PURPOSES : Under the Traffic Safety Act, the installation and management of transportation facilities (facilities and attachments necessary for the operation of transportation, such as roads, railways, and terminals) must take necessary measures to ensure traffic safety, such as enhancing safety facilities. Recently, railway operators have graded the congestion level inside railway stations and vehicles, addressing safety and convenience issues arising from congestion and providing this information to users. However, for bus-related transportation facilities (such as bus stops, terminals, and transfer facilities), criteria and related research for assessing traffic congestion are lacking. Therefore, this study developed a model for the congestion risk factors of four bus-related transportation facilities and proposed criteria for classifying congestion risk levels. METHODS : This study involved selecting congestion risk influence variables for each traffic facility through field surveys, calculating congestion risk index values through evacuation and pedestrian simulations, and constructing a congestion risk influence model based on the ridge model. RESULTS : The factors influencing congestion were selected to include the number of people waiting, effective sidewalk width, and number of bus stops. As a result of developing congestion risk grades, the central bus stops were determined to be in a severe stage if the Average Waiting Time (AWT) was 2.7 or above. Roadside bus stops were considered severe at 4.2, underground metropolitan transit centers at 3.7, and bus terminals at 5.9 or above. CONCLUSIONS : This study can help establish a foundation for a safety management system for congested areas in transportation facilities. When the congestion risk prediction results correspond to cautionary or severe levels, measures that can reduce congestion risk must be applied to ensure the safety of road users.

PURPOSES : This study aims to develop a congestion mitigation strategy at lane drop bottleneck with low Connected and Automated Vehicle (CAV) penetration. METHODS : The proposed strategy is designed to assign a role of a moving bottleneck to CAVs to reduce low-speed lane changes at bottleneck locations, which are the main cause of bottleneck capacity drop. Through this, it aims to induce proactive upstream lane changes for Human-Driven Vehicles (HDVs,). Therefore, this study includes the control algorithm for CAVs, and the evaluation of the strategy assumes penetration rates of 5% and 10% in a Microsimulation VISSIM environment. The assessment is conducted by comparing the capacity drop and total travel time. Additionally, a sensitivity test for the parameter of the CAV control algorithm, reduced speed, is performed to find the optimal parameter. RESULTS : In this study, three scenarios, a) Base, b) CAV with no control, and c) CAV with control, are designed to evaluate the effects of the CAV control strategy. Analysis of segment density and lane change distribution reveals that the control strategy effectively prevented vehicle congestion due to the bottleneck effect. Additionally, the analysis of capacity changes before and after the bottleneck and total travel time shows the effectiveness of the control strategy. The sensitivity test on CAV control speed emphasized the importance of selecting an appropriate speed for maintaining efficient traffic flow. Lastly, as the CAV penetration rate increased, the control strategy exhibited greater effectiveness in mitigating capacity drop. CONCLUSIONS : The proposed strategy is intended for use at low CAV penetration rates and is expected to provide assistance in mitigating congestion at bottlenecks during the early stages of CAV commercialization. Furthermore, since the role of CAV in the strategy can be performed by CVs or even HDVs, it can be applied not only immediately but also in the near future.

PURPOSES : It is necessary to implement traffic-control strategies for underground roads. In this study, the application criteria for traffic control were developed to minimize actual traffic congestion on underground roads before it occurs. In particular, the traffic congestion judgement criteria and procedure (TJCAP) were developed. They can specifically classify the possibility of traffic congestion underground. METHODS : A microscopic traffic simulation model was used to analyze different scenarios. With the scenario simulation results, a hierarchical clustering analysis was applied to produce quantitative values from the TJCAP for each experimental network case. RESULTS : For network case (a), it was concluded that the possibility of traffic congestion on underground roads increases when the speed of the ground road connected to the main underground road and the connected ground road after the outflow of the ramp section is low. When the connected road is an interrupted facility after entering the underground roads, the red time is long, and when the section travel speed is 15 km/h, the possibility of traffic congestion underground is highest. A cluster analysis based on these results was performed using two techniques (elbow and silhouette) to verify the final classification. CONCLUSIONS : The TJCAP were designed to operate traffic flow with stricter criteria than traffic congestion management on ground roads. This reflects the difference in the driving environment between underground and above-ground roadways.

