A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road c...A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road conditions the level of service and the proportion of trucks.The process of identification includes two parts. One is to identify the upstream of the bottleneck by comparing the distance between the current occupancy rate and the mean value of the occupancy rate and the variance of the occupancy rate.The other process is to identify the downstream of the bottleneck by calculating the difference of the upstream occupancy rate with that of the downstream.In addition the algorithm evaluation standards which are based on the time interval of the data the detection rate and the false alarm rate are discussed.The proposed algorithm is applied to detect the bottleneck locations in the Shanghai Inner Ring Viaduct Dabaishu-Guangzhong road section.The proposed method has a good performance in improving the accuracy and efficiency of bottleneck identification.展开更多
With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant（4Q） detector, a fiber positioning and real-tim...With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant（4Q） detector, a fiber positioning and real-time monitoring system based on the 4Q detector is proposed. The detection accuracy of this system is directly determined by the precision of the center of the spot. A Gaussian fitting algorithm based on the 4Q detector is studied and applied in the fiber positioning process to improve the calculated accuracy of the spot center. The relationship between the center position of the incident spot and the detector output signal is deduced. An experimental platform is built to complete the simulated experiment. Then we use the Gaussian fitting method to process experimental data, compare the fitting value with the theoretical one and calculate the corresponding error.展开更多
Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the s...Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the setting thresholds. Using these monitoring methods may cause serious false positive or false negative results. In order to precisely monitor the state of equipment, the problem of abnormality degree detection without fault sample is studied with a new detection method called negative potential field group detectors（NPFG-detectors）. This method achieves the quantitative expression of abnormality degree and provides the better detection results compared with other methods. In the process of Iris data set simulation, the new algorithm obtains the successful results in abnormal detection. The detection rates for 3 types of Iris data set respectively reach 100%, 91.6%, and 95.24% with 50% training samples. The problem of Bearing abnormality degree detection via an abnormality degree curve is successfully solved.展开更多
Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with...Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.展开更多
A Data Acquisition System （DAQ） for electron energy loss coincident spectrometers （EELCS） has been developed. The system is composed of a Multiplex Time-Digital Converter （TDC） that measures the flying time of p...A Data Acquisition System （DAQ） for electron energy loss coincident spectrometers （EELCS） has been developed. The system is composed of a Multiplex Time-Digital Converter （TDC） that measures the flying time of positive and negative ions and a one-dimension positionsensitive detector that records the energy loss of scattering electrons. The experimental data are buffered in a first-in-first-out（FIFO） memory module, then transferred from the FIFO memory to PC by the USB interface. The DAQ system can record the flying time of several ions in one collision, and allows of different data collection modes. The system has been demonstrated at the Electron Energy Loss Coincident Spectrometers at the Laboratory of Atomic and Molecular Physics, USTC. A detail description of the whole system is given and experimental results shown.展开更多
Current traffic signal split failure (SF) estimations derived from high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the sele...Current traffic signal split failure (SF) estimations derived from high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the selected thresholds, detector lengths, and vehicle arrival patterns. Connected vehicle (CV) trajectory data can more definitively show when a vehicle split fails by evaluating the number of stops it experiences as it approaches an intersection, but it has limited market penetration. This paper compares cycle-by-cycle SF estimations from both high-resolution controller event data and CV trajectory data, and evaluates the effect of data aggregation on SF agreement between the two techniques. Results indicate that, in general, split failure events identified from CV data are likely to also be captured from high-resolution data, but split failure events identified from high-resolution data are less likely to be captured from CV data. This is due to the CV market penetration rate (MPR) of ~5% being too low to capture representative data for every controller cycle. However, data aggregation can increase the ratio in which CV data captures split failure events. For example, day-of-week data aggregation increased the percentage of split failures identified with high-resolution data that were also captured with CV data from 35% to 56%. It is recommended that aggregated CV data be used to estimate SF as it provides conservative and actionable results without the limitations of intersection and detector configuration. As the CV MPR increases, the accuracy of CV-based SF estimation will also improve.展开更多
文摘A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road conditions the level of service and the proportion of trucks.The process of identification includes two parts. One is to identify the upstream of the bottleneck by comparing the distance between the current occupancy rate and the mean value of the occupancy rate and the variance of the occupancy rate.The other process is to identify the downstream of the bottleneck by calculating the difference of the upstream occupancy rate with that of the downstream.In addition the algorithm evaluation standards which are based on the time interval of the data the detection rate and the false alarm rate are discussed.The proposed algorithm is applied to detect the bottleneck locations in the Shanghai Inner Ring Viaduct Dabaishu-Guangzhong road section.The proposed method has a good performance in improving the accuracy and efficiency of bottleneck identification.
