scholarly journals Improved Dual-Loop Detection System for Collecting Real-Time Truck Data

2005 ◽  
Vol 1917 (1) ◽  
pp. 108-115 ◽  
Author(s):  
Xiaoping Zhang ◽  
Nancy L. Nihan ◽  
Yinhai Wang
Keyword(s):  
Real Time ◽  
Detection System ◽  
Normal Operation ◽  
Event Data ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
System A ◽  
Loop Detection ◽  
Washington State Department ◽  
Transportation Research

The Washington State Department of Transportation (WSDOT) has a loop detection system on its Greater Seattle freeway network to provide real-time traffic data. The dual-loop detectors installed in the system are used to measure vehicle lengths and then classify each detected vehicle into one of four categories according to its length. The dual loop's capability of measuring vehicle length makes the loop detection system a potential real-time truck data source for freight movement studies because truck volume estimates by basic length category can be developed from the vehicle length measurements produced by the dual-loop detectors. However, a previous study found that the dual-loop detectors were consistently underreporting truck volumes, whereas the single-loop detectors were consistently overcounting vehicle volumes. As an extension of the previous study, the research project described here investigated possible causes of loop errors under nonforced-flow traffic conditions. A new dual-loop algorithm that can address these error causes and therefore tolerate erroneous loop actuation signals was developed to improve the performance of the WSDOT loop detection system. A quick remedy method was also recommended to address the dual-loop undercount problem without replacing any part of the existing system hardware or software. In addition, a laptop-based detector event data collection system (DEDAC) that can collect loop detector event data without interrupting a loop detection system's normal operation was developed in this research. The DEDAC system enables various kinds of transportation research and applications that could not otherwise be possible.

Development of a System for Collecting Loop-Detector Event Data for Individual Vehicles

2003 ◽  
Vol 1855 (1) ◽  
pp. 168-175 ◽  
Author(s):  
Xiaoping Zhang ◽  
Yinhai Wang ◽  
Nancy L. Nihan ◽  
Mark E. Hallenbeck
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Field Application ◽  
Normal Operation ◽  
Vehicle Speed ◽  
Event Data ◽  
Loop Detectors ◽  
Loop Detector ◽  
High Sampling Rate ◽  
Loop Detection

Typical freeway inductive loop detection systems, under normal operation, aggregate individual loop-detector actuations sampled at 60 Hz into 20-s or 30-s averages of velocity, flow, and lane-occupancy measurements. While such aggregations are appropriate for serving as inputs to control system algorithms, and they save disk space for archiving loop data, a large amount of useful data regarding individual vehicles is lost. For single-loop detectors, the lost information includes individual vehicle arrival, departure, and presence times. For speed traps, the lost information also includes the calculated individual vehicle speed and length. Yet this information about individual vehicles is desirable to transportation researchers and planners. The unavailability of this information makes in-depth investigation of detector errors difficult or even impossible. A system for collecting detector event data is proposed. This system can sample loop actuations with sampling rates of 60 Hz or higher and then save, process, and present the collected event data in real time without interfering with the detector controller’s normal operation. A stand-alone Windows program was developed for performing real-time high-frequency loop event data collection. A system reliability test and field application indicate that the system can collect realtime detector event data at a high sampling rate (60 Hz or higher). Additionally, this system makes real-time loop data quality evaluation, loop malfunction identification, and loop error correction feasible.

scholarly journals Species identification of Anguilla japonica by real-time PCR based on a sequence detection system: a practical application to eggs and larvae

2009 ◽  
Vol 66 (9) ◽  
pp. 1915-1918 ◽  
Author(s):  
Yuki Minegishi ◽  
Tatsuki Yoshinaga ◽  
Jun Aoyama ◽  
Katsumi Tsukamoto
Keyword(s):  
Real Time ◽  
Species Identification ◽  
Developmental Stages ◽  
Detection System ◽  
Anguilla Japonica ◽  
Sequence Detection ◽  
Target Region ◽  
System A ◽  
Sequence Variations ◽  
Eggs And Larvae

Abstract Minegishi, Y., Yoshinaga, T., Aoyama, J., and Tsukamoto, K. 2009. Species identification of Anguilla japonica by real-time PCR based on a sequence detection system: a practical application to eggs and larvae. – ICES Journal of Marine Science, 66: 1915–1918. To develop a practical method for identifying Japanese eel Anguilla japonica eggs and larvae to species by a sequence detection system using a real-time polymerase chain reaction (PCR), we examined (i) the sensitivity of the system using samples at various developmental stages, and (ii) influences of intra- and interspecific DNA sequence variations in the PCR target region. PCR amplifications with extracted DNA solution at 7.0 ng µl−1 or lower were efficient at distinguishing A. japonica from other anguillids. A single egg at the gastrula or later developmental stages could also be identified. Two sequence variations in the PCR target region were observed in 2 out of 35 A. japonica collected from three localities, and from four year classes at a single locality. These mutations, however, did not affect the result of species identification achieved by A. japonica-specific PCR primers and probe. The accuracy of this PCR-based method of species identification will help in field surveys of the species.

