During road work, lane closures significantly reduce road capacity and negatively impact traffic safety in the upstream segments. This study introduces a merge control strategy for the work zone on freeway that aims to alleviate severe congestion and improve flow efficiency in environments where manual vehicles (MVs) and connected automated vehicles (CAVs) coexist. Using a short‐term prediction model combined with a proportional‐integral‐derivative (PID) controller, this strategy dynamically adjusts merging behavior based on real‐time traffic conditions. The PID controller calculates error values as the difference between current and target states, adjusting responses through proportional, integral, and derivative terms. The predictions of the traffic state based on the density of open lanes in each segment guide the controller’s decision to initiate a “Merge” or “No Merge” guidance. When merging is deemed necessary, the controller estimates the optimal number of vehicles to merge for each segment, using the severe congestion threshold as a reference point. This approach was tested using a microscopic simulation tool on a calibrated real‐world network under mixed traffic conditions. The results indicate that the proposed strategy effectively disperses merging upstream, increases merging speeds, and maintains lane density below critical congestion levels, thus enhancing operation efficiency and safety in work zone areas.

The emergence of digital mobility services holds great promise for enhancing efficiency, convenience, and accessibility for passengers. However, these benefits are predominantly accessible to those proficient in utilizing these technologies, which may intensify the disparity in transportation usage. This paper presents plans to alleviate the digital divide in the mobility sector. First, two fundamental approaches were established through a literature review: (1) app usage education and (2) an AI-based Mobility Service App. To substantiate the effectiveness of these approaches, a pilot test was conducted in Seoul. The results of the pilot test showed that the AI-based Mobility Service App was effective for reducing travel time and enhancing the convenience of passage. Accordingly, the Technology Acceptance Model was adopted to derive technology acceptance factors of the AI-based Mobility Service App. Finally, a phased approach with short-term, medium-term, and long-term plans was proposed based on the analysis results to ensure sustainable policies in the mobility sector.

This study proposes a reliable and efficient real-time forecasting platform for use in an accident-prone large-scale transportation network. We showed the method could be applied to various roadway sections without any loss of performance or efficiency. Due to its robustness, efficiency, and versatility, the method could be implemented in the Seoul Metropolitan Area to provide traffic authorities and road users with future traffic information even under accident conditions. This is the major contribution of this research and contrasts with state-of-the-art techniques proposed by prior studies, which rely heavily on parameter tuning with large historical datasets and produce only site-specific forecasts with limited prediction horizons under recurrent traffic conditions. The proposed method makes no assumptions about the physical structure of the transportation network and can be applied to different roads under different traffic conditions without time constraints.

Despite the potential benefits of autonomous vehicles (AVs) of reducing human driver errors and enhancing traffic safety, a comprehensive evaluation of recent AV collision data reveals a concerning trend of rear-end collisions caused by following vehicles. This study aimed to address this issue by developing a methodology that identifies the relationship between driving patterns and the risk of collision between leading and following vehicles using spectral analysis. Specifically, we propose a process for computing three indices: reaction time, stimulus compliance index, and collision-risk aversion index. These indices consistently produced reliable results under various traffic conditions. Our findings align with existing research on the driving patterns of following vehicles. Given the consistency and robustness of these indices, they can be effectively utilized in advanced driver assistance systems or incorporated into AVs to assess the likelihood of collision risk posed by following vehicles and develop safer driving strategies accordingly.

Delay is a critical measure when evaluating level of service under interrupted flow conditions and is widely used to assess operational improvements and optimize traffic signals. However, existing delay models often fail to capture the effects of arrival interruptions, which are prevalent at closely spaced intersections, and are typically limited to specific facility types. To address this limitation, this paper proposes a generalized analytical delay model that explicitly accounts for the effects of arrival interruptions caused by upstream signals on delays at downstream signals. The model consists of two components that maintain consistency between delay terms while reflecting both the duration and timing of arrival interruptions. The model was evaluated against the Highway Capacity Manual (HCM) method at internal approaches of a typical diamond interchange and closely spaced arterial intersections. While the HCM model produced reasonable estimates under good progression conditions, it tended to overestimate delays under poor progression. This overestimation was attributed to double-counting of the effects of arrival interruptions; the variation in arrival rates was reflected through the progression factor, while capacity was simultaneously reduced. In contrast, the proposed model avoids this type of redundancy, demonstrating improved accuracy. Results show that it provided more reliable delay estimates across both facility types and various offset scenarios, with errors ranging from −4.6 to 1.9 s compared with VISSIM simulations or field measurements. The model is expected to support more robust delay estimations and informed decision-making under diverse traffic conditions and network configurations.

The key to ensuring the sustainability of transportation operations on the roads is to manage traffic flows homogeneously. By homogenizing the behavior of various drivers, traffic operation can be optimized, and traffic safety can be improved. However, the advent of autonomous vehicles is expected to have a major impact on the homogeneity of sustainable transportation operations. In this study, a method of driving in harmony with surrounding vehicles was studied to minimize the impact of autonomous vehicles on current traffic operation. In particular, in this study, a methodology was developed to optimize the driving behavior of autonomous vehicles by analyzing the driving behavior of following vehicles using spectrum analysis. Specifically, a method for calculating three indicators that can analyze the driving behavior of a following vehicle, such as reaction time, stimulus adaptation index, and collision risk avoidance index, was proposed. These indices produced consistent and robust results for all traffic conditions. If these indicators are used, it is expected that sustainable traffic management will be possible even when autonomous vehicles and human drivers are mixed on the road.