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Tamakloe Reuben,Park D. 서울시립대학교 도시과학연구원 2023 도시과학국제저널 Vol.27 No.3
Studies have employed several techniques to identify the effect of individual risk factors influencing crashes at hotspot locations. Nevertheless, as crashes are sometimes influenced by a combination of risk factors, identifying the chains of factors collectively contributing to fatal crashes at hotspot locations could provide added insights for improving traffic safety. By employing fatal crash data from Korea, this study identifies hotspots with increasing (critical) and decreasing (diminishing) temporal trends using a spatio-temporal hotspot analysis tool in GIS. Further, a machine learning technique is employed to explore the chains of factors influencing the number of vehicles and the number of casualties involved in fatal crashes at intersections and midblocks in each hotspot type identified. In general, results showed that minibuses/vans and construction vehicles were mainly at fault for fatal single-vehicle pedestrian-involved crashes. While many casualties and vehicles are likely to be involved in crashes at midblocks during the daytime regardless of the hotspot type, the nighttime variable was particularly associated with large casualty-size crashes at critical intersection hotspots. Further, while reckless driving was mostly associated with single-vehicle crashes at intersections in diminishing hotspots, pedestrian protection, and improper centreline crossing violations were more pronounced at midblocks in diminishing hotspots. This analysis identified groups of factors that could be collectively controlled to improve road safety and proposed countermeasures to mitigate fatal crashes on roadways.
Tamakloe, Wilson,Agyeman, Daniel Adjei,Park, Mihui,Yang, Junghoon,Kang, Yong-Mook The Royal Society of Chemistry 2019 Journal of Materials Chemistry A Vol.7 No.13
<P>A detailed understanding of the surface modification or coating of materials is becoming more important for the design and development of hybrid materials for their advanced applications. The characteristics of polydopamine-coated surfaces were explored by varying dopamine concentration and polymerization time for noble metal deposition, which is a key for the development of advanced catalysts. The variation of these parameters for dopamine coating on carbon nanofibers (CNFs) finally modulated the amount of palladium (Pd) nanoparticles deposited on the dopamine-coated surface, which is CNFs. The results showed that the higher the dopamine concentration, the larger the amount of deposited Pd, while the polymerization time is inversely proportional to the amount of Pd deposited. Thereby, the optimally functionalized surface for Pd deposition was found with a dopamine concentration of 3 mg mL<SUP>−1</SUP> and a reaction time of 6 hours (PDAT1). This optimum Pd/CNF hybrid material showed very promising electrochemical and catalytic performances with a high discharge capacity of about 5.26 mA h cm<SUP>−2</SUP> which could be maintained up to the 67th cycle at a cut-off capacity of 0.2 mA h cm<SUP>−2</SUP> in non-aqueous Li-O2 batteries and an impressive catalytic activity for the oxygen reduction reaction <I>via</I> the preferred 4 electron pathway in aqueous electrolyte.</P>