Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 서원호 | - |
dc.date.accessioned | 2023-05-17T04:29:29Z | - |
dc.date.available | 2023-05-17T04:29:29Z | - |
dc.date.issued | 2023-03 | - |
dc.identifier.citation | SUSTAINABILITY, v. 15, NO. 5, article no. 4421.0, | - |
dc.identifier.issn | 2071-1050;2071-1050 | - |
dc.identifier.uri | https://www.mdpi.com/2071-1050/15/5/4421 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/180635 | - |
dc.description.abstract | To monitor air pollution on roads in urban areas, it is necessary to accurately estimate emissions from vehicles. For this purpose, vehicle emission estimation models have been developed. Vehicle emission estimation models are categorized into macroscopic models and microscopic models. While the calculation is simple, macroscopic models utilize the average speed of vehicles without accounting for the acceleration and deceleration of individual vehicles. Therefore, limitations exist in estimating accurate emissions when there are frequent changes in driving behavior. Microscopic emission estimation models overcome these limitations by utilizing the trajectory data of each vehicle. In this method, the total emissions in a road segment are calculated by adding together the emissions from individual vehicles. However, most research studies consider the total vehicle emissions in a road section without considering the difference in vehicle emissions at different locations of a selected road section. In this study, a road segment between two intersections was divided into sub-sections, and energy consumption and emission generation were analyzed. Since there are unique driving behaviors depending on the section of the road segment, energy consumption and emission generation patterns were identified. The findings of this study are expected to provide more detailed and quantitative data for better modeling of energy consumption and emissions in urban areas. | - |
dc.description.sponsorship | This work was partially supported by NRF-2020R1A2C1011060 and NRF-2022K1A3A1A09078712 Data4Transport - Harnessing Big data to improve intersection traffic operation in urban cities (Korean government). | - |
dc.language | en | - |
dc.publisher | MDPI | - |
dc.subject | MOVES | - |
dc.subject | emission | - |
dc.subject | energy | - |
dc.subject | signalized intersection | - |
dc.subject | sub-section | - |
dc.subject | microscopic analysis | - |
dc.title | Road-Section-Based Analysis of Vehicle Emissions and Energy Consumption | - |
dc.type | Article | - |
dc.relation.no | 5 | - |
dc.relation.volume | 15 | - |
dc.identifier.doi | 10.3390/su15054421 | - |
dc.relation.journal | SUSTAINABILITY | - |
dc.contributor.googleauthor | Jang, Sunhee | - |
dc.contributor.googleauthor | Song, Ki-Han | - |
dc.contributor.googleauthor | Kim, Daejin | - |
dc.contributor.googleauthor | Ko, Joonho | - |
dc.contributor.googleauthor | Lee, Seongkwan Mark | - |
dc.contributor.googleauthor | Elkosantini, Sabeur | - |
dc.contributor.googleauthor | Suh, Wonho | - |
dc.sector.campus | E | - |
dc.sector.daehak | 공학대학 | - |
dc.sector.department | 교통·물류공학과 | - |
dc.identifier.pid | wonhosuh | - |
dc.identifier.article | 4421.0 | - |
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