항로에서의 위험도 평가 모델은 해상 교통량을 기초로 다양한 형태의 수학적 분석 방법 등이 응용되고 있다. 국내 해상교통안 전진단에서는 항로를 통항하는 선박 규모를 표준화시킨 해상교통혼잡도 모델을 활용하고 있으며, 해상교통혼잡도가 높으면 충돌과 같은 위험상황이 발생할 개연성이 높다고 해석하고 있다. 그러나 항로의 특정 지점에서 관측된 해상 교통량의 밀도 변화가 항로의 위험도를 표현할 수 있는지 보다 면밀한 과학적 검토가 필요하다고 판단된다. 본 연구에서는 항로에서의 충돌 및 좌초 등의 위험도를 확률적 기법으로 평가하는 IWRAP Mk2(IALA 공식 추천 평가모델) 모델로 항로 위험도를 체계적으로 평가하고, 동일 해역에서 해상교통혼잡도 모델로 해상교통혼잡도를 평가하여 항로 위험도와 해상교통혼잡도의 연관성을 분석하였다. 분석 결과, R2이 0.943인 선형함수가 도출되었으며, 유의수준에서도 유의성이 있는 것으로 분석되었다. 또한 Pearson 상관계수가 0.971로 높게 나타나 강한 정적 상관관계를 보였다. 이처럼 각각의 수학모델의 공통적인 입력 변수의 영향으로 항로 위험도와 해상교통혼잡도는 강한 연관성을 가지는 것으로 확인되었다. 이러한 연구 결과를 기반으로 항로 위험도를 예측할 수 있는 평가 기법이 고도화될 수 있는 모델 개발을 위한 응용 자료로 활용되기를 기대한다.

PURPOSES : In this study, analyze the characteristics of IOC indicator 'threshold' which is needed when evaluating the traffic signal operation status with ESPRESSO in various grade road traffic environment of Seoul metropolitan city and derive suggested value to use in field practice. METHODS : Using the computerized database program (Postgresql), we extracted data with regional characteristics (Arterial, Collector road) and temporal characteristics (peak hour, non-peak hour). Analysis of variance and Duncan's validation were performed using statistical analysis program (SPSS) to confirm whether the extracted data contains statistical significance. RESULTS: The analysis period of the main and secondary arterial roads was confirmed to be suitable from 14 days to 60 days. For the arterial, it is suggested to use 20 km/h as the critical speed for PM peak hour and weekly non peak hour. It is suggested to use 25 km/h as the critical speed for AM peak hour and night non peak hour. As for the collector road, it is suggested to use 20 km/h as the critical speed for PM peak hour and weekly non peak hour. It is suggested to use 30 km/h as the critical speed for AM peak hour and night non peak hour. CONCLUSIONS : It is meaningful from a methodological point of view that it is possible to make a reasonable comparative analysis on the signal intersection pre-post analysis when the signal operation DB is renewed by breaking the existing traffic signal operation evaluation method.

Kathmandu is the capital city of Himalayan country Nepal. It is a bowl shaped small valley with 3.5 Million populations. With approximately 5000 lane- km of roads and about 40% of entire vehicle populations of the country plying here without any grade separation, it has become quite challenging to manage the smooth flow of traffic in the city. The population of motor bikes is 75% making the job of traffic management difficult. Lack of foot path and bi cycle lanes for pedestrians and NMT and the narrow streets with manually controlled traffic intersections further complicate the deteriorating traffic flow. The improvement efforts including widening of critical roads could produce short term results only. A project named Kathmandu valley Sustainable Urban Transport Project, KSUTP funded by ADB could not progress as desired. This paper discusses the problems, studies and measures being undertaken to resolve these issues.

