In this study, we propose an adaptive traffic control method that utilizes predictions of near-future traffic arrivals at a signalized intersection based on real-time data collected at an upstream intersection to design acyclic traffic signal timing accordingly. The proposed adaptive control method utilizes a deep learning model developed in this study to predict future traffic arrivals at downstream intersections 24 s ahead based on upstream intersection data at 4 s decision intervals. Using the predicted arrival traffic volume, signal timings were designed to minimize delays. A rolling-horizon approach was employed to correct the prediction errors during this process. The performance of the proposed traffic signal control method was validated by comparing it with the traditional time-of-day (TOD) traffic signal operation method over a 24 h period. The results of comparative validation tests conducted through simulations in a virtual environment indicate that the proposed adaptive traffic control system operates efficiently to minimize average control delays. During the morning peak period, a reduction time of 43.19 s per vehicle (57.02%) was observed, whereas the afternoon peak period exhibited a reduction of 37.91 s per vehicle (48.35%). Additionally, data analysis revealed that the optimal phase length suggested by the pre-timed method, which assumes uniform vehicle arrivals, is statistically identical at a 95% confidence level to the average phase length of the adaptive traffic control system, which assumes random vehicle arrivals. This study confirms the necessity of adopting proactive real-time signal control systems that utilize a new traffic information collection method to respond to dynamic traffic conditions and move away from conventional TOD signal operation, which primarily focuses on peak commuting hours. Additionally, it confirms the need for a fundamental shift in the underlying philosophy traditionally used in traffic signal design

This study aimed to develop a pavement management system suitable for the climate and traffic characteristics of Gangwon Province. This research focused on analyzing the asphalt pavement performance characteristics of national highways in Gangwon Province by region and developing prediction models for the current pavement performance and annual changes in performance. Quantitative indicators were collected to evaluate the condition of national highway pavements in Gangwon Province, including factors affecting road performance, such as weather data and traffic volume. The Gangwon region was then classified according to its topography, climate, weather, traffic volume, and pavement performance. Prediction models for the current pavement performance and annual changes in performance were developed for national highways. This study also compared the predicted values for the Gangwon region using a nationwide pavement performance-prediction model from other studies with the predicted values from the developed annual changes in the performance prediction model. This study established a foundation for implementing a pavement management system tailored to the unique climate and traffic characteristics of Gangwon Province. By developing region-specific performance prediction models, this study provided valuable insights into more effective and efficient pavement maintenance strategies in Gangwon Province.

기존 신호제어기법은 과거 주기에 파악된 교통상황을 바탕으로 다음 주기의 교통신호시간을 설계하는 방식으로 신호시간을 설계하기 위해 관측할 때의 교통상황과 신호시간을 제공받는 교통상황 간의 간극이 존재하였다. 또한, 설정된 주기길이 동안 차량이 교차로에 일정하게 도착하는 균일분포를 가정하지만, 실제 교차로에 도착하는 교통량의 행태는 비 균일분포로 실제 교통수요에 대응하기 어렵 다는 한계가 존재한다. 본 연구는 이러한 한계를 극복하기 위해 교차로로 진입하는 상류 교차로의 교통정보를 활용하여 단기 미래 도 착 교통량 예측모델 개발을 통해 관측 시점과 제공 시점 간의 간극을 최소화한다. 또한, 기존 주기길이 동안의 교통량 도착분포를 비 균일분포로 가정하여 주기길이가 고정되지 않는 방식(Acyclic)의 적응식 신호제어 기법(ATC) 개발한다. 제안된 단기 미래 도착 교통 량 예측모델은 실제 스마트교차로 자료를 가공하여 시뮬레이션을 통하여 학습데이터를 구축하여 장단기 메모리(LSTM) 모형과 시간 분산(TimeDistributed) 모형을 적용하여 딥러닝 모델을 개발하였다. 적응식 교통신호제어 기법은 실시간 예측 교통량을 활용하여 교통 류별 예측 지체 산출을 통하여 지체가 최소화되는 현시 종료 지점에서 현시를 종료하고 다음 시간 단계에서 예측된 교통량을 통해 최 적 현시를 재산출하는 롤링 호라이즌(Rolling Horizon)을 수행한다. 제안 신호제어 기법의 평가를 위해 미시적 교통 시뮬레이션을 활 용하여 기존 신호제어 기법인 TOD 신호제어 기법과 제안기법 간의 평가를 수행하였다.

