Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.     

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.     

探地雷达在桥梁预应力管道定位检测中的应用

对预应力管道进行定位检测,是目前桥梁无损检测技术的一个重要问题,在阐述探地雷达无损检测技术原理和方法的基础上,通过室内模型试验,并结合工程实例说明了高精度探地雷达在桥梁预应力管道定位检测中的应用,及其无损、简易、效率高、精度高、抗干扰能力强的优点,为准确评价桥梁结构耐久性和安全使用功能提供科学依据。     

高密度电法在某铁路隧道检测中的应用

文章以京包线旗下营隧道工程为例，通过比较高密度电法与地质雷达法酮优劣，提出采用高密度电法进行隧道病害检测，并总结了在应用高密度电法前应进行的准备工作，阐述了高密度电算法在现场检测时的具体操作方法和注意事项。     

船舶电网频率实时检测仿真研究

针对电力推进船舶电网频率的实时精确检测,阐述了基于二阶直接型IIR数字陷波器的基本原理,基于LMP(p=3)自适应算法建立了在跟踪陷波器参数基础上的船舶电网频率实时检测模型,并分别在电网频率不变、频率发生阶跃变化以及频率线性变化的不同情况下进行了基于Simulink的仿真研究。仿真结果验证了提出的频率检测方法的实时性和精确性。     

埋地管道防腐层破损点检测技术综合应用研究

对防腐层完整性检测尤其是对防腐层破损点的精确定位,及对检出大、中面积破损点开挖修补,是确保土壤跟管道的有效绝缘、提高阴极保护效果的关键。文中分析了常用埋地钢质管道防腐层破损点检测技术特点,结合现场检测实践,综合应用多种技术,能有效检测埋地钢质管道防腐层破损点。根据测量数据可以判断破损点大小以及是否开挖修理,在管道安全运行及检测的经济性之间寻找结合点。     

原油管线在线等质量取样系统设计及实现

为了有效解决原油长输管线反复出现的原油途耗异常问题,通过对原油油种、工艺条件和原油输送方式等的分析,设计了等质量流量比例采样系统,由该系统实现原油管线在线自动取样。系统投用后,实现了实时对量,及时发现损耗异常情况,有效控制了原油途耗不稳定的问题。采用等质量流量比例采样模式,较好地解决了多油种、边收边付、多种输送方式以及流量范围宽等不利因素影响计量的问题,具有较好的参考和推广价值。     

基于序列影像种子跟踪的道路线自动提取算法

为快速建立道路精确的几何模型,利用车载移动测量系统获取的序列影像开展了道路边线自动提取方法研究.针对不同类型道路边线的特征,提出了一种基于序列影像种子跟踪的道路线自动算法.首先,对不同质量道路影像分别采用基于灰度信息和梯度信息的道路边线边缘检测方法;其次,采用线性回归模型拟合道路边线,以当前帧拟合直线为种子点,提出了基于种子直线的序列影像感兴趣预测方法,在此基础上对下一帧影像道路边线进行跟踪;最后,以高速公路和城市街区道路序列影像为例,开展了道路边线自动跟踪提取试验,结果表明此方法可以达到优于20 cm的精度,具有很好的鲁棒性.     

某型号制导电子箱的检测系统设计*

为解决现有导弹检测设备存在的缺陷,设计了一种模块化、通用化、智能化和标准化的综合检测修理平台。论文介绍了系统的硬件组成以及基于Labwindows／CVI的软件开发。通过部队实践证明,该系统具有重大军事效益和经济效益和广泛的运用前景。     

自行车流视频识别判断方法研究

考虑算法的实时性,交通流个体识别往往采用轮廓特征来描述。由于自行车与行人的很多轮廓特征值相近,在复杂城市交通环境中,从慢行交通流中准确地识别出自行车是目前采用视频检测交通流的难点之一。通过现场视频检测获取足够的样本外轮廓数据,分析检测目标轮廓的高、宽、高宽比、面积等典型轮廓特征,发现目标轮廓高宽比特征能较好地识别出行人、自行车等慢行交通,并给出合适的取值范围。研究表明自行车高宽比能够较好地成为视频识别的依据,为自行车视频识别提供数据。