Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data

Saturation flow rate (SFR) is a fundamental parameter to the level of service evaluation, lane capacity calculation, and signal timing plan optimization at signalized intersections. It is affected by a variety of factors including weather conditions, lane width, and the type of the driver. How to accurately estimate the SFR remains one of the most important tasks in traffic engineering. Existing studies generally rely on the field measurement method which requires a large number of people collecting data at the intersection. As a result, the method incurs a high economic cost and cannot adapt to the dynamic change of SFR. In recent years, video detectors have been widely installed at intersections which are capable of recording the time each vehicle passes the stop line, the number plate of each vehicle, and the vehicle type. This paper therefore aims to propose an automatic estimation method for the SFR based on video detector data in order to overcome the limitation of the field measurement method. A prerequisite for estimating the SFR is to recognize the saturation headway. We consider the actual vehicle headway as time series and build an auxiliary regression equation whose parameters are estimated through the ordinary least squares method. We employ the Dickey-Fuller test to verify whether the headways in the time series are saturation headways. An iterative method using quantiles is proposed to filter out abnormal data. The SFR is finally calculated using the average value of saturation headways. To demonstrate the proposed method, we conduct a case study using data from an intersection with three entrance lanes in Qujing city, Yunnan Province, China. The overall estimation process is displayed and the impacts of quantile selection and data duration on the estimation accuracy are analyzed.

Ground Truth Generating Tool for Traffic Video Detector

The paper presents an application for generating ground truth data for the purposes of video detection and justifies its use in systems which analyze road traffic videos. The usefulness of described application in the development of video detection software is presented - especially during scene configuration and comparative analysis of video detection results versus ground truth data. The latter is possible due to simplicity of the result text files generated in a similar way both by the presented application and by the video detection algorithm. Two exemplary applications of the tool designed to generate ground truth data are presented, together with a discussion of their construction, functionality and abilities.

Video Techniques for Observation of the Leonid Storms

Video techniques, which provide high sensitivity, portability, moderate spatial resolution and excellent temporal resolution promise to be one of the most valuable methods for study of the forthcoming Leonid storm(s). While an unintensified video camera will detect very bright meteors (typically about 0 magnitude), some sort of image intensifier is needed to attain high meteor rates. Most current systems use a second or third generation microchannel plate (MCP) image intensifier lens or fibre-optically coupled to a charge coupled device (CCD) video detector.