This study evaluated the safety impact of automated traffic enforcement cameras targeting tailgating behavior at signalized intersections by comparing traffic conditions shortly after installation and one year later. The Kukkiwon intersection in Gangnam-gu, Seoul, South Korea was selected as the study site. Individual vehicle speeds, accelerations, and subsequent distances were extracted from video data using YOLOv8 and ByteTrack, which are advanced deep learning-based object detection and tracking algorithms. Surrogate safety measures (SSM), such as time to collision (TTC), modified time to collision (MTTC), and proportion of stopping distance (PSD), were calculated to assess changes in traffic safety. Every SSM indicated an improvement one year after the installation of enforcement cameras, suggesting a reduction in collision risks. In particular, the PSD indicator showed a notable improvement, reflecting a better maintenance of safe following distances. These results highlight the effectiveness of automated enforcement in improving intersection safety and suggest its scalability to other intersections with similar tail-gating issues. Future research should explore the long-term and multisite effects using diverse intersection types and behavioral indicators.

PURPOSES : This study aims to establish a performance measure to evaluate metropolitan transit centers from the perspectives of transportation and urban planning. The developed performance measure indicates the effectiveness of the metropolitan transit center in urban areas, suggesting a policy for design and urban development. METHODS : This study assesses the functionality of a transit center using a linear equation. Seven indicators representing the key functions of the transit center are employed to determine the efficiency of current status. We analyzed four transit centers–Cheongnyangni, Hapjeong, Sadang, and Seoul Station–where transfer centers are proposed owing to high traffic volumes. The coefficients are determined using the weights obtained from an analytic hierarchy process (AHP) survey. RESULTS : Application of the weights from the AHP survey to the indicators of each transit center reveals that overall Seoul Station scored the highest, whereas Cheongnyangni Station scored the lowest. In particular, Seoul Station performed better than other stations in terms of accessibility and simplified coverage area index (SCAI). Although Sadang Station slightly outperformed Hapjeong Station with respect to the total score, the variance was due to Hapjeong Station excelling in urban indicators despite its lower transportation metrics. Cheongnyangni Station scored low on most indicators despite significant physical investments, except for congestion, transfer time and floor area ratio. CONCLUSIONS : The AHP survey identified accessibility and SCAI as the most heavily weighted transportation-related indicators, while the floor area ratio, an urban development indicator, was the least weighted. Seoul Station, which excelled in accessibility and SCAI had the highest total score among the sites studied. However, locations with poorer transportation metrics but superior urban indicators can still function effectively as integrated metropolitan transit centers.

PURPOSES : This study explores the preference of shared autonomous vehicle service in an underground dedicated environment. METHODS : A stated preference survey was conducted to examine the mode choice behaviors on autonomous vehicle service competing with existing modes. Multinomial logit was employed to estimate the parameters of explanatory variables from the surveyed data. The model was estimated with alternative specific parameters rather than generic parameters. The value of time was also estimated using the parameters of the mode choice model. RESULTS : The results showed that the travel cost had the highest sensitivity to public transportation and the lowest to private cars. We also found that the value of the in-vehicle travel time was highest for private cars, lowest for public transportation, and intermediate for SAVs, suggesting that SAVs could serve as a premium public transport option. Additionally, the out-vehicle time coefficient was higher for public transportation compared to that for SAVs, indicating that users are more willing to tolerate longer out-vehicle times for SAVs due to their high-speed service compared to that of public transportation. CONCLUSIONS : This study presents a direction for policy regarding the adoption of shared autonomous vehicle services by considering the attributes that are valued by users of each mode.

PURPOSES : This study aims to calculate the estimation of travel time saving benefits from smart expressway construction by considering the willingness to pay for automated vehicles. METHODS : In this study, data were collected from 809 individual drivers through a stated preference survey. A multinomial logit model was constructed to analyze the choice behavior between arterial roads, expressways, and smart expressways. Through this, the values of time and benefits were estimated. RESULTS : The value of time was calculated at 19,379 won per vehicle per hour for arterial roads and expressways and 23,061 won per vehicle per hour for smart expressways. Applying these values to the Jungbu Naeryuk expressway, we evaluated the demand change and benefits resulting from the improvement to the smart expressways. The results show that the traffic volume on the Jungbu Naeryuk expressway is expected to increase by 4.7% to 20.7% depending on the changes in capacity. CONCLUSIONS : The travel time saving benefits are estimated as positive, resulting from the construction of smart expressways. The benefits resulting from the construction of new smart expressways are expected to be enhanced due to the anticipation of more significant time-saving effects.