어떤 해역의 해상교통혼잡도를 평가하는 데 있어서 단위 시간당 항행 척수인 교통량을 분석하는 것보다 어떤 시간 단면에 존재하는 단위 면적당의 밀집도 분석을 활용하는 것이 합리적일 수 있다. 본 연구에서는 해상교통안전진단 대행기관의 해상교통혼잡도 평가기법을 표준화하고, 선박톤급별 환산교통량 사용으로 인한 평가오차를 최소화하기 위하여 새로운 방안을 찾고자 한다. 이를 해결하기 위해 선박자동식별장치(Automatic Identification System, AIS)의 통항선박 데이터를 활용하여 항로구간면적 대비 식별된 개개의 통항선박이 갖고 있는 점용영역의 면적을 합산한 값과의 백분율을 해상교통혼잡도로 평가하는 방안을 제시하였다. 새로운 모형에서는 정보통신기술의 획기적인 발달로 인해 실제 데이터 사용이 가능하여 환산 데이터에 의한 오차발생을 줄일 수 있고, 항로구간별 해상교통혼잡도 평가도 가능하게 되었다.

본 연구에서는 계절별 혼잡도 변화를 검토하기 위해 1년 동안의 주요 연안 통항로 및 항만 입출항로를 대상으로 계절별 기상특보가 발효되지 않은 1주일간의 GICOMS Data를 바탕으로 혼잡도 평가를 실시하였다. 그 결과 시간당 평균 혼잡도의 계절별 차이는 최대 약 11 %, 평균 약 3.5 %, 피크시간 혼잡도의 계절별 차이는 최대 약 82 %, 평균 약 30 %를 보이는 것으로 분석되었다. 향후 혼잡도 평가시에 이러한 계절별 혼잡도 변화를 감안하여야 하며, 특히 해상교통안전진단에서의 평가 시에는 이러한 계절별 차이가 존재하므로 혼잡여부에 대한 해상교통 안전대책 마련에 더욱 주의를 기울여야 할 것이다.

PURPOSES: This study was initiated to estimate expressway traffic congestion costs by using Vehicle Detection System (VDS) data. METHODS : The overall methodology for estimating expressway traffic congestion costs is based on the methodology used in a study conducted by a study team from the Korea Transport Institute (KOTI). However, this study uses VDS data, including conzone speeds and volumes, instead of the volume delay function for estimating travel times. RESULTS : The expressway traffic congestion costs estimated in this study are generally lower than those observed in KOTI's method. The expressway lines that ranked highest for traffic congestion costs are the Seoul Ring Expressway, Gyeongbu Expressway, and the Youngdong Expressway. Those lines account for 64.54% of the entire expressway traffic congestion costs. In addition, this study estimates the daily traffic congestion costs. The traffic congestion cost on Saturdays is the highest. CONCLUSIONS : This study can be thought of as a new trial to estimate expressway traffic congestion costs by using actual traffic data collected from an entire expressway system in order to overcome the limitations of associated studies. In the future, the methodology for estimating traffic congestion cost is expected to be improved by utilizing associated big-data gathered from other ITS facilities and car navigation systems.

In this study, we investigated the concentrations of PM10 and CO2 in public transportation vehicles (express bus, train, KTX, and subway) reported by previous indoor air quality (IAQ) surveys carried out from 2005 to 2013 in Korea. The number of valid data for PM10 was 566 and for CO2 was 579, and all data were classified according to whether it was collected during rush-hour or non rush-hour. PM10 and CO2 concentrations in subway cabin during the rush-hour were 1.3 and 1.45 times higher, respectively, than those of non rush-hour (p<0.05) in terms of geometric mean value. PM10 and CO2 concentration of express bus and train during the rush-hour also were 1.23 times higher than those of non rush-hour with relatively weak correlations (p=0.246). Among all PM10 concentrations, 16.9% and 3.8% of PM10 concentrations exceeded the IAQ guidelines (200 μg/m3 for non-rush hour and 250 μg/m3 for rush-hour), respectively. In terms of CO2 concentrations, 10.5% and 3.0% of them exceeded the IAQ guidelines (2,500 ppm for non rush-hour and 3,000 ppm for rush-hour), respectively. As a result, concentrations of PM10 and CO2 were estimated to be dominantly influenced by the operation characteristics of public transportation, such as degree of congestion and type of vehicle. In order to improve the IAQ of public transportation vehicles, specific air purification and ventilation systems are needed, depending on the characteristics of public transportation vehicles.