Extended MULE Concept for Traffic Congestion Monitoring

Wireless Sensor Networks are being recently studied to monitor real-time traffic conditions on roads and highways. Idea of using vehicles to convey information from sensors placed alongside roads to the dedicated base stations has also been under scrutiny for some time. In this paper, we argue that a sensor placed on a vehicle instead of a fixed location can effectively sense traffic congestion on the road and report it to the already available WLAN Access Points (APs) instead of the dedicated base stations. This way, instead of deploying series of base stations to collect traffic information, congestion information can be sent over the ISM links between the vehicular sensor nodes and the WLAN APs. This paper investigates, as we call it, the Extended MULE concept by using actual experimental data obtained from the test drives across the city. Our results show that adopting this idea is effective in reporting traffic congestion on the roads.     

Road traffic and geography topology based opportunistic routing for VANETs

The message delivery .ratio and transmission delay is affected deeply by road traffic flow in vehicular ad hoc networks （VANETs）. An opportunistic routing based on geography and road traffic flow for VANETs （ORRIS） was proposed. ORRIS leverages the knowledge of geography positions, motion vectors and road traffic flows. In order to estimate the traffic flow density, the history of encounter number of the vehicles in the opposite direction is considered in ORRIS. The forwarding decisions are made by distributed vehicles based on the geography topology and the road traffic flow. The real map based simulation results show that ORRIS has a better performance than other algorithms, especially when the road traffic is busy or the traffic flow rates have great differences between roads.     

GeoVisual analytics for understanding the distribution of speeding patterns on arterial roads: assessing the traffic safety of vulnerable road users

ABSTRACT

Arterial roads have operational significance and play a substantial role in the mobility of modern society. They make up the majority of road network in urban and rural areas and allow high-speed movement despite traffic-controlling elements. In densely populated areas where the presence of Vulnerable Road Users (VRUs) is high, high-speed movement is problematic, and speed calming measures are needed to improve traffic safety, since many VRUs do crossroads, regardless of the road network regulations. These aspects have been researched in the traffic domain in a small scale, and not much has been investigated from a visualisation perspective. To provide comprehensive insights on the movement characteristics of arterial roads, we propose a GeoVisual Analytics (GVA) approach. GVA techniques are suitable solutions to display and extract knowledge from large amounts of Floating Car Data (FCD) collected through on-board devices of vehicles. By cross-sector collaboration between cartographic and traffic experts, five arterial road segments in Aalborg City were selected to answer where and when in particular VRUs do cross streets by ignoring traffic rules. Based on clusters of large unexplainable deviations from driving speed in FCD, the results uncovered meaningful patterns from complex traffic movements. They also allowed for the provision of some recommendations that are critical for traffic safety in urban areas.     

Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic

To alleviate the traffic pressure on roads,reduce the appearance of road congestion,and avoid the occurrence of traffic accidents,a privacy-preserving intelligent monitoring (PPIM) scheme based on intelligent traffic was proposed in combination with the safe and k-nearest neighbor (KNN) algorithm.To ensure the security of traffic data,the data content was randomly divided into independent parts via the secure multi-party computing strategy,and the data components were stored and encrypted separately by non-colluding multi-servers.To improve the accuracy of road condition monitoring,an improved KNN traffic monitoring algorithm was proposed.By virtue of the similarity calculation of data,the correlation value to measure the degree of traffic condition relationship between roads was obtained.And it was integrated with the KNN as the weight coefficient.To speed up the processing of dense data,a series of data security computing protocols were designed,and the data security processing was realized.In addition,real traffic data were used to verify the algorithm.The results show that the improved KNN algorithm is helpful to improve the accuracy of traffic monitoring.The analysis shows that the algorithm can not only guarantee the safety of data but improve the accuracy of traffic monitoring.     

基于三维GIS的城市高架环线噪声地图研究

随着中国城市道路交通的迅速发展,城市环线交通以高架环线为主。现有的各类道路噪声预测模型都缺乏处理城市高架环线这类复杂的空间三维实体的能力。以美国联邦公路局的FHWA噪声预测模型为基础,通过车辆等效计算、高架环线多层多车道修正、高架桥声屏障修正等措施,对FHWA噪声预测模型进行修正。并将修正后的噪声预测模型与三维GIS模型相结合,绘制出了城市高架环线噪声地图。最终通过与实测数据的对比,验证了所修正噪声模型的正确性。     

Multi-layer neural network algorithm for vehicle-to-everything communication in 5G networks

Internet of Vehicles (IoVs), the emerging trend of Internet of Things (IoTs), has undoubtedly become a promising trend to improve communication among vehicles on the roads. Vehicle-to-everything (V2X) communication that is based on 5G technology enables vehicle users to communicate and collaborate with each other to enhance road traffic efficiency and safety. Owing to the increased traffic load and restricted resources of existing network substructure, a channel that responds to the latency and reliability needs of V2X communication must be designed. Thereby, several intelligent spectrum allocation techniques have been proposed to improve the system's overall effectiveness. In this paper, we discuss the spectrum sharing issue of V2X communication in Device-to-Device (D2D)-based cellular networks. We propose a new multi-layer neural network (MLNN)-based Resource Allocation and sharing approach (MNNRA) for D2D-based V2X communications. According to the main advantages of MLNN, the proposed algorithm takes several profits by improving system performance while reducing computational complexity. Numerical analysis is presented to approve the effectiveness of our proposed solution in terms of network sum rate, packet reception ratio, resource utilization ratio, and time complexity.     

