Natural disasters can cause severe damage to critical infrastructure such as road networks, significantly delaying rescue and recovery efforts. Conventional road damage assessments rely heavily on manual inspection, which is labor-intensive, time-consuming, and infeasible in large-scale disaster-affected areas. This study aims to propose a deep learning-based framework to automatically detect and quantitatively assess road damage using high-resolution pre- and post-disaster satellite imagery. To achieve this, the study systematically compares three distinct change detection approaches: single-timeframe overlay, difference-based segmentation, and Siamese feature fusion. Experimental results, validated over multiple runs, show the difference-based model achieved the highest overall F1-score (0.594 ± 0.025), surpassing the overlay and Siamese models by approximately 127.6% and 27.5%, respectively. However, a key finding of this study is that even this best-performing model is constrained by a low detection recall (0.445 ± 0.051) for the ‘damaged road’ class. This reveals that severe class imbalance is a fundamental hurdle in this domain for which standard training strategies are insufficient. This study establishes a crucial benchmark for the field, highlighting that future research must focus on methods that directly address class imbalance to improve detection recall. Despite its quantified limitations, the proposed framework enables the visualization of damage density maps, supporting emergency response strategies such as prioritizing road restoration and accessibility planning in disaster-stricken areas.

The transportation sector is regarded as the main culprit in greenhouse gas emission in the urban network, particularly idling vehicles waiting at signalized intersections. Although autonomous vehicles can be a promising technology to tackle vehicle idling, their environmental benefits receive little attention compared with their safety and mobility issues. This study investigated the environmental benefits of autonomous vehicles equipped with an automatic idling control function based on the queue discharge time and traffic signal information transmitted from the traffic signal controller via V2I communication using microscopic mobility and emission simulation models, VISSIM and MOVES, in Haeundae-gu in Busan, Korea. This study found that the function contributes to a significant reduction in CO₂ emissions by 23.6% for all-inclusive emission and 94.3% for idling emission, respectively. Moreover, total reduced idling time accounts for 47.6% of the total travel time and 94.3% of the total idling time, respectively. Consequently, the autonomous vehicles equipped with automatic vehicle idling control function under C-ITS can play an important role in reducing greenhouse gas emissions and fuel consumption as well in the urban network.

In developing countries, government agencies focus its transportation system developments on building more roads and bridge structures to alleviate the traffic, undermining the idea of using Intelligent Transportation Systems to maximize the capacity of existing infrastructure. When the public sectors perform poorly in managing traffic, a decentralized traffic management approach seems to be more appropriate. The vehicle-to-vehicle and vehicle-to-any communication systems are among the top choices in the decentralized system. Despite the potential benefits of V2V and V2X communication applications, there are many challenges in implementing them in developing countries. We identified the obstacles in various aspects and then designed the system within the framework of such constraints. Also, we envisioned the deployment of small, commercially available off-the-shelf routers which rely on open Internet standards, and used one or more unlicensed frequencies followed by setting a simple experiment up. We have successfully demonstrated that V2V mobile routers can be built from commodity components which are available in developing countries without depending on regulated radio frequencies hence achieving the freedom to use these V2V mobile routers almost everywhere. Since smart devices such as Smartphones and tablets can connect to the proposed mobile routers, there are endless possibilities to develop useful applications.

Road infrastructure is a critical component of modern society, with its maintenance directly influencing traffic safety and logistical efficiency. In this context, automated crack detection technology plays a vital role in reducing maintenance costs and enhancing operational efficiency. However, previous studies are limited by the fact that they provide only bounding box or segmentation mask annotations for a restricted number of crack classes and use a relatively small size of datasets. To address these limitations and advance deep learning-based crack segmentation, this study introduces a novel crack segmentation dataset that reflects real-world road conditions. The proposed dataset includes various types of cracks and defects—such as slippage, rutting, and construction-related cracks—and provides polygon-based segmentation masks captured from an egocentric, vehicle-mounted perspective. Using this dataset, we evaluated the performance of semantic and instance segmentation models. Notably, SegFormer achieved the highest Pixel Accuracy (PA) and mean Intersection over Union (mIoU) for semantic segmentation, while YOLOv7 exhibited outstanding detection performance for alligator crack class, recording an ${AP}_{50}$ of 87.2% and $AP$ of 57.5%. In contrast, all models struggled with the reflection crack type, indicating the inherent segmentation challenges. Overall, this study provides a practical and robust foundation for future research in automated road crack segmentation. Additional resources including the dataset and annotation details can be found at our GitHub repository.

This study introduces a state-of-the-art road debris removal system (i.e., automated road debris removal system (ROBOS)) and analyzes its impact on traffic flow using the microscopic simulation model, VISSIM. Robust simulation scenarios were established, and the major parameters of individual scenarios were also included based on extensive interviews with road workers. The performances of road workers and the ROBOS in regard to road debris removal were investigated in terms of four measures of effectiveness. These included queue, density, percentage delay, and number of vehicle stops. To mimic the major arterial roadways in South Korea, two-lane, three-lane, and four-lane roads were considered for implementing the road debris removal process. The simulation results as well as the statistical analysis revealed that the ROBOS outperforms the direct removal by road workers in all aspects. Thus, the ROBOS can remove the debris more safely and efficiently. The findings of this research can improve institutional operational guidelines for the ROBOS by providing an appropriate range for its parameters. This can result in enhancing traffic safety and efficiency. Notwithstanding the simulation results, the more unexpected cases, such as the driver carelessness during removal work, should be reflected in future research to thoroughly prevent the potential risk of accidents as well as maintaining smooth traffic flow.