Efficient multiple model particle filtering for joint traffic state estimation and incident detection

This article proposes an efficient multiple model particle filter (EMMPF) to solve the problems of traffic state estimation and incident detection, which requires significantly less computation time compared to existing multiple model nonlinear filters. To incorporate the on ramps and off ramps on the highway, junction solvers for a traffic flow model with incident dynamics are developed. The effectiveness of the proposed EMMPF is assessed using a benchmark hybrid state estimation problem, and using synthetic traffic data generated by a micro-simulation software. Then, the traffic estimation framework is implemented using field data collected on Interstate 880 in California. The results show the EMMPF is capable of estimating the traffic state and detecting incidents and requires an order of magnitude less computation time compared to existing algorithms, especially when the hybrid system has a large number of rare models.     

主动声纳的低截获性数字仿真研究

在现代海战中,舰艇面临着越来越严峻的敌方侦察声纳的威胁.低截获是一项非常诱人的技术.低截获声纳通过选择合适的发射信号,在保持探测能力的同时,降低被敌方侦察声纳截获的概率.采用数字仿真的形式,分析浅海侦察声纳信号能量的变化,研究主动声纳的低截获性.仿真结果表明在高斯噪声背景下,主动声纳实现低截获性相当困难.     

交通运输超限治理对策研究

由于交通运输量的不断增大以及运输行业的恶性竞争等原因，导致货车超载现象越来越普遍，并已成为急需解决的问题。探讨交通运输超限治理的对策，对于完善科学而系统的超限管理体系具有一定的借鉴意义。     

汽车自动变速器检测台的开发研究

介绍了一种可对汽车自动变速器性能进行全面测试的检测台，论述了其结构组成、功能特点和推广应用价值。     

城市轨道交通车载列车自动防护设备仿真检测平台研究

仿真测试是开展城市轨道交通信号系统相关技术研究的一种有效方法。利用商业虚拟仪器开发平台LabVIEW,讨论了车载ATP(列车自动防护)设备仿真检测系统研制的技术和方法。实践证明,该车载ATP设备仿真检测平台具有高效、灵活、低成本的特点。     

基于磁链关系函数判断无刷电机转子位置的方法

为了扩展无位置传感器无刷直流电动机（BLDCM）间接位置检测方案的适用速度范围．在电机采用三相六拍两两导通的条件下，基于对磁链相关函数的分析，提出了一种简化的磁链关系函数法。根据电机的数学模型和磁链关系函数，对电机本体和检测算法建模仿真。仿真结果表明该算法能在低、高速时准确检测BLDCM的换相位置。     

A new methodology for incident detection and characterization on surface streets

In this paper, a new methodology is presented for real-time detection and characterization of incidents on surface streets. The proposed automatic incident detection approach is capable of detecting incidents promptly as well as characterizing incidents in terms of time-varying lane-changing fractions and queue lengths in blocked lanes, lanes blocked due to incidents, and incident duration. The architecture of the proposed incident detection approach consists of three sequential procedures: (1) Symptom Identification for identification of incident symptoms, (2) Signal Processing for real-time prediction of incident-related lane traffic characteristics and (3) Pattern Recognition for incident recognition. Lane traffic counts and occupancy are the only two major types of input data, which can be readily collected from point detectors. The primary techniques utilized in this paper include: (1) a discrete-time, nonlinear, stochastic system with boundary constraints to predict real-time lane-changing fractions and queue lengths and (2) a pattern-recognition-based algorithm employing modified sequential probability ratio tests (MSPRT) to detect incidents. Off-line tests based on simulated as well as video-based real data were conducted to assess the performance of the proposed algorithm. The test results have indicated the feasibility of achieving real-time incident detection using the proposed methodology.     

公路隧道照明精确检测与智能控制系统研究

为解决公路隧道照明系统节能与行车安全、运营管理之间的矛盾，采用理论分析及实际检验的方法，分析了隧道照明控制系统的调光原理、照明控制系统结构以及亮度检测误差产生的原因，设计了一种可调量程的亮度检测仪并给出校准及亮度检测步骤，解决了公路隧道亮度精确检测的难题，采用模糊神经网络算法在照明系统中进行了实际应用，实践证明本算法在亮度满足实际运营需求的前提下可取得良好的节能效果。     

基于小波变换的传感器故障检测技术研究

介绍了小波变换的基本理论及小波技术用于传感器故障检测的基本原理，给出了传感器故障检测方法，仿真实例证明了这一技术的有效性。     

Evaluation of detection sensitivity and count performance of advanced vehicle detection technologies at a signalized intersection

This article evaluates the stop-bar detection and count performance of three advanced vehicle detection sensors under various environmental conditions at a signalized intersection. Continuing advancements to vehicle detection technologies and improvements to their detection capabilities to overcome issues from impacting conditions necessitates testing the performance of new and upgraded sensor products to evaluate their performance and identify the most suitable products for various climates and weather conditions. The three evaluated sensors were Autoscope Encore video, Iteris Vantage Edge 2 video, and Wavetronix SmartSensor Matrix microwave sensors. The three sensors performed with high detection sensitivity during ideal environmental conditions with up to 99.9% detection accuracy levels and are suitable for traffic monitoring centers that rely on remote access to the monitored sites and the collected data. However, they were affected by some extreme adverse weather conditions, mainly daytime and nighttime snow, daytime fog, dawn lighting, and strong winds (for high mounted devices). The selection of a sensor product will depend on the type of application and the priority given to the type of traffic data being collected. Overall, the Iteris video sensor performed with the highest detection sensitivity levels, with the Wavetronix Matrix microwave sensor performing similarly under most conditions (14 of 19 evaluated conditions). Autoscope video provided the highest count accuracies and also provides a much broader data collection capability. The results of this study will help transportation agencies in selecting suitable vehicle detection sensor technologies for future installations within their jurisdiction and improved data collection.