• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.6
• /
• pp.25-39
• /
• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Journal of Ocean Engineering and Technology
• /
• v.24 no.1
• /
• pp.140-145
• /
• 2010

This study presents a detection method for mechanically fabricated defects on underwater steel pipes, using ultrasonic guided waves. Three different diameters (60, 90, and 114 mm) of 1000-mm long steel pipes were considered, along with several experimental design factors such as incident angles, incident distances, and the degrees of defects, to investigate how these factors affected the experimental results - the detectability of the mechanical defects. From the experimental results, we determined that the amplitude and arrival time of the first received wave signals gave a promising clue for distinguishing the existence of the defects and their severities. Between the amplitude and arrival time, the arrival time gave a more promising indication since it was affected by the experimental factors in a constant manner. Therefore, it was shown that the use of ultrasonic guided waves for underwater pipe inspection is feasible.

• Tribology and Lubricants
• /
• v.11 no.5
• /
• pp.114-121
• /
• 1995

An ultrasonic technique using normal-incident compressional waves was used to evaluate the surface and subsurface damage in ceramics produced by Hertzian indentation. Damage was produced by a blunt indenter (tungsten carbide ball) in glass-ceramic, green glass and silicon nitride. The damage was classified into two types; (1) Hertzian cone crack, in green glass and fine grain silicon nitride, and (2) distributed subsurface micro fractures, without surface damage, produced in glass ceramic. The ultrasonic technique was successful in detecting cone craks. The measurement results with the Hertzian cone cracks indicated that cracks perpendicular to the surface could be detected by the normal-incident compressional waws. Also shown is the capability of normal-incident compressional waves in detection distributed micro-sized cracks size of subsurface microfractures.

• Journal of Korean Society of Transportation
• /
• v.22 no.6
• /
• pp.175-196
• /
• 2004

This study focuses on improving the performance of freeway incident detection by introducing some new measures to reduce false alarms in developing a new incident detection model. The model consists of the 5 major components through which a series of decision makings in determining the given traffic flow condition are made. The decision making process was designed such that the causes of traffic congestions can be accurately classified into several types including incidents and bottlenecks according to their unique characteristics. The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of the detection rate and detection time. It should noted that the model produced much less false alarms than most of the existing models. The study results prove that the initial objective of the study was satisfied as it was an experimental trial to improve the false alarm rate for the incident detection model to be more pactically usable for traffic management purposes.

• Journal of Korean Society of Transportation
• /
• v.28 no.6
• /
• pp.7-15
• /
• 2010

Research of this paper focused on developing and demonstrating of algorithm with the figures of difference between historical traffic pattern data and real-time traffic data to decide on what the incident is. The aim of this dissertation is to develop incident detection algorithm which can be understood and modified easier to operate. To establish traffic pattern of this algorithm, weighted moving average method was applied. The basis of this method was traffic volume and speed of the same day and time at the same location based on 30-second raw data. The model was completed by a serious of steps of process-screening process of error data, decision of the traffic condition, comparison with pattern data, decision of incident circumstances, continuity test. A variety of parameter value was applied to select reasonable parameter. Results of application of the algorithm came out with figures of average detection rate 94.7 percent, 0.8 percent rate of misinformation and the average detection time 1.6 minutes. With these following results, the detection rate turned out to be superior compared with result of existing model. Applying the concept of traffic patterns was useful to gain excellent results of this study. Also, this study is significant in terms of making algorithm which theorized the decision process of actual operators.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.7 no.4
• /
• pp.49-60
• /
• 2008

The current VIPS are not effective in safety point of view, because they are originally developed for mimicking loop detectors. Therefore, it is important to identify vehicle trajectories in real time, because recognizing vehicle movements over a detection zone enables to identify which situations are hazardous, and what causes them to be hazardous. In order to improve limited safety functions of the current VIPS, this research has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, incident detection and conflict as well as traffic information via tracking image detectors. This system is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of various traffic situations. Experiments were conducted for measuring the cases of incident detection and abnormal vehicle trajectory with rapid lane change.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.4
• /
• pp.1-11
• /
• 2014

Traffic congestion cost is more likely to occur in the inner city than interregional road, and it accounts for about 63.39% of the whole. Therefore, it is important to mitigate traffic congestion of the inner city. Traffic congestion in the urban could be divided into Recurrent congestion and Non-recurrent congestion. Quick and accurate detection of Non-recurrent congestion is also important in order to relieve traffic congestion. The existing studies about incident detection have been variously conducted, however it was limited to Uninterrupted Traffic Flow Facilities such as freeway. Moreover study of incident detection on the interrupted Traffic Flow Facilities is still inadequate due to complex geometric structure such as traffic signals and intersections. Therefore, in this study, incident detection model was constructed using by Artificial Neural Network to aim at urban arterial road that is interrupted traffic flow facility. In the result of the reliability assessment, the detection rate were 46.15% and false alarm rate were 25.00%. These results have a meaning as a result of the initial study aimed at interrupted traffic flow. Furthermore, it demonstrates the possibility that Non-recurrent congestion can be detected by using car navigation data such as car navigator system device.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.5D
• /
• pp.623-631
• /
• 2011

In this study, a new method which can detect incidents in interrupted traffic flow was suggested. The applied method of detecting the incident is the Latin Square Analysis Method by using traffic traits. In the Latin Square Analysis, unlike other previously tried methods, the traffic situation was analyzed, this time considering the changes in traffic traits for each lane and for each time period. The data used in this study were the data observed in the actual field with fine weather. The traffic volumes, the vehicle speed and the occupancy rate were collected on the interrupted flow road. The data were collected in normal and incident situations. The incidents occurred on the second lane, the time of persistent incidents was set to 10 minutes. The Latin Square Analyses were performed using the collected data with the traffic volume, with the vehicle speed or with the occupancy rate. As a result in this study, in case of detecting the traffic situations with Latin Square Analysis, it will be more successful to apply traffic volume to detect the traffic situations than to apply other factors.

• Journal of the Korean Magnetics Society
• /
• v.18 no.2
• /
• pp.71-74
• /
• 2008

In this paper, we have measured the effect of the optical spot size, incident upon the quadrant photodetector, on the optical displacement sensitivity of the optical beam deflection technique. We have built an optical displacement detection system based on the optical beam deflection method using 3 mW He-Ne laser and measured the displacement sensitivity with changing the optical spot size on the quadrant photodetector. We have also calculated the changes in the optical displacement sensitivity as a function of the incident laser spot size by modeling a circular optical spot with constant laser intensity. Our experimental and theoretical studies show that the optical displacement sensitivity increases with the decrease in the optical spot size. This suggests that in the design of the optical motion detection systems with sub-nanometer sensitivity, the displacement sensitivity can be optimized by reducing the size of the incident optical spot on the detector.

• Journal of Industrial Convergence
• /
• v.18 no.3
• /
• pp.53-61
• /
• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.