Performance Analysis with Traffic Accident for Cooperative Active Safety Driving in VANET/ITS

Recently, by using vehicle-to-vehicle and vehicle-to-infrastructure communications for VANET/ITS, the cooperative active safety driving (ASD) providing vehicular traffic information sharing among vehicles significantly prevents accidents. Clearly, the performance analysis of ASD becomes difficult because of high vehicle mobility, diverse road topologies, and high wireless interference. An inaccurate analysis of packet connectivity probability significantly affects and degrades the VANET/ITS performance. Especially, most of related studies seldom concern the impact factors of vehicular accidents for the performance analyses of VANET/ITS. Thus, this paper proposes a two-phase approach to model a distributed VANET/ITS network with considering accidents happening on roads and to analyze the connectivity probability. Phase 1 proposes a reliable packet routing and then analyzes an analytical model of packet connectivity. Moreover, the analysis is extended to the cases with and without exhibiting transportation accidents. In phase 2, by applying the analysis results of phase 1 to phase 2, an adaptive vehicle routing, namely adaptive vehicle routing (AVR), is proposed for accomplishing dynamic vehicular navigation, in which the cost of a road link is defined in terms of several critical factors: traffic density, vehicle velocity, road class, etc. Finally, the path with the least path cost is selected as the optimal vehicle routing path. Numerical results demonstrate that the analytical packet connectivity probability and packet delay are close to that of simulations. The yielded supreme features justify the analytical model. In evaluations, the proposed approach outperforms the compared approaches in packet connectivity probability, average travel time, average exhausted gasoline. However, the proposed approach may lead to a longer travel distance because it enables the navigated vehicle to avoid traversing via the roads with a higher traffic density.     

基于新型路网模型的路径寻优方法研究

 针对传统路网模型描述交通信息节点多、数据冗余及结构稳定性差的问题,本文从实际交通路网本身的特点和人类对路网的认识出发,构建以"道路"为基本元素的路网模型.该模型详细描述道路的空间属性和交通属性,并利用道路间的相交关系描述交通路网.在尝试减少模型数据量、简化模型结构的基础上,采用改进A^*算法进行路径搜索.实例表明,所提出的模型及路径搜索算法可以满足实际应用的需要,并且在存储开销和搜索算法效率上具有一定的优势.     

Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks

An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the same security concerns of legacy ad hoc networks. Unlike exiting works, we consider, in this paper, detection a common attack where nodes modify safety message or drop them. Unfortunately, detecting such a type of intrusion is a challenging problem since some packets may be lost or dropped in normal VANET due to congestion without malicious action. To mitigate these concerns, this paper presents a novel scheme for minimizing the invalidity ratio of VANET packets transmissions. In order to detect unusual traffic, the proposed scheme combines evidences from current as well as past behaviour to evaluate the trustworthiness of both data and nodes. A new intrusion detection scheme is accomplished through a four phases, namely, rule-based security filter, Dempster–Shafer adder, node’s history database, and Bayesian learner. The suspicion level of each incoming data is determined based on the extent of its deviation from data reported from trustworthy nodes. Dempster–Shafer’s theory is used to combine multiple evidences and Bayesian learner is adopted to classify each event in VANET into well-behaved or misbehaving event. The proposed solution is validated through extensive simulations. The results confirm that the fusion of different evidences has a significant positive impact on the performance of the security scheme compared to other counterparts.

     

Infrastructure‐based vehicular congestion detection scheme for V2I

With the increasing number of vehicles, traffic jam becomes one of the major problems of the fast‐growing world. Intelligent transportation system (ITS) communicates perilous warnings and information on forthcoming traffic jams to all vehicles within its coverage region. Real‐time traffic information is the prerequisite for ITS applications development. In this paper, on the basis of the vehicle‐to‐infrastructure (V2I) communication, a novel infrastructure‐based vehicular congestion detection (IVCD) scheme is proposed to support vehicular congestion detection and speed estimation. The proposed IVCD derives the safety time (time headway) between vehicles by using iterative content‐oriented communication (COC) contents. Meanwhile, the roadside sensor (RSS) provides an infrastructure framework to integrate macroscopic traffic properties into the estimation of both the traffic congestion and vehicle safety speed. The main responsibilities of RSS in IVCD are to preserve privacy, aggregate data, store information, broadcast routing table, estimate safety speed, detect traffic jam, and generate session ID (S‐ID) for vehicles. Monte Carlo simulations in four typical Chinese highway settings are presented to show the advantage of the proposed IVCD scheme over the existing Greenshield's and Greenberg's macroscopic congestion detection schemes in terms of the realized congestion detection performance. Real road traces generated by Simulation of Urban Mobility (SUMO) over NS‐3.29 are utilized to demonstrate that the proposed IVCD scheme is capable of effectively controlling congestion in both single and multilane roads in terms of density and speed health with previous schemes in this field.     

