A BP neural network based information fusion method for urban traffic speed estimation

Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an very important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings and tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, we developed a novel method employing a back propagation (BP) neural network to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, we can use the speed of its related road sections to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the speed of this road section estimated by our method is better.     

考虑多节点拥堵的城市道路网级联失效仿真

为量化城市路网在多个节点突发拥堵时出现的级联失效现象,解决网络重要节点拥堵而引起的连锁反应问题,本文构建了双层网络配流的网级联失效模型,采用计算路网拥堵度完成该情景下的级联失效仿真研究的方法,对级联失效现象进行分析。首先采用原始法构建路网的几何拓扑图,依次从拓扑图中删除拥堵节点,然后利用连通度、最大连通子图相对大小与圈数率3个鲁棒性评价指标衡量路网通行效率,计算出通行效率不低于10%条件下的最大节点失效比例,再运用双层网络配流模型测算级联失效时的路网拥堵度,最后通过Transcad 4.5和Matlab 2012a完成对包含54个节点、92条路段的成都市某地中等规模路网的仿真实验。实验结果表明：路网在最不利条件下的最大节点失效比例为19.3%;相比于单个拥堵节点,2个以上节点同时拥堵更易造成路网出现级联失效现象;当拥堵节点数目超过7个后,拥堵度逐渐趋于稳定并使路网到达其鲁棒性的极限。该研究旨在为多节点失效条件下的路网管控措施提供仿真技术参考。     

Improve urban passenger transport management by rationally forecasting traffic congestion probability

A Bayesian network (BN) approach is proposed in this study to analyse the overall traffic congestion probability of an urban road network in consideration of the influence of applying various transport policies. The continually expanding urbanised region of Beijing has been chosen as the study area because of its rapid expansion and motorisation, which lead to the severe traffic congestion occurring nearly every day. It is demonstrated that the proposed BN approach is able to rationally predict the probability of the overall traffic congestion that will take place given a certain transport policy. It is also proven that increasing the number of buses providing convenient passenger transport service in the urbanised region of Beijing will most effectively reduce the probability of the traffic congestion in this area, especially when the newly constructed roads in the same region are put into use.     

Attacker-defender models and road network vulnerability

The reliability of road networks depends directly on their vulnerability to disruptive incidents, ranging in severity from minor disruptions to terrorist attacks. This paper presents a game theoretic approach to the analysis of road network vulnerability. The approach posits predefined disruption, attack or failure scenarios and then considers how to use the road network so as to minimize the maximum expected loss in the event of one of these scenarios coming to fruition. A mixed route strategy is adopted, meaning that the use of the road network is determined by the worst scenario probabilities. This is equivalent to risk-averse route choice. A solution algorithm suitable for use with standard traffic assignment software is presented, thereby enabling the use of electronic road navigation networks. A variant of this algorithm suitable for risk-averse assignment is developed. A numerical example relating to the central London road network is presented. The results highlight points of vulnerability in the road network. Applications of this form of network vulnerability analysis together with improved solution methods are discussed.     

城市交通规划的声环境影响评价

介绍了依据城市测绘图、交通规划基础资料和交通噪声预测模式进行噪声预测与评价的技术方法。其方法通过对城区等声级线图与声环境功能区划图、声环境敏感点分布、城市人口密度分布等图层进行叠置分析，给出噪声超标区数量、面积、不同声级下受影响的人群等评价结论，为交通规划的优化及环境影响评价提供技术支持。     

Perceived susceptibility to and perceived causes of road traffic injuries in an urban and rural area of Tanzania

The aim of the study was to investigate social and behavioral correlates of perceived vulnerability to traffic injuries in an urban and rural setting in Tanzania. In 2002, a sample of 494 adults aged 15 years and above participated in household interviews in Dar es Salaam (urban) and Hai District (rural). The study was part of a population-based survey that collected self-report data on non-fatal injuries. In Dar es Salaam 75 and 82% of males and females, respectively, perceived it as likely that they would experience a traffic injury in general. The corresponding figures in Hai were 63 and 64%. Men rated their road traffic vulnerability similarly to women (OR=0.8, 95% CI 0.5-1.3). Factors associated with high perceived vulnerability as a pedestrian or being injured by a bicycle were amount of road safety information received from health workers and friends, having caused a car to swerve and having crossed a road while talking. Respondents perceived driver recklessness and driver drunkenness as the leading causes of traffic injuries in both areas. Differences were found between the urban and rural setting with respect to perceived risk for traffic injury. The implications of these findings in the context of traffic injury prevention are discussed.     

