Urbanization combined with climate change is intensifying urban thermal stress, requiring proactive and spatially explicit heat-risk assessment. This study develops a scenario-based framework that integrates machine-learning–enhanced land cover simulation with land surface temperature (LST) prediction to compare future thermal outcomes under Urban Growth (UG) and Ecological Conservation (EC) scenarios through 2034. Within the FLUS framework, probability-estimation modules were empirically evaluated, and XGBoost achieved the most stable predictive performance; validation against the observed 2024 land cover map confirmed simulation reliability. By 2034, the EC scenario reduced the study-area mean LST by 0.10°C relative to UG, while localized cooling within the existing urbanized area reached 0.6°C. Distance-based analysis showed that mitigation effects were concentrated within approximately 300 m of the urban boundary. When high-temperature areas were defined as pixels exceeding 32.10°C (top 10% threshold), their extent reached 90.64 km² under UG but was constrained to 77.77 km² under EC, representing a reduction of 12.87 km². These findings demonstrate that conservation-oriented land-use strategies can meaningfully influence both average warming levels and the spatial redistribution of urban heat risk.

Abnormal weather is occurring around the world, including the hottest weather in 174 years of observation records, the largest fire in Europe’s observation records, and approximately twice the average annual rainfall recorded in one day. This abnormal climate is highly related to greenhouse gases, and efforts to reduce emissions are required in various fields. This study aims to reduce carbon dioxide emissions in the transportation sector, which accounts for a high proportion of emissions. A multi-agent reinforcement learning technique is used for adaptive traffic signal control, and especially a novel cooperative approach is introduced, when considering neighboring intersections. We consider not only the adjacent intersection’s last reward as a Q-function but also its state and action as state. This method has the advantage of considering only vehicles from adjacent intersections that enter an intersection. The proposed method was evaluated on roads in Icheon City, and the results show that it reduces waiting time and carbon dioxide emissions.

Climate change and urbanization have intensified the urban heat island (UHI) effect, significantly impacting urban living environments. While existing studies have yielded valuable insights into macro-scale thermal environments, this study shifts the focus toward microscale residential contexts, where localized urban form and land use patterns critically shape thermal conditions.In this study, we analyzed the temporal variations in LST in a residential neighborhood of Okgye-dong, Jung-gu, Daejeon, South Korea. High-resolution thermal imagery captured by unmanned aerial vehicles (UAVs) and interpretable machine learning (ML) techniques were used to model and analyze thermal patterns at the microscale. The study site, adjacent to a river and designated as an Urban Regeneration Area, is particularly vulnerable to summer heat.Exploratory data analysis (EDA) was conducted to examine statistical characteristics and spatial patterns, followed by confirmatory data analysis (CDA) using nonlinear regression models such as CatBoost, Random Forest, and XGBoost. The results showed that the importance of variables influencing LST varied by time of day. However, meteorological variables such as solar radiation, wind, and humidity were not included due to data limitations.Among the key findings, alley width, shadow ratio, and distance from the river emerged as dominant variables affecting thermal conditions in residential areas. This study contributes to identifying time-sensitive drivers of urban thermal vulnerability by leveraging UAV-based imagery and ML. Based on these findings, we propose specific policy-oriented strategies for heat mitigation in urban regeneration areas, including improving airflow in narrow alleys by removing obstructions or illegal parking, expanding riverside green spaces to enhance cooling effects, and installing vertical shading elements to reduce localized heat stress and improve thermal comfort.These results are particularly valuable for urban regeneration projects, where thermal vulnerability is often intensified by high building density and limited green infrastructure. The proposed strategies—such as optimizing alley width, increasing shade coverage, and enhancing riverside green spaces—can be effectively incorporated into localized urban redevelopment plans to improve thermal comfort and resilience.

The delivery market in Republic of Korea has experienced significant growth, leading to a surge in motorcycle-related accidents. However, there is a lack of comprehensive data collection systems for motorcycle safety management. This study focused on designing and implementing a foundational data collection system to monitor and evaluate motorcycle driving behavior. To achieve this, eleven risky behaviors were defined, identified using image-based, GIS-based, and inertial-sensor-based methods. A motorcycle-mounted sensing device was installed to assess driving, with drivers reviewing their patterns through an app and all data monitored via a web interface. The system was applied and tested using a testbed. This study is significant as it successfully conducted foundational data collection for motorcycle safety management and designed and implemented a system for monitoring and evaluation.

