This paper presents a cumulative travel-time responsive (CTR) real-time intersection control algorithm that takes full advantage of connected vehicles (CVs). The potential benefits of the proposed CTR algorithm under varying imperfect CV market penetration rates and traffic congestion conditions were investigated. The core of the CTR algorithm is based on a stochastic state estimation technique utilizing Kalman filtering that is used in estimating the cumulative travel times under imperfect market penetration rates at every update interval. Comprehensive simulation experiments covering varying volume-to-capacity ratios and imperfect market penetration rates were performed at a hypothetical isolated intersection with two through lanes and a single left-turn lane at each approach. At 100% market penetration, the CTR algorithm improved the total delay time and average speed of the intersection by 34 and 36%, respectively, compared to an optimized actuated control. It was found that at least 30% marke...

Existing state-of-the-practice traffic signal timing-optimization programs rely on macroscopic and deterministic models to represent traffic flow, including coordinated actuated traffic signal systems. One distinct shortcoming of such an approach is its inability to account for the stochastic nature of traffic, such as the variability in traffic demand, driver behavior, vehicular interarrival times, vehicle mix, and so forth. In addition, the existing traffic signal timing-optimization programs for coordinated actuated traffic signal systems still focus on four basic traffic signal timing parameters (i.e., cycle length, green times or force-off points, offsets, and phase sequences). Studies have shown that actuated signal settings such as minimum green time, vehicle extension, and recall mode are also important parameters in traffic signal operations. This study presents the development of a stochastic-optimization method for coordinated actuated traffic signal systems. The proposed method account...

Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.

As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

Traffic signal timing optimization and control are one of the most cost-effective ways of improving urban arterial network congestions. Among various control modes, actuated traffic signal control is designed to provide green times where they are needed and it uses a pre-specified gap-out time to determine early termination of current phase green time. However, the effectiveness of its signal state decisions is limited by its dependence on vehicular information from fixed point sensors. With the emerging wireless communication technology based on cooperative vehicle-infrastructure system known as IntelliDrive, individual vehicular information (e.g., speed, acceleration, and location) is needed to be fully utilized for traffic signal control applications. This paper presents a dynamic gap-out approach that uses individual vehicular information and quantified benefits of the proposed approach via simulation. The timing plans were optimized by the genetic algorithm for both the traditional and the proposed gap-out cases. The results based on a four-leg intersection indicated that the dynamic gap-out reduced vehicular delays by 12.5% over existing regular gap-out.