수도권으로 인구가 집중되면서 광역 통행의 비중이 증가하게 되었다. 이러한 상황에서 서울시는 광역 통행에서 승용차 통행량을 줄 이고 대중교통의 편리성을 강화하여 대중교통이 광역 통행 수요를 분담하도록 하는 동시에 도심 주요 지역의 고밀복합개발을 통해 효 율적으로 도시 인프라를 개발하고자 하는 목적으로 서울시 여러 곳에 광역복합환승센터를 설치하였다. 본 연구는 이러한 복합환승센 터가 잘 기능하는지 평가하고 추후 다른 환승센터를 계획할 때에도 활용 가능한 평가 지표를 개발하고자 하였다. 평가를 위한 지표는 교통 기능 평가 지표 4가지, 도시 기능 평가 지표 3가지로 총 7가지의 지표를 선정하였으며, 환승센터마다 하나 의 점수로 환산 가능하도록 이 지표들을 하나의 선형식으로 통합하였다. 스마트카드 데이터로 환승시간, 광역교통분담률을 계산하였으 며, 교통카드 빅데이터 시스템상의 데이터를 통해 혼잡도와 접근성 평가 데이터를 추출하였다. 또한, 주변 지역 활성화도 관련 지표들 은 서울 열린 데이터 광장에서 취득하였다. 선형식의 각 변수의 계수는 서울시립대학교 교통공학과 구성원들을 대상으로 AHP 설문을 실시하여 얻은 지표별 가중치를 이용하여 결정하였다. 그 결과 광역복합환승센터 평가에 가장 큰 영향을 미치는 요소는 환승시간과 혼잡도였으며, 가장 적은 영향을 미친 요소는 용적률 활용도였다. 또한 완성된 선형식으로 서울역과 청량리역 환승센터를 평가한 결과 종합적으로 서울역 0.801543점, 청량리역 0.742488점으로 서울역이 청량리역보다 광역복합환승센터로서의 기능을 더 잘 수행하고 있는 것을 확인할 수 있었다. 환승시간, 용적률 활용도 등 일부 지표가 청량리역에서 우세하였으나 혼잡도나 주변 지역 활성화 지표가 서울 역에서 더 좋은 평가를 받은 점이 원인일 것으로 분석되었다.

PURPOSES : This study proposes an index for analyzing mobility based on smartcard and taxi data to evaluate imbalances in public transit. METHODS : The proposed mobility index is calculated based on the difference between the mobility indexes of public transit and taxis using the variables of the in-vehicle time, waiting time, and driving ratio. For a more detailed analysis, the distances are divided into short distances, medium distances, and long distances. RESULTS : Public transit mobility indexes are generally evenly distributed, but the taxi mobility indexes are located in the largest legend. When comparing the respective mobilities of public transit and taxis, many areas with a high mobility of taxis (similar to the distribution) exist, especially in the outskirts such as Dobong-gu, Nowon-gu, Gangdong-gu, Guro-gu, Geumcheon-gu, and Eunpyeong-gu. On average, the mobility of public transit according to the distance is smaller in a short distance and higher in a long distance. CONCLUSIONS : The results demonstrate the use of the proposed index for analyzing the basic statuses of complementary indexes for evaluating public transit imbalances. In the future, more detailed results (including socioeconomic variables corresponding to the grid areas) should be studied to identify the impacts of the mobility index.

PURPOSES : This study aimed to explore crowding impedance for high-risk travelers on various modes of public transit during the COVID-19 pandemic and develop a transport policy to encourage the proper use of public transport. METHODS : A stated preference survey was conducted to investigate the behaviors of travelers on various modes of public transit, with special emphasis on crowding inside vehicles. Multinomial logit-based modeling was used to estimate the explanatory variables identified as parameters based on the surveyed data. A crowding multiplier was adopted to represent the behavioral differences for the high-risk travelers on various modes of public transit. RESULTS : The established model was solved using the ‘mlogit’ R package program to estimate the identified parameters. The results demonstrated significant behavioral difference for the high-risk travelers on public transit during the COVID-19 pandemic. The proposed crowding multiplier successfully captured the reduced likelihood of high-risk travelers to be sensitive to crowding on the subway; furthermore, it revealed that non-crowding travelers on the subway are less sensitive to crowding than bus travelers. CONCLUSIONS : This study estimated crowding impedance for high-risk travelers on various modes of public transit during the COVID-19 pandemic and suggested an appropriate transport policy for those travelers.

PURPOSES : This study analyzed explanatory variables, such as dangerous driving behaviors, in a negative binomial regression model, using the Digital Tachograph data of commercial vehicles, to assess the factors associated with freeway accidents. METHODS : Fixed parameter and random parameter negative binomial regression models were constructed using freeway accident data of commercial vehicles from January 2007 to July 2018 on the Gyeongbu Expressway from West Ulsan Interchange to Gimcheon Junction. RESULTS : Six explanatory variables (logarithm of average annual daily traffic, sunny, rainy, and snowy weather conditions, road curvature, and driving behaviors that included sudden stops) were found to impact the occurrence of freeway accidents significantly. Two of these variables (snowy weather conditions and sudden stops among dangerous driving behaviors) were analyzed as random parameters. These variables were shown as probabilistic variables that do not have a fixed impact on traffic accidents CONCLUSIONS : The variables analyzed as random parameters should be carefully considered when the freeway operating authorities plan an improvement project for highway safety.