Seoungbum Kim

Driven by climate change and escalating traffic, rapid road deterioration threatens public safety, prompting a shift from costly manual inspections toward scalable solutions. High-precision laser scanners offer accuracy, but prohibitive costs limit continuous monitoring across extensive road networks. To overcome these constraints, this study proposes an automated multimodal monitoring system integrating real-time video imagery with gyroscopic impact data. The architecture employs a late fusion approach, combining visual features extracted from the Global Average Pooling (GAP) layer of a pre-trained ResNet50 model with synchronized gyroscopic time-series data. Experimental results revealed that while the initial baseline model achieved a recall of 0.75 and an F1-score of 0.55, applying data balancing enhanced performance, increasing the recall to 0.98 and the F1-score to 0.65. When evaluated on a geographically independent highway section, the model identified 44 bad road segments and encompassed 94% of actual bad segment durations, predicting a total duration of 741 s compared to the ground-truth 564 s. Ultimately, despite a tendency for over-detection, the model demonstrates significant practical utility as a conservative decision-support tool for proactive road maintenance due to its high sensitivity and robust detection capabilities.

Autonomous vehicles (AVs) at SAE Levels 3 require a take-over request to switch from autonomous to manual mode when leaving the operational design domain (ODD). An appropriate take-over request lead time (TORlt) is necessary for safe interaction between AVs and non-AVs. This study developed a methodology to derive the optimal TORlt for AVs entering the area out of the ODD using a multi-agent driving simulator experiment. The multi-criteria decision-making method was adopted to integrate evaluation indicators to derive an optimal TORlt. The TORlt was defined as 3, 6, 9, 12, and 15 s in the driving simulation experiment scenario. The driving simulation experiment was conducted with a total of 60 participants. The simulation network was a two-lane urban road in each direction with a total length of 1.7 km, including a school zone where the autonomous driving mode is prohibited. Three requirements were established to determine the optimal TORlt: minimizing the take-over time, maximizing the success rate of take-over, and minimizing the potential of rear-end collisions due to vehicle interactions. After conducting comparative analyses of individual evaluation indicators for each scenario, a multi-criteria decision-making method was used for integrated evaluation to determine the optimal TORlt. It was found that the optimal TORlt for AVs on urban roads is 9 s. The results of this study can be used as valuable fundamentals in determining take-over requests for AVs toward safer vehicle interactions in the traffic stream.

Over the past decade, Intelligent Transportation Systems (ITSs) have evolved from conceptual frameworks into operationally deployed technologies

Identifying road segments with a high crash incidence is essential for improving road safety. Conventional methods for detecting these segments rely on historical data from various sensors, which may inadequately capture rapidly changing road conditions and emerging hazards. To address these limitations, this study proposes leveraging crowdsourced data alongside historical traffic accident records to identify areas prone to crashes. By integrating real-time public observations and user feedback, the research hypothesizes that traffic accidents are more likely to occur in areas with frequent user-reported feedback. To evaluate this hypothesis, spatial autocorrelation and clustering analyses are conducted on both crowdsourced data and accident records. After defining hotspot areas based on user feedback and fatal accident records, a density analysis is performed on such hotspots. The results indicate that integrating crowdsourced data can complement traditional methods, providing a more dynamic and adaptive framework for identifying and mitigating road-related risks. Furthermore, this study demonstrates that crowdsourced data can serve as a strategic and sustainable resource for enhancing road safety and informing more effective road management practices.

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with road and traffic condition information using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed while increasing the spacing or changing lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behavior from a traffic safety point of view. This study seeks to quantitatively evaluate the effect of in-vehicle warning information using per-vehicle data (PVD) collected from freeway C-ITSs. The PVD are reproduced to extract the speed–spacing relationship and are evaluated to determine whether the warning information induces drivers to drive in a conservative way. This study reveals that the in-vehicle warning prompts drivers to increase the spacing while decreasing their speed in the majority of samples. The rate of conservative driving behavior tends to increase during the initial operation period, but no significant changes were observed after this period; that is, the reliability of in-vehicle warning information is not constant in the CV environment.

Accidents caused by road hazards can be prevented through regular inspections by road management agencies. To this end, traffic agencies allocate substantial budgets and workforces to maintain the performance of roads. Additionally, traffic agencies require comprehensive data such as the classifications and sizes of road hazards. However, collecting spatial–temporal data on various road hazards is challenging, and evaluating it comprehensively with respect to work efficiency and budget allocation is difficult due to stakeholder interests across agencies. This study proposes a process of evaluating operational efficiency in terms of maintaining roads and preventing hazards by analyzing citizen scientist-based data. First, we collected data from drivers through a mobile application and applied text mining techniques to classify each complaint into several types of road hazard maintenance. Second, we developed an indicator to measure operational efficiency using the processed data and evaluated each regional agency per each type of maintenance. The results of this study provide evidence that specific types of road hazards occur prominently under specific agencies. In addition, the time required to provide maintenance for identical road hazards can vary among agencies. These results suggest that the maintenance budget for the entire national highway may need to be distributed differently based upon regional characteristics.