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標題: | 利用自然語言深度學習及 COVID-19 之 CDC 每日新聞稿預測交通量及空氣污染量 Traffic Volume and Traffic Related Air Pollution (TRAP) Estimation through Deep Learning Natural Language Processing of COVID-19 CDC Press Release |
作者: | 鍾慕捷 Mu-Chieh Chung |
指導教授: | 陳柏華 Albert Y. Chen |
關鍵字: | COVID-19疫情,交通量預測,空氣污染,深度學習,自然語言處理,BERT, COVID-19 Pandemic,Traffic Prediction,Air Pollution,Deep Learning,Natural Language Processing (NLP),BERT, |
出版年 : | 2023 |
學位: | 碩士 |
摘要: | COVID-19 的疫情對我們生活上的各個層面皆帶來重大的影響,其中包含了 交通運輸以及交通運輸所帶來的空氣污染量。許多研究指出上述的兩項指標與疫 情的嚴峻程度具有高度相關,然而僅透過確診數作為判斷疫情嚴峻程度的依據, 可能忽略因為政府的相關政策而產生的變化。
本研究旨在從文本資料的角度分析疫情下之交通運量以及空氣污染量的改 變。我們使用基於 BERT 的自然語言處理模型,從台灣疾病管制署(CDC)每日 發佈的新聞稿中收集文本數據,以捕捉人們在疫情相關的資訊和政策之下在交通 行為上的變化,並針對台北捷運在疫情期間的運量以及各項與交通運輸相關的空 氣污染指標進行預測,藉此分析疫情在這些不同層面上所帶來的影響。 本研究示範並比較了透過文本資料分析疫情對台北捷運運量及空氣污染量的 影響的多種方法及其相對應的結果,並在捷運運量的預測上得到 18.8% 的平均絕 對百分比誤差,而在空氣污染量的方面,則分別得到一氧化碳 14.4%、二氧化氮 19.9%、二氧化硫 14.9%、臭氧 25.6% 以及 PM10 19.1% 的平均絕對百分比誤差。 政策制定者可以參考我們的研究結果,提供有效的運輸服務,以應對人們在疫情 期間不斷變化的交通需求。 The COVID-19 pandemic has had a significant impact on various aspects of our lives, including transportation and the associated air pollution levels. Many studies have indi- cated a strong correlation between these two indicators and the severity of the pandemic. However, relying solely on the number of confirmed cases as a measure of the severity of the pandemic may overlook changes resulting from government policies. This study aims to analyze changes in transportation volume and air pollution levels during the pandemic from a textual data perspective. We utilize a BERT-based natural language processing model to collect textual data from daily press releases issued by the Taiwan Centers for Disease Control (CDC). The data allow us to capture changes in peo- ple’s transportation behaviors under pandemic-related information and policies. We then predict the traffic volume and various air pollution indicators related to the Taipei Metro during the pandemic, thereby analyzing the impact of the pandemic on these different aspects. This study demonstrates and compares multiple methods for analyzing the impact of the pandemic on Taipei Metro ridership and air pollution levels using textual data. In terms of metro ridership prediction, an average absolute percentage error of 18.8% was obtained. For air pollution levels, the average absolute percentage errors were as follows: 14.4% for CO, 19.9% for NO2, 14.9% for NO2, 25.6% for O3, and 19.1% for PM10. Policy makers can refer to our research findings to provide effective transportation services to meet the changing demands of people during the pandemic. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88920 |
DOI: | 10.6342/NTU202303637 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 土木工程學系 |
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