一种道路交通安全神经网络评价模型的构建

在综合分析国内外交通安全评价研究的基础上,针对中国道路交通安全现状,提出了基于一个城市行政分区的道路交通安全评价指标和评价模型,并针对我国城市道路交通安全的现状及存在问题,提出了采用基础数据、交通安全管理、公众安全评价3个方面共17项指标组成的面向城市道路交通安全可持续发展的评价指标集;构建了基于BP神经网络的评价模型;并结合算例详细分析了其计算方法。     

Development of degradation model for urban asphalt pavement

There is a lack of a profound understanding of urban pavement deterioration pattern. This is due to the complexity of traffic conditions and the variety of pavement structures in urban roads. The lack of a suitable deterioration model for the urban pavements limits the possibility of making any scientific and cost-effective repair and maintenance strategy. There is a need for a better understanding of the long-term behaviour of urban pavements by which predictive pavement condition models can be derived and consequently a suitable maintenance management system can be built. In response to this need, a comprehensive field study was performed in three Iranian cosmopolitan cities. Pervasive pavement damages were defined and an urban pavement condition index was established. A deterioration model was developed by monitoring and analysing the conditions of road pavements in a period of four years. This model varies as the structural and loading conditions of the pavement change. The efficiency and practicability of the model in predicting the conditions of the pavements were illustrated.     

Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Recently, the importance of vehicle safety supporting system has been highlighted as autonomous driving and platooning has attracted the researchers. To ensure driving safety, each vehicle must broadcast a basic safety message (BSM) every 100 ms. However, stable BSM exchange is difficult because of the changing environment and limited bandwidth of vehicular wireless communication. The increasing number of vehicles on the road increases the competition to access wireless networks for BSM exchange; this increases the packet collision rate. An increased packet collision rate impairs the transmission and reception of BSM information, which can easily cause a traffic accident. We propose a solution, the vehicular safety support system (V3S), which exchanges BSMs reliably even when many vehicles are on the road. The V3S uses a clustering scheme to decrease network traffic by reducing the amount of data exchanged between a vehicle and the roadside unit (RSU). In addition, the V3S reduces the collision rate of wireless network packets by broadcasting the vehicle's BSM in an allocated timeslot using the time division multiple access (TDMA) MAC protocol. The V3S also deals with insufficient bandwidth for dedicated short-range communications (DSRC) by changing DSRC channels according to traffic flow. In evaluating the packet error rate for stable BSM packet delivery, the V3S demonstrates an excellent packet error rate of less than 1%, compared to the 802.11p with its packet error rate of 82%.     

基于简化IDA的结构地震损伤评估方法

将等效单自由度（SDOF）体系假设与Pushover分析方法相结合,建议了一种简化的增量动力分析（IDA）方法。给出损伤指数R与结构反应限值和抗震三水准的对应关系。根据结构抗震失效概率公式给出结构地震损伤概率计算方法,进一步得到结构在不同地震强度下的损伤指数期望值R_e,用以评估结构的地震损伤等级。在此基础上建立了基于简化IDA的结构地震损伤评估方法。为了验证所提出的方法,按照不同场地设计了三幢结构模型,选取了典型地震动记录作为结构的水平地震动激励,对结构模型进行了IDA、地震易损性分析和地震损伤评估。通过算例分析表明,基于简化IDA的结构地震损伤评估方法计算量小,易于操作,但得到的结构损伤值较大,抗震评估结果偏于安全。     

