基于浮动车数据的交通事件参数特性研究

利用微观交通仿真软件Vissim搭建交通事件环境,对在不同堵塞程度和不同交通流量的交通事件情况下,浮动车采集的浮动车速、行程时间、浮动车位置等移动检测交通参数变化特性进行了分析比较,总结出交通事件情况下的浮动车移动检测各交通参数变化规律,并以此为基础对模式识别法、统计预测法、时间序列和智能算法等4种基于浮动车数据的交通事件检测算法进行了探讨,具有覆盖面广、成本低等特点。     

神经网络于拥挤指针之研究

拥挤是国内外用来描述路况最通俗的代名词,主要在于简单易懂;目前以车速间距发布拥挤等级的方式常会发生与用路人主观之行车拥挤感知经验不符的现象。文中以路段固定侦测器之实时交通参数、CCTV信息画面,结合类神经网络理论,探究群体用路人于号志化干道上之拥挤感知。以台15线为例,进行主观拥挤指针之模式建构与评估。     

地球物理方法在高速公路路基质量检测中的应用

提出采用以探地雷达为主，瑞雷波法为辅的地球物理方法无损检测技术，结合钻探工程岩土试验和地质基础资料对公路路基工程质量进行定量化的多方法、多参数、多信息、快速有效的分析和评价。经试验研究，获得了良好的效果。     

高速光子探头轴温探测器

高速光子探头轴温探测器采用HgCdTe(碲镉汞)晶体材料的低阻光导型器件作为探测器的敏感元件,选用锗(Ge)材料制作透镜,并在光学透镜表面镀增透膜,使透过率达到80%以上;光学系统焦距为25mm,相对孔径为F/1.5。为提高探测器的灵敏度和探测率,采用3级热电致冷,元件冷面温度为-65℃±0.02℃。为提高放大器的抗干扰性能,放大器电路采用共模抑制能力很强的差模输入方式。由于探测器箱温的变化会影响对轴温的探测精度,所以在探测器中增加了由可控热源、热源温度传感器(铂电阻)、热靶、控制模板、加热电源模板等构成的温控自适应系统。该探测器经在提速铁路干线上安装运用,取得了满意的效果。     

海洋石油平台超声波可燃气体探测器应用分析

文章首先对目前常用的可燃气体泄漏探测手段进行分析,讨论了超声波原理和超声波可燃气体探测器的性能特点,对比了超声波气体探测器的优点和缺点。并结合南海某气田的应用实例,论证了在海洋石油平台上气体超声波的布置建议的可行性。     

基于DTA的OD估计方法的交通检测器优化布置模型研究

论文在探讨了动态交通分配和动态0D估计背景下的交通检测器优化问题的基础上，提出了基于DTA的动态OD估计方法的交通检测器布置原则；从预算，对路网中交通流量信息的覆盖程度，对关键路段的检测、对重复检测器的剔除等方面对路网交通检测方案进行约束，建立了交通检测器优化布置模型；最后将遗传算法用于交通检测器优化布置模型的求解，证明了基于DTA的动态0D估计方法的交通检测器优化布置模型的有效性。论文方法概念清楚、操作简单，是交通检测器优化布置的一种可行方法。     

光纤光栅测温结合红外火焰探测器在公路隧道火灾监测中的应用

公路隧道结构封闭狭长、救援空间有限,因此公路隧道的火灾探测对系统的反应时间和准确率具有更高的要求.文中将光纤光栅测温技术和火焰探测器技术进行结合应用,更有利于高速公路隧道火灾的实时监测.使用光纤光栅作为温度传感器,能够快速探测到隧道环境温度的变化,且传感器结构稳定;而对于火灾初期范围较小的火焰,暂时未能引起环境温度的明...     

全电子汽车灯光检测器的研制

针对车辆行驶过程中不能及时发现车灯故障的问题,设计了一种新型全电子汽车灯光检测器。该检测器作为汽车制造过程中的固定电器件安装在汽车中央控制盒上,通过和车灯串联的分压电阻作为敏感元件来控制电路工作。介绍了该灯光检测器的工作原理、电路设计及性能。通过装车使用表明,该产品性能优良,且创下50万km无故障的记录。     

Macroscopic Fundamental Diagrams: A cross-comparison of estimation methods

This paper aims to cross-compare existing estimation methods for the Macroscopic Fundamental Diagram. Raw data are provided by a mesoscopic simulation tool for two typical networks that mimic an urban corridor and a meshed urban center. We mainly focus on homogenous network loading in order to fairly cross-compare the different methods with the analytical reference. It appears that the only way to estimate the MFD without bias is to have the full information of vehicle trajectories over the network and to apply Edie’s definitions. Combining information from probes (mean network speed) and loop detectors (mean network flow) also provides accurate results even for low sampling rate (<10%). Loop detectors fail to provide a good estimation for mean network speed or density because they cannot capture the traffic spatial dynamics over links. This paper proposes a simple adjustment technic in order to reduce the discrepancy when only loop detectors are available.     

Improved vehicle classification from dual-loop detectors in congested traffic

Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Length-based vehicle classification from dual loop detectors is among the lowest cost technologies commonly used for collecting these data. Like many vehicle classification technologies, the dual loop approach works well in free flow traffic. Effective vehicle lengths are measured from the quotient of the detector dwell time and vehicle traversal time between the paired loops. This approach implicitly assumes that vehicle acceleration is negligible, but unfortunately at low speeds this assumption is invalid and length-based classification performance degrades in congestion.To addresses this problem, we seek a solution that relies strictly on the measured effective vehicle length and measured speed. We analytically evaluate the feasible range of true effective vehicle lengths that could underlie a given combination of measured effective vehicle length, measured speed, and unobserved acceleration at a dual loop detector. From this analysis we find that there are small uncertainty zones where the measured length class can differ from the true length class, depending on the unobserved acceleration. In other words, a given combination of measured speed and measured effective vehicle length falling in the uncertainty zones could arise from vehicles with different true length classes. Outside of the uncertainty zones, any error in the measured effective vehicle length due to acceleration will not lead to an error in the measured length class. Thus, by mapping these uncertainty zones, most vehicles can be accurately sorted to a single length class, while the few vehicles that fall within the uncertainty zones are assigned to two or more classes. We find that these uncertainty zones remain small down to about 10 mph and then grow exponentially as speeds drop further.Using empirical data from stop-and-go traffic at a well-tuned loop detector station the best conventional approach does surprisingly well; however, our new approach does even better, reducing the classification error rate due to acceleration by at least a factor of four relative to the best conventional method. Meanwhile, our approach still assigns over 98% of the vehicles to a single class.