• International Journal of Highway Engineering
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• v.12 no.3
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• pp.49-57
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• 2010

This case study investigated red light violations at CBD, suburban and rural signalized intersections in chungbuk. The goal of this study is to understanding the collection between red light violations and various driver, vehicles and environmental factors. This study uses descriptive statistics analysis and logistics analysis with SPSS 12.0 software. The major results of this study are as follows. First, red light violations occurred at rural and CBD more than suburban area. Second, About 81.1% of the violators were traveling at or below the posted speed limit. Moreover, 77.3% of the violations occurred within 2 seconds after the on set of red light. Finally, the logistic regression model, which is statistically significant(chisquare=0.000, McFadde=0.265)was developed, and includes the local type(CBD/suburban/rural), violators' gender, season, vehicle type, time of day, vehicle speed as the independent variables. In this study did not find significant relationship between red light violators' age and their driving behavior approaching signalized intersections.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.187-196
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• 1999

The increasing interest in the use of Automated Speed Enforcement (ASE) systems in Korea enables to enforce speed violation by National Police Agency. We have analyzed the mechanism of ASE systems on traffic safety throughout Korea. 1 The data collected on a 2km road-section of each 32 ASE stations during one rear period indicate significant safety improvement. The results were (a) a decrease in the total number of accidents of 28%, (b) a decrease in the number of fatalities of 60%. 2. The study also that ASE systems are effective to reduce average speed, speed variance, and short headway. 3. Based on the operational data collected at 15 locations, an aggregate safety prediction model is proposed as a multiple regressions form. The primary operational variables that appear to affect the frequencies of accident are : average speed, speed variance, and the number of vehicles exceeding 30km/h of posted speed limit.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.593-596
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• 2018

The speed of vehicles has remained a significant factor that influences the severity of accidents and traffic accident rate in many parts of the world including South Korea. This behavior where drivers drive at speeds which exceed a posted safe threshold is known as 'speeding'. Over the past twenty years, the Korean National Police Agency (NPA) has become aware of an increased frequency of drivers who are speeding. Therefore, fixed-type ASE systems [1] have been installed on hazardous road sections of many highways. These system monitor vehicle speeds using a camera. However, the use of ASE systems has changed the behavior of the drivers. Specifically, drivers reduce speed or avoid the route where the cameras are mounted. It is not practical to install cameras at every possible location. Therefore, it is challenging to thoroughly explore the location where speeding occurs. In view of these problems, the author of this paper designed and implemented a prototype visualization system in which point and color are used to show vehicle location and associated over-speed information. All of this information was used to create a comprehensive visualization application to show information about vehicle driving. In this paper, we present an approach detecting vehicles moving at speeds which exceed a threshold and visualizing the points those violations occur on a map. This was done using vehicle trajectory data collected in Daegu city. We propose steps for exploring the data collected from those sensors. The resulting mapping has two layers. The first layer contains the dynamic vehicle trajectory data. The second underlying layer contains the static road networks. This allows comparing the speed of vehicles on roads with the known maximum safe speed of those roads, and presents the results with a visualization tool. We also compared data about people who drive over threshold safe speeds on each road on days and weekends based on vehicle trajectories. Finally, our study suggests improved times and locations where law enforcement should use monitoring with speed cameras, and where they should be stricter with traffic law enforcement. We learned that people will drive over the speed limit at midnight more than 1.9 times as often when compared with rush hour traffic at 8 o'clock in the morning, and 4.5 times as often when compared with traffic at 7 o'clock in the evening. Our study can benefit the government by helping them select better locations for installation of speed cameras. This would ultimately reduce police labor in traffic speed enforcement, and also has the potential to improve traffic safety in Daegu city.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.95-105
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• 2002

Under a specific roadway alignment condition by design-speed standards, safety of the roadway is determined by an actual operating speed of a driver. This research takes first lanes of four-lane(hi-direction) rural highways as target facility. It also takes the straight and curved lanes of the selected highways for in-depth study. This study used NC-97 to detect speeds of passenger cars whose speeds are not affected by front vehicles. This research analyzed properties of 85th percentile operating speed at upstream of horizontal and through curves under various alignment conditions. The results show that 53∼65 Percent of drivers drive faster than the posted speed-limit (80KPH) by 14∼20 KPH on average. It also shows that the 85th-percentile operating speeds are the lowest at the middle point of curve length when curve radius is smaller. However, they are lowest at 1/4 point of curve length when curve radius is greater. Along roadways where curve radius is small, difference between upstream speed and the speed along the curve is considerably large. On the other hand. the speed difference is setting smaller as the curve radius is increasing. According to the results, significant variables affecting the 85th percentile operating speeds are curve radius and the 85th-Percentile operating speeds of upstream curves.