• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.1
• /
• pp.9-21
• /
• 2009

This study presents a useful heuristic algorithm to classify vehicle classes using vehicle length information, which is extracted from inductive loop vehicle signatures. A high-speed scanning equipment was used to extract more detailed change of inductance magnitude for individual vehicles. Vehicle detection time and individual vehicle speeds were used to derive vehicle length information that is an input of the proposed algorithm. The spatial and temporal transferability tests were further conducted to evaluate algorithm. The spatial and temporal transferability tests were further conducted to evaluate algorithm performance more systematically. It is expected that the proposed method would be useful for obtaining vehicle classification information from wide-spread existing loop infrastructure.

• Journal of Korean Society of Transportation
• /
• v.28 no.1
• /
• pp.39-49
• /
• 2010

Although many studies have been carried out on the pattern of behavior of drivers which result from the provision of traffic information, there have been few detailed studies on the composition of message, method for message expression, timing of provision, point of provision, media for provision, changes by traffic condition, etc. This study was intended to provide an insight into the changes in the characteristics related to the provision of information by analyzing how the patterns of information utilization change depending on the traffic condition and reclassifying such patterns according to the characteristics of media. Unlike the existing studies, this study adopted the traffic condition, using rate of information media, and the correlation coefficient label as the basis for information media classification, and categorized them into passive utilization media, active utilization media, and past experience in order to ensure the statistical reasonability. The categorized using rate of information media and traffic condition was found to have a positive(+) correlation with the travel speed in the case of passive utilization media during both consecutive holidays(Korea's traditional Thanksgiving day) and weekends, but had a negative(-) correlation with the positive utilization media and past experience. The rate of decision to take a detour based on the past experience was high at the condition of congestion or slow during both consecutive holidays and weekends, but the rate of decision to take a detour through passive utilization media was high in a smooth traffic. In other words, if the traffic condition worsens, using rate of passive utilization media would be low while the diversion rate would be high which uses the active utilization media and past experience. Therefore, it should be established to suit the traffic condition and media characteristics for strategies of traffic distribution through drivers' diversion behavior on weekends and consecutive holidays.

• Journal of Industrial Convergence
• /
• v.17 no.4
• /
• pp.45-51
• /
• 2019

In this study, climate change considerations are an important measure to create a resilient city that conforms to the principle of sustainable development that balances the economic, social and environmental harmony of a country or city and can preserve its size, function and characteristics to the maximum extent possible. In this regard, the public transportation system being built and operated in Dublin City, Ireland, illustrates the city's system through green traffic. Therefore, based on the urban-based conditions equipped by Dublin, Ireland, in that Dublin is realizing preemptive mass-traffic-oriented urban development (TOD) considering climate change, the purpose of the study was to classify the methods of research as internal (strong and weak) and external (opportunity and threat) factors through SWOT analysis and to present mass-oriented urban development strategies and policy implications.

• Knowledge Management Research
• /
• v.25 no.1
• /
• pp.217-233
• /
• 2024

The demand for two-wheelers has increased in recent years, driven by the growing delivery culture, which has also led to a rise in the number of two-wheelers. Although two-wheelers are economically efficient in congested traffic conditions, reckless driving and ambiguous traffic laws for two-wheelers have turned two-wheeler accidents into a significant social issue. Given the high fatality rate associated with two-wheelers, the severity and risk of two-wheeler accidents are considerable. It is, therefore, crucial to thoroughly understand the characteristics of two-wheeler accidents by analyzing their attributes. In this study, the characteristics of two-wheeled vehicle accidents were categorized using the K-prototypes algorithm, based on data from two-wheeled vehicle accidents. As a result, the accidents were divided into four clusters according to their characteristics. Each cluster showed distinct traits in terms of the roads where accidents occurred, the major laws violated, the types of accidents, and the times of accident occurrences. By tailoring enforcement methods and regulations to the specific characteristics of each type of accident, we can reduce the incidence of accidents involving two-wheelers in metropolitan areas, thereby enhancing road safety. Furthermore, by applying machine learning techniques to urban transportation and safety, this study adds to the body of related literature.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2013.06a
• /
• pp.521-523
• /
• 2013

