Fuzzy-logic-based incident detection for signalized diamond interchanges

This paper documents a fuzzy-logic-based incident detection algorithm for signalized urban diamond interchanges. The model is capable of detecting lane-blocking incidents whose effects are manifested by patterns of deterioration in traffic conditions that require adjustments in signal control strategies. As a component of a real-time traffic adaptive control system for signalized diamond interchanges, the algorithm feeds an incident report (i.e., the time, location, and severity of the incident) to the system's optimization manager, which uses that information to determine the appropriate signal control strategy.The performance of the model was studied using a simulation of an actual diamond interchange. The simulation study evaluated the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy-logic-based approach is considered promising.     

超声相控阵缺陷检测技术研究

为了将相控阵超声检测技术进一步推广应用,对带有人工缺陷的试件分别采用CR、超声相控阵检测技术进行检测,将检测结果进行比对,试验结果表明CR检测技术与相控阵检测技术在体积型缺陷与面积型缺陷检出率方面各具优势,应结合实际工件使用工况,确定可能出现的缺陷类型,采用合适的检测方法与检测工艺,最大限度地提高缺陷检出率.     

TEM技术在大牛地气田管道腐蚀评价中的应用

文中结合大牛地气田埋地管道腐蚀直接评价（ECDA,ICDA）实践,应用管道壁厚瞬变电磁（TEM）检测技术,通过在沿管道按一定间距布置测点进行基础检测,对所发现的管壁厚度异常点处再加密检测,从而确定腐蚀严重部位,较快地掌握气田整体管道质量情况,达到不需开挖、不破坏管道的良好检测效果。     

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.     

Railway wheel-flat detection and measurement by ultrasound

This work presents an innovative ultrasound technique designed to detect and quantify flats formed in the rolling surface of railway wheels. Differently from other approaches, ultrasonic pulses (Rayleigh waves) are sent over a measuring rail. The variations in the round-trip time of flight (RTOF) of the ultrasound pulse to the rail-wheel contact point allow detecting and quantifying the wheel-flats. In spite of the wear state of the irregularity, the method provides the loss of material and the length of the flat originally formed by abrasion. A theoretical background supports the technique which offers many advantages for railway maintenance. Simulations and experimental results match the expected ones.     

高速公路场景下的电子稳像技术研究

为了去除高速公路监控视频中自身位置不稳定的事物给全局运动矢量估计带来误差,先通过Canny算子检测图像中的直线,并根据车道边界的特征筛选出道路边界,确定道路区域。再检测道路区域内的Harris角点信息,并与背景图中道路区域内的角点位置进行匹配,从而得到全局运动矢量,并计算出稳定的图像。实验表明,该算法能够满足高速公路场景下的稳像需求。     

Converting mixed pedestrian flows into equivalent commuters using standard pedestrian equivalent factors

This paper develops the concept of standard pedestrian equivalent (SPE) factors for converting a mixed pedestrian flow into an equivalent commuter flow. After a comprehensive review of passenger car equivalent (PCE) methodologies, the equal total travel time method is utilised for SPE estimation. A micro-simulation approach is employed for the formulation of the total travel time–flow relationship. Field data collected on walking speed distributions for commuters and older adults in Australia are used as model inputs. An independent samples t-test confirms the significant difference between walking speeds of commuters and older adults. For this paper, a unidirectional flow on flat walkways is initially considered and evaluated across proportions of older people, different flows and different walkway widths. The introduction of older adults significantly increases total travel time especially under congested conditions. Results of this investigation can be used for evaluation or design of pedestrian facilities experiencing similar flow conditions.     

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.     

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.     

信号采样理论在交通流检测点布设中的应用

在对交通流检测点布设领域研究应用现状进行分析与抽象的基础上,提出交通流检测点布设本质上属于空间交通流信号采样问题。依据信号采样理论,建立了确定检测点间距或密度的方法与流程,该流程包括空间交通流信号产生、离散傅里叶变换、频谱分析、间距确定、误差分析5个步骤。对高速公路拥挤状态下的仿真分析表明:100 m的检测点间距可以满足对拥挤交通流状态的真实检测,该数值接近于实际应用中的最小值(125 m),说明该理论方法具有较好的实用性。