Conventional fixed-time traffic signal operations at urban intersections are typically based on prescheduled plans that presume stable and recurring traffic patterns, particularly during peak commuting hours. However, recent societal changes—including flexible work schedules, telecommuting, and evolving workweek structures—have introduced greater variability in traffic demand, thereby diminishing the effectiveness of traditional peak-hour-focused control strategies. This study investigated the performance of an AI-based adaptive traffic signal control system that operated independently of predefined time plans. A field demonstration was conducted in Jeju City, South Korea, where the system was deployed in both the cyclic and acyclic operation modes. By leveraging real-time traffic data obtained from AI-enabled video detectors, the system adjusted the signal timings on a per-second basis in response to dynamic traffic conditions. The performance was evaluated against the conventional time-of-day (TOD) control method under diverse traffic scenarios, including typical weekdays, weekends, and local event days. The AI-based system achieved substantial reductions in intersection delays—24.3% on weekdays, 22.2% on weekends, and 17.1% on event days—compared with the TOD baseline. Moreover, it preserved a comparable level of traffic progression (measured by the proportion of non-stop vehicle flows) even during acyclic operations. The greatest efficiency gains were observed during the nighttime and low-traffic periods, underscoring the capacity of the system to minimize unnecessary delays under variable conditions. These results highlighted the potential of AI-based adaptive signal control as a viable alternative to conventional fixed-time operations, offering enhanced responsiveness and operational flexibility in increasingly complex urban traffic environments. Future research will focus on scaling the system to larger networks and developing integrated optimization strategies across multiple intersections.

Autonomous vehicle technology is targeted for commercialization in 2027. However, a mixed traffic environment of conventional vehicles and autonomous vehicles is expected to be inevitable. In mixed traffic, conventional vehicles drive at reduced speeds due to limited visibility, while autonomous vehicles can drive at normal speeds using sensors. The difference in driving speeds between the two vehicles creates a mismatch in traffic flow, and the risk of congestion and accidents is likely to increase. It is necessary to analyze the impact of the interaction between autonomous vehicles and regular vehicles on traffic safety in advance and develop management measures to mitigate it. In this study, we aim to analyze the effect of reducing the speed deviation between general vehicles and autonomous vehicles by providing the driving speed deceleration level information to autonomous vehicles in the event of fog to induce the same traffic flow and improve the safety level accordingly. We examined the method of delivering the driving speed deceleration level information to autonomous vehicles. When providing speed limit information to autonomous vehicles through systems such as VMS, each country has different ways of recognizing regulatory symbols. Due to these differences, it may not be easy to provide regulatory information to overseas vehicles through external systems such as VMS in Korea. For this reason, there is a possibility that autonomous vehicles may violate laws and regulations by not recognizing them properly, and there are still limitations in defining the responsibility for applying laws and regulations between countries. Therefore, we adopted an information provision approach that encourages autonomous vehicles to maintain a harmonious traffic flow with regular vehicles by sharing safe driving speed information to be encouraged at the public center level. To analyze the effectiveness of these safe driving speed management measures, we used a quantitative indicator, the number of observable conflicts, to distinguish the mixing ratio of regular vehicles and autonomous vehicles. The analysis was divided into early (30%), mid (50%), and late (80%) periods of autonomous vehicle introduction. As a result of giving autonomous vehicles the same traffic flow as regular vehicles, the number of collisions decreased by 128 collisions/hour in the early period, 393 collisions/hour in the mid period, and 337 collisions/hour in the late period. This indicates that the interaction between autonomous vehicles and conventional vehicles becomes more complex as the mixing ratio increases, and the effectiveness of the safe speed management measures proposed in this study increases accordingly. These results can be used as an important basis for transportation policy and design.

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

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.

긴급차량 골든타임 확보를 위해 지방자치단체에서는 ‘긴급차량 우선신호’ 시스템을 도입하고 있다. 그러나 긴급차량 우선신호로 인하 여 일반차량의 지체시간은 증가하게 되고 기존 신호주기로 바로 복귀하여 교차로 전체의 지체를 유발할 것으로 예상된다. 해당 지체 를 해결하기 위해 일반차량을 고려한 ‘회복신호 산정’ 연구가 수행되고 있다. 그러나 현재까지 교차로 유형별로 적절한 회복신호는 무 엇인지에 대한 연구는 부족한 상황이다. 본 연구에서는 교차로를 유형별로 구분하여 일반차량 지체도를 감소시키기 위한 회복신호의 필요성을 검증하고자 한다. 우선신호의 경우 긴급차량이 교차로를 통과하기까지 걸리는 시간을 계산하여 부여하였다. 회복신호의 경우 는 우선신호에서 부여한 시간만큼 일반차량에게 보상하는 신호방식을 적용하였으며, 도입 효과를 SUMO 교통 시뮬레이션을 통해 비 교 분석하였다.