Real-Time Traffic Measurement from Single Loop Inductive Signatures

2002 ◽  
Vol 1804 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Seri Oh ◽  
Stephen G. Ritchie ◽  
Cheol Oh
Keyword(s):  
Real Time ◽  
The United States ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Single Loop ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

scholarly journals Sliding wire detection system of electric lock screw tool based on motor characteristics

2021 ◽  
Vol 2137 (1) ◽  
pp. 012009
Author(s):  
Ning Zhang ◽  
Yinxin Yan ◽  
Houcheng Yang ◽  
Zhangsi Yu
Keyword(s):  
Real Time ◽  
Detection System ◽  
Low Cost ◽  
High Sensitivity ◽  
High Accuracy ◽  
Normal Operation ◽  
Automatic Assembly ◽  
The Real ◽  
Current Change ◽  
Real Time Detection

Abstract This paper presents a sliding wire detection system of electric screw locking tool based on the characteristics of motor. The system can judge whether the screw has sliding wire through the current change of motor during normal operation, and realize the real-time detection and alarm of sliding wire. The system has the advantages of high sensitivity, low cost and high accuracy. It can be widely used in automatic assembly and other fields.

A MACHINE LEARNING FRAMEWORK FOR REAL-TIME TRAFFIC DENSITY DETECTION

2009 ◽  
Vol 23 (07) ◽  
pp. 1265-1284 ◽  
Author(s):  
JING CHEN ◽  
EVAN TAN ◽  
ZHIDONG LI
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Density Estimation ◽  
Heavy Traffic ◽  
Weather Conditions ◽  
Traffic Density ◽  
Model Parameters ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Flow Information

Traffic flow information can be employed in an intelligent transportation system to detect and manage traffic congestion. One of the key elements in determining the traffic flow information is traffic density estimation. The goal of traffic density estimation is to determine the density of vehicles on a given road from loop detectors, traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and traffic radars, there is a growing trend of using video-content-understanding technique to determine the traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy traffic congestions. In this paper, we attempt to address the problem of real-time traffic density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the traffic density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of traffic density estimation. Thus, we propose a novel feature extraction scheme to represent traffic density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average traffic density estimation accuracy of 96.6% over various different illumination and weather conditions.

scholarly journals Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 409 ◽  
Author(s):  
Fan Ding ◽  
Xiaoxuan Chen ◽  
Shanglu He ◽  
Guangming Shou ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Field Test ◽  
High Capacity ◽  
Traffic Information ◽  
Percentage Error ◽  
Power Module ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Comparison Results ◽  
Data Source

Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.

Real Time Statistical Leak Detection on the RRP Crude Oil Network, Holland

2002 ◽  
Author(s):  
Joep Hoeijmakers ◽  
John Lewis
Keyword(s):  
Crude Oil ◽  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Field Application ◽  
Normal Operation ◽  
False Alarms ◽  
Pipeline Network ◽  
Oil Pipeline ◽  
Detection Systems

Prior to the year 2000, the RRP crude oil pipeline network in Holland and Germany was monitored using a dynamic leak detection system based on a dynamic model. The system produced some false alarms during normal operation; prompting RRP to investigate what advances had been made in the leak detection field before committing to upgrade the existing system for Y2K compliance. RRP studied the available leak detection systems and decided to install a statistics-based system. This paper examines the field application of the statistics based leak detection system on the three crude oil pipelines operated by RRP. They are the 177 km Dutch line, the 103 km South line, and the 86 km North line. The results of actual field leak trials are reported. Leak detection systems should maintain high sensitivity with the minimum of false alarms over the long term; thus this paper also outlines the performance of the statistical leak detection system over the last year from the User’s perspective. The leak detection experiences documented on this crude oil pipeline network demonstrate that it is possible to have a reliable real-time leak detection system with minimal maintenance costs and without the costs and inconvenience of false alarms.

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