锈蚀钢框架地震易损性评定方法

钢结构锈蚀降低了钢材塑性变形能力,降低了断裂韧性,势必影响锈蚀钢结构的抗震性能。该文借助于加速腐蚀试验,研究了钢材力学性能随锈蚀程度的变化规律,建立了钢材强度、延性指标与锈蚀程度之间的关系;研究了地震作用下钢结构损伤状态及性能水平与锈蚀程度之间的关系,并基于极限破坏状态提出了锈蚀钢框架层间位移角限值取值方法。以5层钢框架为例,运用静力弹塑性分析方法,探讨了锈蚀对钢框架构抗震性能的影响;运用增量动力分析法,建立结构损伤指标与地震动强度指标的关系,得到不同龄期锈蚀钢框架地震易损性曲线,提出了锈蚀钢框架地震易损性评定方法。研究结果表明:同龄期不同极限状态结构超越极限状态的概率不同,倒塌极限状态下差距较大,而正常使用极限状态差距较小,若不修正层间位移角限值会高估锈蚀钢框架抗震性能。     

Pedestrian crash trends and potential countermeasures from around the world

As automobile transportation continues to increase around the world, bicyclists, pedestrians, and motorcyclists, also known as vulnerable road users (VRUs), will become more susceptible to traffic crashes, especially in countries where traffic laws are poorly enforced. Many countries, however, are employing innovative strategies to ensure that road users can more safely navigate the urban landscape. While bicyclists and motorcyclists are important road users, this paper will focus on pedestrian crash problems and solutions. Pedestrians are most at risk in urban areas due in part to the large amount of pedestrian and vehicle activity in urban areas. With this in mind, designing safe, accessible, and comprehensive facilities for pedestrians is vital to reducing pedestrian crashes. This paper will provide some insight into the magnitude of the pedestrian crash problem around the world, and will offer some lessons learned from several countries, particularly in Europe and the U.S., for improving pedestrian safety. Beginning with pedestrian safety statistics at the global, regional, and national levels, this paper will address potential countermeasures and strategies for improving pedestrian safety from an international perspective.     

Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices

The Internet of Things (IoT) is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems. These networks of wireless sensors monitor the physical environment and report the collected data to the base station, allowing for smarter decisions. Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane. However, in some application areas, such as various surveillances, underwater monitoring systems, and various environmental monitoring applications, wireless sensors are deployed in a three-dimensional plane. Recently, localization-based applications have emerged as one of the most promising services related to IoT. In this paper, we propose a novel distributed range-free algorithm for node localization in wireless sensor networks. The proposed three-dimensional hop localization algorithm is based on the distance error correction factor. In this algorithm, the error decreases with the localization process. The distance correction factor is used at various stages of the localization process, which ultimately mitigates the error. We simulated the proposed algorithm using MATLAB and verified the accuracy of the algorithm. The simulation results are compared with some of the well-known existing algorithms in the literature. The results show that the proposed three-dimensional error-correction-based algorithm performs better than existing algorithms.     

城市建筑群概率地震灾害风险评估研究

科学合理地考虑地震风险评估中各环节的不确定性是地震风险评估结果可靠的基础。该文利用某城市现有工程场地地震安全性评价数据,综合考虑城市地震危险性评估中的不确定性,以结构基本类作为城市建筑群模型,并考虑地震动不确定性对建筑群地震易损性的影响,建立了同时考虑地震危险性和地震易损性不确定性的地震风险评估模型,最终给出城市结构基本类不同极限破坏状态的年平均超越概率和50年内地震风险概率。基于结构不同极限破坏状态对应的损失比,获得每一种结构基本类的地震经济损失风险曲线。在此基础上,提出了采用地震风险一致概率为控制点,得到城市建筑群总的地震经济损失风险曲线组合的方法,该方法可为评估城市建筑群概率地震风险提供参考。     

Epidemic algorithms for reliable and efficient information dissemination in vehicular

Vehicular ad hoc networks (VANET), which are created by vehicles equipped with short- and mediumrange wireless communication, have an array of important applications in intelligent transport systems. Many of these applications require reliable, bandwidth-efficient dissemination of traffic and road information via ad hoc network technology. This is a difficult task since inter-vehicular networks often lack continuous end-to-end connectivity and are characterised by large variations in node density. A new epidemic algorithm for information dissemination in highly dynamic and intermittently connected VANET is introduced. It is shown through realistic simulations in highway traffic that the proposed algorithm is capable of reliable and efficient information dissemination in VANET in the face of frequent network fragmentation and large density variations.     

