• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.1
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• pp.1-15
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• 2011

The RLC systems is one of the intelligent transportation systems that has gained a nation-wide support for last decades and being installed to discourage motorists from running the red lights at signalized intersections. It is taken for granted that the RLC will provide motorists with increased safety, so that their installments are always justifiable. However, in order to acquire more efficiency and wider supports from the general public in future RLC installments, an improved methodology for analyzing the effects of the RLC systems is required. In order to satisfy this requirement, this research performed the following tasks. First, the number of signal violations after the RLC systems were investigated in order to check its resulting effects. Second, the number of crashes after the RLC systems were collected and compared with the number of signal violations. Third, a statistical analysis was carried out to develop the relationships between the signal violations and the crashes based on negative binomial distribution. The analysis revealed that the number of crashes has a close relationship with the RLC placement, traffic volume, vehicle speed, the number of phases, and the number of lanes for major approaches. Finally, based on the results found in this analysis, this research presents a methodology for analyzing the safety effects of placing the RLC that should be of service when investigating the economic consequences of the RLC systems.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.198-206
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• 2014

According to the statistics of the National Police Agency, red light running (RLR) crashes represent a significant safety issue throughout Korea. This study deals with the RLR crashes occurred at signalized intersections in Cheongju. The objectives of this study are to comparatively analyze the characteristics of between RLR crashes and the Non-RLR crashes, and to find out factors using a Binary Logistic Regression(BLR) model. In pursuing the above, the study gives particular attentions to testing the differences between the above two groups with the data of 2,246 RLR/ 3,884 Non-RLR crashes (2007-2011). The main results are as follows. First, many RLR crashes were occurred in the nighttime and in going straight. Second, the difference between RLR and Non-RLR crashes were clearly defined by crash type, maneuver of vehicle before crash, age of driver (30s, 50s), alcohol use and accident pattern. Finally, a statistically significant model (Hosmer and Lemeshow test : 7.052, p-value : 0.531) was developed through the BLR model.

• 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.30 no.6
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• pp.93-102
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• 2012

The frequency and severity of traffic accidents related to signalized intersections in urban areas have been more serious than those in both arterial segments and crosswalks. Especially, traffic accidents involved with injuries and fatalities have caused by traffic signal violations within intersections. Therefore, many countries including Korea have installed the red light enforcement camera (RLE) to reduce traffic accidents associated with the traffic signal violation. Meanwhile, many methodologies have been studied in terms of safety impacts estimation of red light enforcement, which, however, cannot be easy to conduct. In this study, safety impacts was estimated for intersections of Chicago downtown area using SPF models and EB approach. As a result, for all crash types and target traffic accident types such as "angle", "rear end", "sideswipe in the same and other directions", "turn", and "head on", fatal crashes were reduced by 26% and 38%. However, RLE may increase property-demage-only-crashes by 3.23% and 1.16%, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.553-560
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• 2010

This study examined the factors influencing the number of enforcement cases on traffic signal violation. The study collected enforcement data on traffic signal violation by multi-functional unmanned cameras across the Daegu metropolitan area, and analyzed the data to determine correlations among various factors including physical dimensions and engineering characteristics of the roadway as to how they would influence the number of violations. The resulting analysis provided a general model that assesses applicability for multi-functional unmanned cameras, and further shed helpful insights on efficient utilization of such traffic signal enforcement equipments in terms of installation thresholds and location. By identifying violation-prone features and characteristics and subsequent enforcement considerations, this research also supports safety efforts in reducing the number of traffic accidents as well.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.111-118
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• 2009

Factors causing traffic accidents are various and traffic law violations can be among them. The efforts to increase traffic law obedience rates of drivers are common in many other countries to reduce traffic accidents, and one of generally applied methods is to increase the amounts of violation fines. Expensive levels of traffic law violation fines could have drivers choose "obey the law" in their decision-making stages since they are economically better-off by following the law. In this sense, this study has developed an economic model to verify whether the current levels of the traffic violation fines of Korea are effective for drivers to choose "obey-the-law" decisions. Speed violations and traffic signal violations have been selected for the case study to verify the relationships between "expected probabilities of being-caught" and "levels of violations fines".