The problems of administrations based simply on administrative units that do not consider the operational purposes of the system have been consistently discussed. For example, in the Republic of Korea, sex offenders’ information is distributed via physical mail only in a few regions, a practice that is too rigidly based on the boundaries of the administrative ‘Dong’ of the offender’s residence. This implies that citizens in an adjacent building will not be notified if their Dong is different. Therefore, this study analyzed the problems of an administrative system that does not consider its realistic scope by using the case study of sex offender notifications. By expanding the distance from children and youth grids, we ascertained the extent of the problems with sex offender notifications. Additionally, to determine whether these problems have occurred by chance at a specific point in time or if there has been a fundamental limitation in the system, the Monte Carlo simulation was applied to compare the actual and random data of residences.

The growing concerns over road safety and the increasing popularity of two-wheeled vehicles highlight the need to address aggressive driving behaviors in this context. Understanding and detecting such behaviors can significantly contribute to rider safety and accident prevention. The primary aim of this research is to develop an effective method for detecting aggressive driving patterns, specifically focusing on rapid turns and lane-change maneuvers using two-wheeled vehicles. To achieve this objective, we conducted a survey to establish criteria for aggressive driving. Subsequently, we collected data through a virtual simulator, implementing staged aggressive driving scenarios. The data underwent preprocessing, feature engineering, and deep learning model training for detection. The results of this study demonstrate the successful detection of aggressive driving patterns, including rapid turns and lane changes, using sensor data. The criterion for rapid turns is specified as a significant change in sensor values within 1 s. In the CNN-LSTM model for aggressive lane changes, the precision for normal driving is 0.97, and the overall accuracy for aggressive driving is 95%. Our approach, which relies on sensor technology rather than impractical camera systems, showcases the potential for enhancing rider safety in two-wheeled vehicles. In conclusion, this research provides valuable insights into the detection of aggressive driving patterns in two-wheeled vehicles. By leveraging sensor data and innovative methods, it offers promising implications for improving rider safety and accident prevention in the future.

Given the high incidence of traffic accidents and fatalities on two-wheeled vehicles, there is a growing need for safety management. However, studies on evaluating two-wheeled vehicle driving in a quantitative and comprehensive form are insufficient. In this study, 11 items were defined for the first step to evaluate two-wheeled vehicle driving: signal violation, central line violation, helmet violation, pedestrian close driving, sidewalk driving, reverse lane driving, speed violation, rapid acceleration, rapid deceleration, rapid turn, and rapid lane change. The items were classified into three categories (traffic violation, pedestrian threat, and reckless driving), and their weights were derived using the AHP technique. For rapid acceleration, rapid deceleration, rapid turn, and rapid lane change, a high-performance driving simulator was used to establish risk criteria and calculate the weight based on the degree of risk. The calculated weight of each item indicates its importance in evaluating two-wheeled vehicle driving, with helmet violation (0.158), speed violation (0.124), and pedestrian close driving (0.122) having the highest weights. Finally, the dangerous driving index for two-wheeled vehicles was calculated by the weights of each evaluation item and applied to the driving trajectory data.

In the present study, we propose a methodology that predicts the number of passengers on new public transport lines based on smart card data and an optimal path finding algorithm. It employs a deterministic approach that assumes that, when a new line is added to the public transport network, passengers choose the fastest route to their destination. The proposed methodology is applied to actual lines (bus and subway lines) in Seoul, the capital of South Korea, and it is validated through the observed traffic volume of those lines recorded in the smart card data. The experiments are conducted using smart card data, with more than 100 million trips stored, extracted from about 1 million passengers who have check-in records in the catchment area of the new lines. The experimental results show that the proposed methodology predicts the daily average number of passengers very similar to the observed data.

In order to deal with dynamic traffic flow, adaptive traffic signal controls using reinforcement learning are being studied. However, most of the related studies are difficult to apply to the real field considering only mathematical optimization. In this study, we propose a reinforcement learning-based signal optimization model with constraints. The proposed model maintains the sequence of typical signal phases and considers the minimum green time. The model was trained using Simulation of Urban MObility (SUMO), a microscopic traffic simulator. The model was evaluated in the virtual environment similar to a real road with multiple intersections connected. The performance of the proposed model was analyzed by comparing the delay and number of stops with a reinforcement learning model that did not consider constraints and a fixed-time model. In a peak hour, the proposed model reduced the delay from 3 min 15 s to 2 min 15 s and the number of stops from 11 to 4.7 compared to the fixed-time model.

