Spatial and temporal analysis of scooter-induced traffic patterns and their environmental implications Article Swipe
I‐Chun Tsai
,
Chenwei Lin
,
Shih‐Hao Su
,
Chiao-Wei Chang
,
Chih-Wen Su
,
Shih‐Chun Candice Lung
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aeaoa.2024.100291
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aeaoa.2024.100291
Scooters are a popular means of transportation in urban areas. However, studies examining their spatial and temporal features are lacking. This study examined traffic patterns in New Taipei City from September 2021 to June 2023 using real-time surveillance camera images and the YOLOv4 algorithm. The focus of the study was to investigate the role of scooters in urban transportation and their effect on air quality. The findings revealed that sedans and scooters accounted for approximately 90% of the total vehicles, and their usage exhibited significant spatial and temporal variations across the city. Sedans were more prevalent in rural areas, whereas scooters were predominant in urban and suburban regions. An examination of diurnal patterns revealed that peak traffic occurred during early morning and evening rush hours, with distinct usage patterns between weekdays and weekends. Through hierarchical clustering, the city's stations were categorized into three types based on the dominant vehicle usage: sedan-dominant, sedan-prevailing, and scooter-dominant. The analysis also established a correlation between the number of vehicles and air pollution, highlighting the significant role of sedans and scooters as primary sources of emissions, particularly in areas where scooters were dominant. This suggests that the current emission inventories may underestimate the impact of scooters and buses on air quality while overestimating the trucks' contribution. Consequently, this study emphasizes the significance of scooters in determining urban air quality and advocates for improved monitoring and image identification technologies to accurately assess the numbers and speeds of scooters. These measures are essential for improving the accuracy of emissions inventories and forecasts, which are crucial for effective urban air quality management.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.aeaoa.2024.100291
- OA Status
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4401798766Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.aeaoa.2024.100291Digital Object Identifier
- Title
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Spatial and temporal analysis of scooter-induced traffic patterns and their environmental implicationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-01Full publication date if available
- Authors
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I‐Chun Tsai, Chenwei Lin, Shih‐Hao Su, Chiao-Wei Chang, Chih-Wen Su, Shih‐Chun Candice LungList of authors in order
- Landing page
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https://doi.org/10.1016/j.aeaoa.2024.100291Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.aeaoa.2024.100291Direct OA link when available
- Concepts
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Environmental science, Geography, Transport engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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17Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.variations | 88 |
| abstract_inverted_index.categorized | 140 |
| abstract_inverted_index.clustering, | 135 |
| abstract_inverted_index.correlation | 159 |
| abstract_inverted_index.determining | 220 |
| abstract_inverted_index.established | 157 |
| abstract_inverted_index.examination | 109 |
| abstract_inverted_index.inventories | 194, 252 |
| abstract_inverted_index.investigate | 51 |
| abstract_inverted_index.management. | 263 |
| abstract_inverted_index.predominant | 102 |
| abstract_inverted_index.significant | 84, 170 |
| abstract_inverted_index.hierarchical | 134 |
| abstract_inverted_index.highlighting | 168 |
| abstract_inverted_index.particularly | 181 |
| abstract_inverted_index.significance | 216 |
| abstract_inverted_index.surveillance | 37 |
| abstract_inverted_index.technologies | 232 |
| abstract_inverted_index.Consequently, | 211 |
| abstract_inverted_index.approximately | 74 |
| abstract_inverted_index.contribution. | 210 |
| abstract_inverted_index.underestimate | 196 |
| abstract_inverted_index.identification | 231 |
| abstract_inverted_index.overestimating | 207 |
| abstract_inverted_index.transportation | 6, 58 |
| abstract_inverted_index.sedan-dominant, | 150 |
| abstract_inverted_index.scooter-dominant. | 153 |
| abstract_inverted_index.sedan-prevailing, | 151 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.11386227 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |