction with viscoelastic traffic model

Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.

PerfMon: Measuring Application-Level Performance in a Large-Scale Campus Wireless Network

WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.

A Statistical Framework for Real-Time Traffic Accident Recognition

Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.