• Transportation Technology and Policy
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• v.12 no.1
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• pp.16-20
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• 2015

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.141-143
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• 2022

This paper presented a new technology to identify traffic violations of motorbike riders by detecting the traffic signal using You Only Look Once (YOLO) object detection. The hardware module that is mounted on the front of the motorbike consists of Raspberry Pi with a camera to run the YOLO object detection, a GPS module to acquire the motorcycle's coordinate, and a LoRa communication module to send the data to a cloud DB. The main goal of the software is to determine whether a motorbike has violated a traffic signal. This paper proposes a function to recognize the red traffic signal colour with its movement inside the camera angle and determine that the traffic signal violation happens if the traffic signal is moving to the right direction (the rider turns left) or moving to the top direction (the riders goes straight). Furthermore, if a motorbike rider is violated the signal, the rider's personal information (name, mobile phone number, etc), the snapshot of the violation situation, rider's location, and date/time will be sent to a cloud DB. The violation information will be delivered to the driver's smartphone as a push notification and the local police station to be used for issuing violation tickets, which is expected to prevent motorbike riders from violating traffic signals.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.300-309
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• 2007

본 연구는 신호교차로에서의 딜레마 죤 범위를 산정하고 신호위반 단속의 허용 범위를 분석하는데 있다. 각 교차로 접근로별 조사 자료를 토대로 딜레마 죤의 범위를 산정하였고, 이를 토대로 신호위반 단속에 있어 딜레마 죤의 영향을 최소화 할 수 있는 방안을 제시하고자 하였다. 기존의 딜레마 죤에 관한 연구에서는 인지-반응 시간과 황색신호시간을 초기값으로 적용하였으나 본 연구에서는 해당 교차로의 조사치를 적용하였다. 조사 방법으로 속도조사는 스피드 건을 이용하여 각각의 대상 교차로별 접근로에서 접근 속도 및 통과 속도를 조사하였으며, 운전자의 인지-반응 시간 및 황색신호시간에 교차로를 통과하는 차량조사는 비디오 촬영을 통하여 조사하였다. 이러한 조사된 자료를 토대로 신호위반 단속기준에 맞추어 딜레마 죤에 관하여 분석하였다. 본 연구에서 딜레마 죤은 최소정지거리($d_0$)가 최대통과거리($d_c$)보다 클 때 존재하는 것으로 보았으며, 그 차이만큼의 딜레마 죤이 발생하는 것으로 정의하였다. 이에 신호위반 단속을 함에 있어딜레마 죤의 영향을 최소화 할 수 있는 방안 등을 제시하였다. 그러나 각각의 방안을 개별적으로 적용시킬 경우 문제점이 발생하였다. 이러한 문제점을 해결하기 위해서 자기감응식 루프검지기의 위치를 재조정함에 있어 하나의 루프 검지기를 정지선 이후에 존재함과 동시에 황색신호시간을 재조정하거나, 자기감응식 루프검지기의 작동시간을 재조정하는 방안을 제시하고자 하였다. 본 연구에서는 3개의 교차로를 비교대상으로 선정하여 각각의 교통환경에 따른 접근로별 딜레마죤의 범위를 최소화하기 위한 대안을 제시하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.424-430
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• 2021

Determining the cause of a traffic signal violation is difficult if the drivers' claims are contradictory. In this study, the process of identifying signal violations using a simulation was presented based on cases. First, statements from the driver or witness whose cause of the signal violation is unclear were excluded. Second, the final position, final location, damaged area, steering status, braking status, and road surface traces of the vehicle were collected. The impact point was investigated from the stop line. Third, simulation data were modified and entered until the collision situation of the accident vehicle and the final stop position were met. Fourth, if the simulation results were consistent with the crash situation, the facts were verified by cross-validation to conform to the driver's statement. The results of the simulation showed that the Lexus entered the left turn signal in the intersection at approximately 55 km/h. In comparison, the Sonata driver saw the vehicle straight ahead at the intersection, entered the 72 km/h intersection, and collided with the Lexus. Therefore, the Sonata was identified as a signal violation, and the claims of the Sonata driver, witnesses, and police were contradictory.