본 연구는 국내 도로사업의 교통수요 예측오차를 종합적으로 평가하고, 보다 효율적인 기대교통량 추정모형의 개발을 목적으로 수 행되었다. 이를 위해 본 연구에서는 1999년부터 2010년까지 수행된 예비타당성조사 및 타당성재조사 사업들 가운데 62건의 도로사업 (690개 구간)의 자료를 활용하였다. 본 연구의 주요 특징은 다음과 같다. 첫째, 기존 연구들과 달리 사업구간 뿐만 아니라 주변구간을 포함하여 교통수요 예측 오차를 평가했다는 점이다. 둘째, 본 연구는 교통수요 예측의 오차를 정확성, 추정편의, 추정연계성 등 다양한 평가지표를 활용하여 분석했다는 점이다. 실측자료를 통한 분석결과, 전체구간의 평균 백분율 오차(MPE)는 11.6%(과소추정)로 파악되 었지만, 이를 사업구간과 주변구간으로 나누어 살펴보면, 사업구간의 경우 -13.5%(과다추정), 주변구간은 16.5%(과소추정)로 상반된 결 과를 나타내었다. 추정편의 분석결과, 전체구간에서는 통계적으로 유의미한 편의가 발견되지 않았으나, 사업구간과 주변구간 각각에서 는 편의가 존재하는 것으로 나타났다. 추정연계성 분석에서는 주변구간의 경우 기준연도 정산 결과와 개통연도 오차 간 유의미한 관 계가 확인되었다. 이러한 분석결과를 바탕으로, 본 연구는 분위회귀모형을 활용한 기대교통량 추정모형을 제안하였는데, 이는 기존의 점 추정 방식의 한계를 보완하는 방안이다. 이 모형은 사업구간과 주변구간을 구분하여 개발되었으며, 실측교통량의 50% 분위를 중심 으로 95% 신뢰구간을 제시하였다. 또한, 동 모형에서는 고속도로 여부, 준공 지연 기간 등 주요 변수들의 영향을 고려하여 모형의 설 명력을 높였다는 특징을 갖는다. 본 연구의 결과는 도로사업의 교통수요 예측 정확성 향상과 투자 의사결정의 합리성 제고에 기여 할 수 있을 것으로 기대된다. 특히, 제안된 기대교통량 추정 모형은 예비타당성조사 등에서 보다 현실적인 교통수요 예측치를 제공하고, 이를 통해 경제성 분석의 신뢰도를 높이는 데 활용될 수 있을 것이다. 또한, 사업구간과 주변구간의 교통량 변화 특성이 다르다는 점 을 고려하여, 향후 도로 사업의 영향 평가 시 보다 세밀한 접근이 필요함을 시사한다.

PURPOSES : This study aimed to predict the number of future COVID-19 confirmed cases more accurately using public and transportation big data and suggested priorities for introducing major policies by region. METHODS : Prediction analysis was performed using a long short-term memory (LSTM) model with excellent prediction accuracy for time-series data. Random forest (RF) classification analysis was used to derive regional priorities and major influencing factors. RESULTS : Based on the daily number of COVID-19 confirmed cases from January 26 to December 12, 2020, as well as the daily number of confirmed cases in Gyeonggi Province, which was expected to occur on December 24 and 25, depending on social distancing, the accuracy of the LSTM artificial neural network was approximately 95.8%. In addition, as a result of deriving the major influencing factors of COVID-19 through random forest classification analysis, according to the number of people, social distancing stages, and masks worn, Bucheon, Yongin, and Pyeongtaek were identified as regions expected to be at high risk in the future. CONCLUSIONS : The results of this study can help predict pandemics such as COVID-19.

해양사고 예방을 위해서는 사고의 원인과 결과에 대한 분석 및 진단뿐만 아니라, 사고의 발생 패턴과 변화 추이를 예측함으로 써 정량적 위험도를 제시할 필요성이 있다. 선박교통과 관련된 해양사고 예측은 선박의 충돌위험도 분석 및 항해 경로 탐색 등 선박교통 의 흐름에 관한 연구가 주로 수행되었으며, 해양사고의 발생 패턴에 대한 분석은 전통적인 통계 분석에 따라 제시되었다. 본 연구에서는 해양사고 통계 자료 중 선박교통관련 사고의 월별, 시간대별 발생 현황 데이터를 활용하여 해양사고 발생 예측 모델을 제시하고자 한다. 국내 해양사고 발생 현황 중 월별, 시간대별 데이터 집계가 가능한 1998년부터 2021년까지의 통계자료 중 선박교통 관련 데이터를 분류하 여 정형 시계열 데이터로 변환하였으며, 대표적인 인공지능 모델인 순환 신경망 기반 장단기 기억 신경망을 통하여 예측 모델을 구축하 였다. 검증데이터를 통하여 모델의 성능을 검증한 결과 RMSE는 초기 신경망 모델에서 월별 52.5471, 시간대별 126.5893으로 나타났으며, 관측값으로 신경망 모델을 업데이트한 결과 RMSE는 월별 31.3680, 시간대별 36.3967로 개선되었다. 본 연구에서 제안한 신경망 모델을 기 반으로 다양한 해양사고의 특징 데이터를 학습하여 해양사고 발생 패턴을 예측할 수 있을 것이다. 향후 해양사고 발생 위험의 정량적 제 시와 지역기반의 위험지도 개발 등에 관한 추가 연구가 필요하다.

