Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

电动汽车电传动试验系统

介绍了电动汽车电传动试验系统,着重分析了其测功机的系统校正方法。试验系统对电机的控制参数进行了调试和验证,对控制系统的稳定性进行了模拟和验证,为系统效率、系统温升等测试和验证提供了试验数据。     

铁道车辆轮对圆度误差测量方法研究

给出了一种在车测量铁道车辆轮对圆度误差的测量系统和测量方法,同时介绍了系统的标定公式、标定公式检验方法和检验结果。检验结果表明,该测量系统可以高精度地连续测量轮对圆度误差。     

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介绍了重力模型的各种不同形式的标定和检验方法，标定方法有最小二乘法、试算法及其该进方法，检验方法包括t检验法和相对差的检验。通过具体的算例，比较分析了它们不同的计算过程和结果，从而得出各类重力模型的标定和检验方法的特点。据此得出在我国的现有情况下的适用性。     

高速公路监控摄像机太阳能供电方案设计研究

针对高速公路监控摄像机远程供电困难的问题,结合环保、节能的原则,设计了监控摄像机太阳能供电系统,详细介绍太阳能供电设计方案,并进行了实例计算,最后对本文进行了总结。     

Development of a Calibration and Monitoring System for GD-1 High Pressure Common Rail Diesel Engine

IntroductionEngine performance and exhaustemissions arecontrolled by a great number of control parameters( constants,curves and maps) in the ECU relatedto injection system,combustion chamber geome-try,boost pressure and EGR percentage. As iswell known,all these parameters are required tobe changed and calibrated again and again,so thecalibration workload is very big.And many itemsof exhaust emissions,such as good NOX and sootengine- out level predictions,are strictly linked notonly to the …     

基于视频的城市快速路车辆异常行为检测

文章提出虚拟传感器的概念,针对城市快速路视频监控系统,改革单一功能摄像机,使其成为具人工智能的新型视频传感器,完成道路车辆跟踪及异常行为检测。异常检测算法运用带有时间和空间信息的车辆轨迹对自组织神经网络进行训练,获得神经网络参数后利用概率模型对实时车辆轨迹进行异常提取。该文所提算法能在嵌入式DM642视频处理平台上有效运行,能够提取诸如超低(高)速行驶、违章停车、违规掉头等异常行为,具有低运算量及较好的鲁棒性。     

视频监控系统摄像机技术发展趋势研究

郑州地铁5号线、市民文化服务区地下交通市政工程及后续线路单站摄像机数量均在90路以上,部分站点甚至超过110路。摄像机数量的增加为前期工程建设及后期运营维护带来了诸多挑战,如何在满足安防需求的前提下简化摄像机点位布放数量,提升运维效率成为一大课题。目前市场上全景摄像机种类较多,各摄像机厂家提供的产品性能参数、适用场景也不尽相同,本研究探讨视频监控系统现状及全景摄像机的应用。     

基于高分辨率摄像机的机动车违法检测系统

在目前应用的机动车视频违法检测系统中,广泛采用的是多摄像机分别拍摄违法行为全景及违法车辆车牌特写的技术方案。针对其存在证据性弱、系统调试复杂等缺点,设计并实现了一种基于高分辨率摄像机的视频违法检测系统,重点阐述了系统设计方案、软件结构、实现方法和实验结果。     

舰船 RCS 测试内外结合定标方法

针对舰船 RCS 测试的定标问题，讨论从接收机前端注入标准微波信号或从发射机耦合发射功率输入接收机前端的2种内定标方法，作为修正外场定标的依据。根据雷达方程分析外场定标 RCS 测量设备、标准体加工、目标跟踪定位、测试区域环境等主要误差因素。讨论发射功率、系统非线性、系统稳定性、极化损耗等引入的误差，提出外场定标对标准体几何尺寸、表面光洁度以及所需信噪比等要求。通过内定标与外定标相结合的方法，解决由于定标与测试间隔时间过长接收机状态出现漂移导致测试误差的问题，保障舰船 RCS 长时间测试的精度和稳定度。