• International Journal of Highway Engineering
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• v.10 no.1
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• pp.31-39
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• 2008

Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. The accurate traffic accident prediction model requires not only understanding of the factors that cause the accident but also having the transferability of the model. So, this paper suggest the traffic accident diagram using CART(Classification And Regression Tree) analysis, developed Model is compared with the existing accident prediction models in order to test the goodness of fit. The results of this study are summarized below. First, traffic accident prediction model using CART analysis is developed. Second, distance(D), pedestrian shoulder(m) and traffic volume among the geometrical factors are the most influential to the traffic accident. Third. CART analysis model show high predictability in comparative analysis between models. This study suggest the basic ideas to evaluate the investment priority for the road design and improvement projects of the traffic accident blackspots.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2473-2481
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• 2013

The two characteristics of two-lane-and-two-way traffic flow are platoon and overtaking triggered by low-speed vehicle. It is crucial to develop a robust model which simultaneously generates the behaviors of platoon by low-speed vehicle and overtaking using opposite lane. Hence, a microscopic two-lane and two-way vehicle model was introduced (B. Yoon, 2011), which is based on CA (Cellular Automata) which is one of discrete time-space models, in Korea. While the model very reasonably explains the behaviour of overtaking low-speed vehicle in stable traffic flow below critical density, it has shortcomings to the overtaking process in unstable traffic flow above the critical density. Therefore, the objective of this study is to develope a vehicle model to more realistically explain overtaking process in unstable traffic flow state based on the model developed in the previous study. The experimental results revealed that the car-following model robustly generates the various macroscopic relationships of traffic flow generating stop-and-go traffic flow and the overtaking model reasonably explains the behaviors of overtaking under the conditions of both opposite traffic flow and stochastic parameter to consider overtaking in unstable traffic flow state. The vehicle model presented in this study can be expected to be utilized for the analysis of two-lane-and-two-way traffic flows more realistically than before.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1729-1740
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• 2017

This paper describes a vision-based method that effectively recognize a traffic light. The method consists of two steps of traffic light detection and discrimination. Many related studies have used color information to detect traffic light, but color information is not robust to the varying illumination environment. This paper proposes a new method of traffic light detection based on intensity and saturation. When a traffic light is turned on, the light region usually shows values with high saturation and high intensity. However, when the light region is oversaturated, the region shows values of low saturation and high intensity. So this study proposes a method to be able to detect a traffic light under these conditions. After detecting a traffic light, it estimates the size of the body region including the traffic light and extracts the body region. The body region is compared with five models which represent specific traffic signals, then the region is discriminated as one of the five models or rejected as none of them. Experimental results show the performance of traffic light detection reporting the precision of 97.2%, the recall of 95.8%, and correct recognition rate of 94.3%. These results shows that the proposed method is effective.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.368-373
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• 2005

This paper presents a new framework for the traffic flow control based on integrated model descriptions as the Hybrid Dynamical System (HDS).

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.257-263
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• 2016

Mombasa is the principle port of Kenya, serving inland countries in Eastern and central Africa. Mombasa port has undergone a massive infrastructure upgrade and dredging works with an expectation that more vessels and large post Panamax ships will be able to enter Mombasa port. Therefore, it is vital to carry out a marine traffic risk assessment in order to quantify the degree of navigation safety needed in the Mombasa approach channel and also to evaluate the navigation risk imposed on transit traffic by local ferry traffic. In this paper, a marine traffic risk assessment is carried out using the IWRAP mk2, Environmental Stress (ES) model, and the PARK model. Risk assessment results show that Likoni area has an unacceptable stress/risk ranking at 20.7% by the ES model and 38.89% by the PARK model. The IWRAP mk2 model shows that the crossing area has the highest risk of crossing collision and the area at the entrance to the inner channel has a high risk of grounding. The conclusions derived from this study will provide the basis for proposing the most effective countermeasure to improve navigation safety in the Mombasa approach channel.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.81-84
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• 2016

Mombasa is the principle port of Kenya, serving hinter countries in Eastern and central Africa. Mombasa port has undergone a massive infrastructure upgrade and dredging works with an expectation that more vessels and large post Panamax ships will be able to call at Mombasa port. Therefore, it is vital to carry out a marine traffic risk assessment so as to quantify the degree of navigation safety on Mombasa approach channel and also to evaluate navigation risk imposed on transit traffic by local ferry traffic. In this paper marine traffic risk assessment is carried out using IWRAP mk2, Environmental Stress model, and PARK model. Risk assessment results show that Likoni area has unacceptable stress/ risk ranking at 20.7% on ES model and 38.89% by PARK model. IWRAP mk2 model shows that crossing area has the highest risk of crossing collision and the area at the entrance to inner channel has a high risk of grounding. The conclusions derived from this study will provide the basis for proposing the most effective countermeasure so as to improve navigation safety in Mombasa approach channel.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1038-1046
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• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.587-593
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• 2018

The current road traffic noise prediction programs of Korea, which are widely used, are based upon foreign prediction model. Thus, it is necessary to verify foreign prediction models to find out whether they are suitable for the domestic road traffic environment. In addition, an analysis and an in-depth study on the main factors should be conducted in advance as the influence factors on the occurrence of traffic noise vary for each prediction model. Therefore, this study examined the influence factors and the existing prediction models used to forecast road traffic noise. Also, analyzed their relationship with the factors influencing the noise generated by driving vehicles through multiple regression analysis using a prediction model, taking into consideration of the traffic environment and the road geometric structure. In addition, this study will apply experimental values to the existing road traffic noise prediction model (NIER, RLS-90) and the deducted road traffic noise prediction model. As a result, the order of the absolute value sum of the errors are NIER, RLS-90, model value. Through comparison and verification, developed models are to be analyzed for providing basic research results for future study on road traffic noise prediction modeling.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2B
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• pp.217-227
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• 2004

With a rapid growth in the Internet technology, the network traffic is increasing swiftly. As for the increase of traffic, it had a large influence on performance of a total network. Therefore, a traffic management became an important issue of network management. In this paper, we study a forecast plan of network traffic in order to analyze network traffic and to establish efficient correspondence. We use time series forecast models and determine fitness whether the model can forecast network traffic exactly. In order to predict a model, AR, MA, ARMA, and ARIMA must be applied. The suitable model can be found that can express the nature of traffic for the forecast among these models. We determines whether it is satisfied with stationary in the assumption step of the model. The stationary can get the results by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). If the result of this function cannot satisfy then the forecast model is unsuitable. Therefore, we are going to get the correct model that is to satisfy stationary assumption. So, we proposes a way to classify in order to get time series materials to satisfy stationary. The correct prediction method is managed traffic of a network with a way to be better than now. It is possible to manage traffic dynamically if it can be used.