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

PURPOSES : This study aims to create a pleasant environment by exploring ITS technology-based reduction measures to manage vehicles on the road, which are the main cause of traffic noise, while identifying the effects of traffic noise and various noise reduction measures. METHODS : A review of the literature identified the matters discussed mainly by reviewing the pre-examination and related statutes of traffic noise management measures at home and abroad. Furthermore, in the field investigation section, the variables affecting traffic noise (traffic volume, large vehicle mix rate, and driving speed) were investigated and the noise impact was analyzed using the three-dimensional (3D) noise prediction model (SounpdPLAN). RESULTS: The noise impact levels of the 3D noise prediction model were identified from various angles, such as horizontal and vertical, and traffic noise management measures for pre-real-time management and related DB utilization measures were proposed. CONCLUSIONS: Unlike the existing traffic noise management measures, which focus on follow-up management measures, it is believed that further research is needed to develop standards and related guidelines that meet regional characteristics by taking into account the characteristics of traffic noise and creating concrete and drawing action plans that can be used in future policies using ITS technology.

PURPOSES : The purpose of this study is to compare applicability, explanation power, and flexibility of traffic accident models between estimating model using the statistical method and the machine learning method. METHODS: In order to compare and analyze traffic accident models between model estimated using the statistical method and machine learning method, data acquisition was conducted, and traffic accident models were estimated using statistical methods such as negative binomial regression model, and machine learning methods such as a generalized regression neural network (GRNN). Then, the fitness of model as R2, root mean square error (RMSE), mean absolute percentage error (MAPE), accuracy, etc., were determined to compare the traffic accident models. RESULTS: The results showed that the annual average daily traffic (AADT), speed limits, number of lanes, land usage, exclusive right turn lanes, and front signals were significant for both traffic accident models. The GRNN model of total traffic accidents had been better statistical significant with R2: 0.829, RMSE: 2.495, MAPE: 32.158, and Accuracy: 66.761 compared with the negative binomial regression model with R2: 0.363, RMSE: 9.033, MAPE: 68.987, and Accuracy: 8.807. The GRNN model of injury traffic accidents also showed similar results of model’s statistical significance. CONCLUSIONS: Traffic accident models estimated with GRNN had better statistical significance compared with models estimated with statistical methods such as negative binomial regression model.

Even though cars have a good effect on modern society, traffic accidents do not. There are traffic laws that define the regulations and aim to reduce accidents from happening; nevertheless, it is hard to determine all accident causes such as road and traffic conditions, and human related factors. If a traffic accident occurs, the traffic law classifies it as ‘Negligence of Safe Driving’ for cases that are not defined by specific regulations. Meanwhile, as Korea is already growing rapidly elderly population with more than 65 years, so are the number of traffic accidents caused by this group. Therefore, we studied predictive and comparative analysis of the number of traffic accidents caused by ‘Negligence of Safe Driving’ by dividing it into two groups : All-ages and Elderly. In this paper, we used empirical monthly data from 2007 to 2015 collected by TAAS (Traffic Accident Analysis System), identified the most suitable ARIMA forecasting model by using the four steps of the Box-Jenkins method : Identification, Estimation, Diagnostics, Forecasting. The results of this study indicate that ARIMA (1, 1, 0)(0, 1, 1)12 is the most suitable forecasting model in the group of All-ages; and ARIMA (0, 1, 1)(0, 1, 1)12 is the most suitable in the group of Elderly. Then, with this fitted model, we forecasted the number of traffic accidents for 2 years of both groups. There is no large fluctuation in the group of All-ages, but the group of Elderly shows a gradual increase trend. Finally, we compared two groups in terms of the forecast, suggested a countermeasure plan to reduce traffic accidents for both groups