해상교통안전진단 제도 시행관련, 경과 진행에 따른 실적 분석 및 소요 분석을 통한 개선점 도출이 현 시점에는 시행된 바가 없는 상태로 제도의 원활한 운영 및 개선점 발굴을 위해선 연구 필요성이 있다. 따라서 수행된 진단 실적, 사업별 특성, 진단 특성을 분석하고 장래 진단 수요량을 예측 분석하여 진단 수행에 따른 개선점을 도출하고 장래 상황 예측을 통한 해상교통안전진단 효율화 자료로 사용하고자 한다. 그에 따라 본 연구에서는 진단실적 분석, 진단수행 개선점 분석, 장래 진단 수요분석, 진단 수요에 따른 개선점 분석의 4단계로 분류하고 관련 내용을 연구, 분석, 검토 기술하였다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.6
• /
• pp.1160-1166
• /
• 2006

The information of vehicle is very important for maintaining traffic order under the present complex traffic environments. This paper proposes a new vehicle plate recognition mechanism that is essential to know the information of vehicle. The proposed method uses SVM which is excellent object classification compare to other methods. Two-class SVM is used to find the location of vehicle plate and multi-class SVM is used to recognize the characters in the plate. As a real-time processing system using multi-step image processing and recognition process this method recognizes several different vehicle plates. Through the experimental results of real environmental image and recognition using the proposed method, the performance is proven.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.1
• /
• pp.49-54
• /
• 2024

Data imbalance is a common issue encountered in classification problems, stemming from a significant disparity in the number of samples between classes within the dataset. Such data imbalance typically leads to problems in classification models, including overfitting, underfitting, and misinterpretation of performance metrics. Methods to address this issue include resampling, augmentation, regularization techniques, and adjustment of loss functions. In this paper, we focus on loss function adjustment, particularly comparing the performance of various configurations of loss functions (Cross Entropy, Balanced Cross Entropy, two settings of Focal Loss: 𝛼 = 1 and 𝛼 = Balanced, Asymmetric Loss) on Multi-Class black-box video data with imbalance issues. The comparison is conducted using the I3D, and R3D_18 models.

• Journal of Korean Society of Transportation
• /
• v.28 no.2
• /
• pp.151-159
• /
• 2010

This paper presents a research result that was performed to develop a more accurate freeway crash prediction model than existing models. While the existing crash models only focus on developing crash relationships associated with highway geometric conditions found on a short section of a crash site, this research applies a different approach considering the upstream highway geometric conditions as well. Theoretically, crashes occur while motorists are in motion, and particularly at freeways vehicle speed at one specific point is very sensitive to upstream geometric conditions. Therefore, this is a reasonable approach. To form the analysis data base, this research gathers the geometric conditions of the West Seaside Freeway 269.3 km and six years crash data ranging 2003-2008 for these freeway sections. As a result, it is found that crashes fit well into Negative Binomial Distribution, and, based on the developed model, total number of crashes is inversely proportional to highway curve length and radius. Contrarily, crash occurrences are proportional to tangent length. This result is different from existing crash study results, and it seems to be resulted from this research assumption that a crash is influenced greatly by upstream geometric conditions. Also, this research provides the expected effects on crash occurrences of the length of downgrade sections, speed camera placements, and the on- and off- ramp presences. It is expected that this research result is useful for doing more reasonable highway designs and safety audit analysis, and applying the same research approach to national roads and other major roads in urban areas is recommended.

• Journal of KIISE:Computer Systems and Theory
• /
• v.30 no.11
• /
• pp.610-620
• /
• 2003

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

• Journal of Platform Technology
• /
• v.12 no.3
• /
• pp.62-70
• /
• 2024

Recent surveillance systems employ multiple sensors, such as cameras and radars, to enhance the accuracy of intrusion detection. However, object recognition through camera (RGB, Thermal) sensors may not always be accurate during nighttime, in adverse weather conditions, or when the intruder is camouflaged. In such situations, it is possible to detect intruders by utilizing the trajectories of objects extracted from camera or radar sensors. This paper proposes a method to detect intruders using only trajectory information in environments where object recognition is challenging. The proposed method involves training an LSTM-Attention based trajectory classification model using normal and abnormal (intrusion, loitering) trajectory data of animals and humans. This model is then used to identify abnormal human trajectories and perform intrusion detection. Finally, the validity of the proposed method is demonstrated through experiments using real data.