Comprehensive Information Security Evaluation Model Based on Multi-Level Decomposition Feedback for IoT

The development of the Internet of Things (IoT) calls for a comprehensive information security evaluation framework to quantitatively measure the safety score and risk (S&R) value of the network urgently. In this paper, we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback. The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection. Firstly, we establish an overall evaluation indicator system that includes four primary indicators of threat information, asset, vulnerability, and management, respectively. It also includes eleven secondary indicators of system protection rate, attack detection rate, confidentiality, availability, controllability, identifiability, number of vulnerabilities, vulnerability hazard level, staff organization, enterprise grading and service continuity, respectively. Then, we build the core algorithm to enable the evaluation model, wherein a novel weighting technique is developed and a quantitative method is proposed to measure the S&R value. Moreover, in order to better supervise the performance of the proposed evaluation model, we present four novel indicators includes residual risk, continuous conformity of residual risk, head-to-tail consistency and decrease ratio, respectively. Simulation results show the advantages of the proposed model in the evaluation of information security for IoT.     

Ex-ante assessment of the safety effects of intelligent transport systems

There is a need to develop a comprehensive framework for the safety assessment of Intelligent Transport Systems (ITS). This framework should: (1) cover all three dimensions of road safety—exposure, crash risk and consequence, (2) cover, in addition to the engineering effect, also the effects due to behavioural adaptation and (3) be compatible with the other aspects of state of the art road safety theories. A framework based on nine ITS safety mechanisms is proposed and discussed with regard to the requirements set to the framework. In order to illustrate the application of the framework in practice, the paper presents a method based on the framework and the results from applying that method for twelve intelligent vehicle systems in Europe. The framework is also compared to two recent frameworks applied in the safety assessment of intelligent vehicle safety systems.     

Simulation of safety: A review of the state of the art in road safety simulation modelling

Recent decades have seen considerable growth in computer capabilities, data collection technology and communication mediums. This growth has had considerable impact on our ability to replicate driver behaviour and understand the processes involved in failures in the traffic system. From time to time it is necessary to assess the level of development as a basis of determining how far we have come. This paper sets out to assess the state of the art in the use of computer models to simulate and assess the level of safety in existing and future traffic systems. It reviews developments in the area of road safety simulation models. In particular, it reviews computer models of driver and vehicle behaviour within a road context. It focuses on stochastic numerical models of traffic behaviour and how reliable these are in estimating levels of safety on the traffic network. Models of this type are commonly used in the assessment of traffic systems for capacity, delay and general performance. Adding safety to this assessment regime may allow more comprehensive assessment of future traffic systems. To date the models have focused primarily on vehicular traffic that is, cars and heavy vehicles. It has been shown that these models have potential in measuring the level of conflict on parts of the network and the measure of conflict correlated well with crash statistics. Interest in the prediction of crashes and crash severity is growing and new models are focusing on the continuum of general traffic conditions, conflict, severe conflict, crash and severe crashes. The paper also explores the general data types used to develop, calibrate and validate these models. Recent technological development in in-vehicle data collection, driver simulators and machine learning offers considerable potential for improving the behavioural base, rigour and application of road safety simulation models. The paper closes with some indication of areas of future development.     

考虑路网交通流状态的单变量交通噪声预测

为实现路网区域交通噪声预测,克服传统预测模型中路段间交通特性相互独立以及路段内流量与速度相互独立的缺陷,借助Van Aerde交通流模型,在不同道路等级、设计速度约束下,结合道路线声源噪声排放,构建基于速度的单变量交通噪声预测模型.分别对比4种常见城市道路的交通噪声实测值,模型预测值平均偏差为1.63 dB,满足精度需...     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.