汽车流量对移动通信信号的影响研究

通过对一段城市道路上的汽车流量提取特征参数，进行一维建模，并以GSM基站发射的电磁波信号为入射波，利用矩量法对此一维散射情况建立矩阵方程，并进行数值计算、仿真，仿真结果显示了通信基站发射的电磁波在公路上的散射场随车流量变化而发生改变的规律，并分析了由此产生的对移动通信的影响。     

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。     

Fuzzy Logic Ticket Rate Predictor for Congestion Control in Vehicular Networks

Cooperative vehicular systems are currently being investigated to design innovative intelligent transportation systems (ITS) solutions for road traffic management and safety. This paper proposes a preventive congestion control mechanism applied at highway entrances and devised for ITS systems. Our mechanism integrates different types of vehicles and copes with vehicular traffic fluctuations due to an innovative fuzzy logic ticket rate predictor. The proposed mechanism efficiently detects road traffic congestion and provides valuable information for the vehicular admission control. When we apply an authentic enhanced mobility model, the results demonstrate the mechanism capability to accurately characterize road traffic congestion conditions, shape vehicular traffic and reduce travel time.     

基于光散射快速检测法的渭南市道路积尘研究

为掌握渭南市道路积尘分布特征及影响因素, 获取道路积尘本地化参数和道路扬尘的管控依据, 基于光散射 法利用道路积尘负荷车载式快速监测系统对渭南市城市道路积尘负荷进行了测定, 分析了不同类型道路的积尘负荷 特征, 并研究了车流量、车重及道路抑尘方式等与积尘负荷的关系。研究结果表明, 渭南市道路积尘负荷平均值为 1.13 g·m−2, 不同类型道路积尘负荷大小呈支路 (1.79 g·m−2) > 次干道 (1.22 g·m−2) > 省道 (1.08 g·m−2) > 主干道 (0.94 g·m−2) > 国道 (0.71 g·m−2) 的特征; 车流量和车速变化与道路积尘负荷值均呈负相关, 即车流量和车速越大, 积尘负荷 值越小; 此外, 洒水能有效降低道路积尘负荷值, 但保持时间较短, 因此建议改变重洒水弱清扫的作业模式, 重点关注 清扫除尘。     

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.     

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.     

SNMS: an intelligent transportation system network architecture based on WSN and P2P network

With the development of city road networks, the question of how to obtain information about the roads is becoming more and more important. In this article, sensor network with mobile station (SNMS), a novel two-tiered intelligent transportation system (ITS) network architecture based on wireless sensor network (WSN) and peer-to-peer (P2P) network, is proposed to provide significant traffic information about the road and thereby, assist travelers to take optimum decisions when they are driving. A detailed explanation with regard to the strategy of each level as well as the design of two main components in the network, sensor unit (SU) and mobile station (MS), is presented. Finally, a representative scenario is described to display the operation of the system.     

干线交通流量采集传感器布局研究

交通流量采集传感器在交通控制中起着极其重要的作用,它是交通控制系统中控制参数采集的主要工具,它为干线协调控制提供基础数据,是决定交通信号控制精度的主要部件。本文通过对干线交通的研究,研究了干线交通中交通流量采集传感器布局。首先,讨论了干线中测速对传感器在车道上布局的影响。其次,考虑传感器的采集可靠性,给出了干线不同车道上车流量之间的相似度估算公式,讨论车道上传感器利用率的情况。最后给出了干线交通流量采集传感器的优化布局的整体思路。     

Proposed Low-Power High-Speed Microring Resonator-Based Switching Technique for Dynamically Reconfigurable Optical Interconnects

The dynamic bandwidth re-allocation (DBR) technique balances traffic by re-allocating bandwidth from under-utilized links to over-utilized links in a network. In this letter, we propose a nonblocking, fast, low-power, and integratable optical switch that enables DBR. The basic building blocks of the proposed switch are silicon-on-insulator-based microring resonators. Analytical and numerical studies show that the active switch design gives similar performance in throughput and latency, while reducing cost (number of lasers) and area significantly when compared to implementation of DBR with only passive components. There is a slight increase in power (~0.4% for worst-case traffic pattern) using the active switch implementation.     

基于5G、物联网的新一代智能交通路口研究与设计

与4G通信传输技术相比,5G传输技术在终端密度、峰值速率、传输延时以及传输容量等多个方面取得突破性进展。这种优势有利于实现信息的高速传递,有助于构建出“人-车-路”智能化的通信系统,进而降低交通事故的风险率,构建出靓丽的城市交通风景线。对此,文章从新一代智能交通道路的发展以及5G的新一代智能交通道路的构成、功用、开发以及瓶颈五方面对此做了介绍。