Accurate estimation of vehicle exhaust emissions at urban intersections is essential to assess environmental impacts and support sustainable traffic management. Traditional emission models often rely on aggregated traffic volumes or measures of average speed that fail to capture the dynamic behaviors of vehicles such as acceleration, deceleration, and idling. This study presents a methodology that leverages video data from smart intersections to estimate vehicle emissions at microscale and in real time. Using a CenterNet-based object detection and tracking framework, vehicle trajectories, speeds, and classifications were extracted with high precision. A structured preprocessing pipeline was applied to correct noise, missing frames, and classification inconsistencies to ensure reliable time-series inputs. Subsequently, a lightweight emission model integrating vehicle-specific coefficients was employed to estimate major pollutants including CO and NOx at a framelevel resolution. The proposed algorithm was validated using real-world video data from a smart intersection in Hwaseong, Korea, and the results indicated significant improvements in accuracy compared to conventional approaches based on average speed. In particular, the model reflected variations in emissions effectively under congested conditions and thus captured the elevated impact of frequent stopand- go patterns. Beyond technical performance, these results demonstrate that traffic video data, which have traditionally been limited to flow monitoring and safety analysis, can be extended to practical environmental evaluation. The proposed algorithm offers a scalable and cost-effective tool for urban air quality management, which enables policymakers and practitioners to link traffic operations with emission outcomes in a quantifiable manner.

Autonomous vehicles are widely expected to be commercialized in the near future. This would naturally lead to situations in which existing vehicles and autonomous vehicles would be on the road at the same time, which would pose a notable hazard to traffic safety. From this perspective, high-risk factors relating to this deployment should be identified to prepare measures to promote traffic safety. However, at this point, deriving high-risk factors based on actual data is problematic because autonomous vehicles have not yet been widely commercialized. In this study, we derive high-risk factors that would apply if autonomous vehicles were allowed to drive alongside vehicles driven by humans using a meta-analysis. We synthesized factors related to autonomous vehicles mentioned in the relevant literature. An analysis was conducted based on a total of 58 documents according to five keywords related to autonomous vehicles (crash factors, scenarios, predictive models, laws, and regulations). We also performed a binary meta-analysis of factors related to autonomous vehicles according to these keywords and a meta-analysis of effect size according to the relative size of factors to evaluate them comprehensively. We found that many different aspects of driving such as navigating intersections, lanes, fog, rain, acceleration and deceleration, rear-end collisions, inter-vehicle spacing, and pedestrian collisions were notable as high-risk factors. This study provides basic data to identify high-risk factors to support the development of related prediction models.

This study was carried out in a cold storage chamber with a floor space of roughly 3.3 square meters (1 pyeong). The findings revealed that the hybrid cooling system consumed a comparable amount of electricity to that of the conventional vapor compression system. This similarity in power usage can be attributed to the hybrid system’s operational strategy: thermoelectric modules were selectively activated during periods of frost accumulation, effectively minimizing the energy typically used for electric defrosting in vapor compression units. To advance the commercialization of this hybrid system in cold storage applications, several technical improvements must be considered in addition to cost optimization. First, the design should address the bulky nature of the heat exchanger setup. At present, the vapor compression and thermoelectric modules are housed in separate outdoor units; a more efficient approach would involve integrating them into a single, space-saving unit. Second, incorporating a water mist spray mechanism for the outdoor heat exchanger coil could enhance heat dissipation. This method, which leverages latent heat exchange, has demonstrated strong performance in other applications and merits further investigation for use in the proposed system.

본 연구에서는 교목성 낙엽침엽수인 메타세쿼이아(Metasequoia glyptostroboides)가 가로수로 식재된 국내 8개 지역(삼척, 대전, 대구, 구미, 포항, 부산, 진안, 담양)의 10개 도로에서 총 280본을 대상으로 결함 및 관리 특성을 조사하였다. 육안 평가를 기반으로 2022년과 2023년 6~7월에 기본 현황, 결함, 관리 특성을 종합적으로 분석하였다. 결함도는 고사지, 줄기 상처, 병해충 등을 조사하여 정량화하였다. 메타세쿼이아의 평균 수고는 17.8m, 흉고직경 43.2cm, 근원직경 62.3cm, 수관폭 7.7m, 지하고 4.1m였으며, 흉고직경과 수고 간에는 전반적으로 양의 상관관계가 확인되었다. 그러나 흉고직경 기준 수고 예측 모델을 사용하였을 때 자연 집단보다 수고가 최대 11.9m 낮았다. 결함도는 평균 2.21점으로, 근계 결함(96.79%), 해충 피해(60.00%), 고사지(46.79%)가 가장 빈번했다. 보호틀 폭은 대부분 1~2m였으나 일부 구간은 1m 내외로 근계 손상이 발생하였고, 전선 비지중화 구간에서는 가지치기로 인해 수형이 고착되는 경향을 보였다. 교목성 가로수로서 전국적으로 조성된 메타세쿼이아 가로수의 지속 가능한 관리를 위한 종합적인 방안을 강구하는 것이 필요하다.