基金support by the Fundamental Research Funds for the Central Universities of China (2013/B15020271)the National Natural Science Foundation of China (1014/515029111)the National Undergraduate Training Program for Innovation and Entrepreneurship (201610294069)
文摘With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant（4Q） detector, a fiber positioning and real-time monitoring system based on the 4Q detector is proposed. The detection accuracy of this system is directly determined by the precision of the center of the spot. A Gaussian fitting algorithm based on the 4Q detector is studied and applied in the fiber positioning process to improve the calculated accuracy of the spot center. The relationship between the center position of the incident spot and the detector output signal is deduced. An experimental platform is built to complete the simulated experiment. Then we use the Gaussian fitting method to process experimental data, compare the fitting value with the theoretical one and calculate the corresponding error.
基金Supported by National Natural Science Foundation of China(Grant No.51175316)Specialized Research Fund for the Doctoral Program of Higher Education,China(Grant No.20103108110006)Basic Research Project of Shanghai Science and Technology Commission,China(Grant No.11JC1404100)
文摘Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the setting thresholds. Using these monitoring methods may cause serious false positive or false negative results. In order to precisely monitor the state of equipment, the problem of abnormality degree detection without fault sample is studied with a new detection method called negative potential field group detectors（NPFG-detectors）. This method achieves the quantitative expression of abnormality degree and provides the better detection results compared with other methods. In the process of Iris data set simulation, the new algorithm obtains the successful results in abnormal detection. The detection rates for 3 types of Iris data set respectively reach 100%, 91.6%, and 95.24% with 50% training samples. The problem of Bearing abnormality degree detection via an abnormality degree curve is successfully solved.
基金supported by the National Natural Science Foundation (71301119)the Shanghai Natural Science Foundation (12ZR1434100)
文摘Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.
基金National Natural Science Foundation of China (Nos. 10134010 and 10004010)
文摘A Data Acquisition System （DAQ） for electron energy loss coincident spectrometers （EELCS） has been developed. The system is composed of a Multiplex Time-Digital Converter （TDC） that measures the flying time of positive and negative ions and a one-dimension positionsensitive detector that records the energy loss of scattering electrons. The experimental data are buffered in a first-in-first-out（FIFO） memory module, then transferred from the FIFO memory to PC by the USB interface. The DAQ system can record the flying time of several ions in one collision, and allows of different data collection modes. The system has been demonstrated at the Electron Energy Loss Coincident Spectrometers at the Laboratory of Atomic and Molecular Physics, USTC. A detail description of the whole system is given and experimental results shown.
文摘Current traffic signal split failure (SF) estimations derived from high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the selected thresholds, detector lengths, and vehicle arrival patterns. Connected vehicle (CV) trajectory data can more definitively show when a vehicle split fails by evaluating the number of stops it experiences as it approaches an intersection, but it has limited market penetration. This paper compares cycle-by-cycle SF estimations from both high-resolution controller event data and CV trajectory data, and evaluates the effect of data aggregation on SF agreement between the two techniques. Results indicate that, in general, split failure events identified from CV data are likely to also be captured from high-resolution data, but split failure events identified from high-resolution data are less likely to be captured from CV data. This is due to the CV market penetration rate (MPR) of ~5% being too low to capture representative data for every controller cycle. However, data aggregation can increase the ratio in which CV data captures split failure events. For example, day-of-week data aggregation increased the percentage of split failures identified with high-resolution data that were also captured with CV data from 35% to 56%. It is recommended that aggregated CV data be used to estimate SF as it provides conservative and actionable results without the limitations of intersection and detector configuration. As the CV MPR increases, the accuracy of CV-based SF estimation will also improve.