This paper proposes an extended floor field model considering the spread of fire and detour behavior. It includes a fire spread field and a fire recognition field. An agent who recognizes a fire makes a detour to an exit with the lowest risk considering transition probability based on the weighted static floor field. The improved transition probability takes into account the agent's visibility reduced by smoke diffusion. The proposed model differs from the related models in that the agent dynamically chooses the safety path upon fire detection. Compared to simulations using commercial software, the model predicted longer evacuation times and fewer agents inhaling smoke. The model's implementation included realistic pedestrian movements, such as trying to move away from a smoke-filled space.

There are some studies that analyze public transport service gap by region from comparing supply and demand. However, due to data limitations, previous studies were limited in subdividing region-based service gap by Origin-Destination (O-D). This study analyzes the gaps of public transport services based on O-D, a micro spatial unit. The data used in this study include timetable of public transport and smart card data stored with transportation used records of individual users. The supply index presented in this study is based on O-D travel time considering for temporal fluctuation. And the demand index is explained in terms of actual traffic of O-D. The proposed methodology is applied to Seoul metropolitan city and the analysis for identifying service gap is conducted along major time periods of a day. Visualization is performed on some O-D pairs that require improvements in supply relative to demand. The areas where disparities in service exist were identified

A fire simulator and an evacuation simulator are generally used independently to diagnose the safety of a building in the case of the fire and evacuation. However, it is hard to provide highly accurate safety diagnosis with this method because it does not reflect the movement of pedestrians in the situation of a fire. Therefore, this study proposed a fire evacuation simulation technique that can describe the movement of pedestrians with considering the fire spread. The proposed simulation technique applies the fire spread data of the fire dynamics simulator (FDS) to the floor field model (FFM) and it models that pedestrians recognizes the fire and take a detour to a safe route. This study proposed a method to link the data between FDS and FFM and an improved FFM considering fire spread. Additionally, the proposed method was applied to a real building on a university campus. This study simulated evacuations under various scenarios. Simulation results showed that the number of evacuees escaping through each exit varied by the presence of fire. Moreover, it was found that the evacuation time was increased or decreased by the fire and bottleneck phenomenon was also worsened under fire situation.

Most of the existing public transit routing algorithms were developed on the basis of graph theory. Recently, algorithms are being developed that can compute for O-D public transit paths by using timetable information only, not using network structure consisting of nodes and links. The timetable-based public transit routing algorithm produces one shortest path to destination, using departure time and arrival time by stop. But it has limitations in reflecting additional factors, such as transfer penalty and alternative path selection, in the process of path calculation. In addition, since public transit passengers tend to choose one among various alternative paths, it is necessary to calculate multiple paths rather than a single path as in the existing methods. Therefore, this study proposes an improved RAPTOR algorithm that can consider transfer penalty and produce multiple paths, while it is based on RAPTOR, the existing timetable-based public transit routing algorithm. The transfer penalty was applied at the point of transfer, and differently according to transfer types. As a result of analyzing computed paths of the algorithms before and after improvement, it was found that computed paths with the improved RAPTOR algorithm proposed by this study were more similar to Seoul public transit passengers. actual travel paths than computed paths by the existing RAPTOR alone.

교통카드 데이터는 대중교통 이용 승객의 개별 통행이 미시적인 단위로 기록된다. 교통카드 데이터는 크기로 인하여 유의미한 패턴을 찾는데 많은 시간이 소모되어, 대용량 시공간 데이터에서 패턴을 찾을 수 있는 시각화 기법이 활용되고 있다. 그러나 교통카드 데이터를 활용한 시각화 방법은 미비한 실정이다. 이에 본 연구에서는 통행패턴을 확인할 수 있는 방법으로 교통카드 데이터에서 개별 통행을 동적으로 시각화하는 방법을 제안한다. 본 논문은 교통카드 데이터와 대중교통 네트워크로 개별 통행의 궤적을 추정하고, 추정한 궤적을 동적으로 시각화하는 방법을 제시한다. 제시한 방법론을 적용하여 강남역을 이용한 승객의 교통카드 데이터를 시각화하여 미시적인 통행 특성을 분석하였다. 분석 결과 강남역을 이용하는 버스 승객들이 평일 오전에 강남 이남과 이북에서 상이한 패턴을 보이는 것을 확인하였다.