• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.1-15
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• 2023

In this study, the number of truck traffic accidents was predicted by using Poisson and negative binomial regression analysis to understand what factors affect accidents using expressway data. Significant variables in the truck traffic accident prediction model were continuous driving time, link length, truck traffic volume. number of bridges and number of drowsy shelters. The calculated LOSS rating was expressed on the national expressway network to diagnose the risk of truck accidents. This is expected to be used as basic data for policy establishment to reduce truck accidents on expressways.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.55-76
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• 2000

We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.207-215
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• 2009

Generally, traffic accidents can be influenced by variables driving conditions including geometric, roadside design, and traffic conditions. Under the circumstance, homogeneous roadway segments were firstly identified using typical geometric variables obtained from field data collections in this study. These field data collections were conducted at highways located in several areas having various regional conditions for examples, outside metropolitan city; level and rolling rural areas. Due to many zero cells in crash database, a Zero Inflated Poisson model was used to develop crash prediction model to overestimated results in this study. It was found that EXPO, radius, grade, guardrail, mountainous terrain, crosswalk and bus-stop have statistically significant influence on vehicle to vehicle crashes at rural multi-lane roadway segments.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.2
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• pp.186-192
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• 2014

In this paper, we analysed vessel traffic volume and patterns of traffic flow for ships using areas where included wind farm site and adjacent waters of Daejeong Offshore Wind Farm, and estimated traffic volume by classified navigational routes according to suggestion of rational routeing measures on the basis of classified patterns after installation of offshore wind facilities. Also, we assessed vessel traffic safety for each designed routeing measures on the basis of estimated traffic volume and proposed requisite countermeasures for the safe navigation of ships. With a result of analysing patterns of traffic flow, the current traffic flow was classified by 8 patterns and the annual traffic volume was predicted to 8,975 ships. On the basis of these, expected the vessel traffic volume according to designed four routeing mesaures after installation of wind farm. As result of assessing vessel traffic safety by using powered-vessel collision model of SSPA on the basis of the estimated traffic volume, the value of collision probability was less than safe criteria $10^{-4}$. Thereby we made sure usability of the designed routeing measures for the safe navigation of ships.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.49-58
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• 2004

The first and perhaps the most critical and perhaps the most important step in the process of predicting future traffic volume in a region (Zone) is to estimate the number of trips generated in from each traffic analysis zone. Most trip generation models for urban transportation planning, and highway in Korea are regression models. In Korea the category analysis has not been tried for last decades since the proper data such as the household travel behavior data have not been collected. Recently, the comprehensive household travel behavior survey such as ${\ulcorner}$1996 The Household Travel Behavior Survey${\lrcorner}$, ${\ulcorner}$2002 The Household Travel Behavior Survey${\lrcorner}$ has been done. In this paper, the cross-classification tables of Seoul Metropolitan Area including the City of Seoul and Kyonggi Province are estimated by the category analysis. The tables are compared with regression models and ${\ulcorner}$2002 The Household Travel Behavior Survey${\lrcorner}$ data in terms of predictive capabilities in Seoul Metropolitan Area. Improvement strategies for trip generation forecast in Seoul Metropolitan Area are proposed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.377-380
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• 2004

본 논문에서는 기존의 교통 상황 검지 장비들이 가지고 있는 문제점들을 해결하기 위해 실제 통행속도 데이터의 해당 도로 속성들을 이용하여 데이터 마이닝을 통한 합리적인 오차범위 내에서의 실시간 교통 정보 예측 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘은 데이터 파이닝의 분석 기법중 하나인 신경망(Neural Network)분석을 통하여 통행 속도 예측 근사 모델을 개발하는 것이며, 기존의 교통 상황 판단 알고리즘과의 결과 비교를 통해 비용 절감 효과와 속도 정보가 없는 도로까지의 합리적인 통행 속도 예측, 그리고 Off line상에서의 시간대별 교통 정보 제공이 가능함을 보인다.

• Journal of Korean Society of Transportation
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• v.16 no.4
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• pp.53-64
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• 1998

본 연구의 목적은 '96년말 서울시에서 실시한 가구통행조사를 이용하여 서울시 수단선택모형을 구축하고 그 예측결과를 남산 혼잡통행료 전후저사자료와 비교하여 보다 구체적으로 그 정확성을 검증한 뒤 향후 서울시 교통수요관리 방안의 시행에 따른 수단선택변화 예측의 기본 모형으로 활용하는데 있다. 5가지의 대안모형의 분석결과 통행비용변수(승용차의 경유 주차요금포함)와 총통행시간변수(OVTT와 IVTT의 합), 승용차, 지하철, 택시상수로 구성된 모형이 최적모형으로 분석되었다. 이모형에 의한 시간가치는 9,395원, 승용차의 비용탄력성은-0.6767로서 기존 연구결과의 범위 내에 속한 것으로 나타났다. 최적모형을 이용하여 승용차통행비용이 증가한 경우를 모사분석결과 남산1,3호 터널 혼잡통행료 징수효과와 유사하게 승용차 분담율이 13% 가까이 감소한 것으로 나타나서 모형의 현실적합성도 비교적 높은 것으로 판명되었다. 향후 본 연구에서 선정된 최적수단선택모형을 통행배정모형과 결합하여 다양한 교통수요관리 방안에 따른 효과를 예측하는데 활용하면 서울과 같은 대도시의 단기적 교통관리의 수준을 한 단계 높이는데 기여할 것으로 판단된다.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.123-135
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• 2007

The objective of this study is to analyze the relationship between traffic accidents occurring at trumpet interchange ramps according to accident type as well as the relevant factors that led to the traffic accidents, such as geometric design elements and traffic volumes. In the process of analysis of the distribution of traffic accidents, negative binomial distribution was selected as the most appropriate model. Negative binomial regression models were developed for total trumpet interchange ramps, direct ramps, loop ramps and semi-direct ramps based on the negative binomial distribution. Based upon several statistical diagnostics of the difference between observed accidents and predicted accidents with four previously developed models, the fit proved to be reasonable. Understanding of statistically significant variables in the developed model will enable designers to increase efficiency in terms of road operations and the development of traffic accident prevention policies in accordance with road design features.