篇名 | COVID-19三級警戒下臺灣中長程運具選擇之分析 |
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卷期 | 35:2 |
並列篇名 | A Study on Long-distance Travel Behavior and Mode Preferences under COVID-19 Level 3 Alert in Taiwan |
作者 | 胡大瀛 、 洪于鈞 |
頁次 | 161-192 |
關鍵字 | COVID-19 、 多項羅吉特 、 運具選擇 、 探索性因素分析 、 COVID-19 、 Multinomial logit model 、 Mode choice behavior 、 Exploratory factor analysis 、 TSSCI |
出刊日期 | 202306 |
DOI | 10.6383/JCIT.202306_35(2).0002 |
2019年底全球爆發新型冠狀病毒(COVID-19),為了防止病毒的擴散,各國政府陸續實施鎖國政策,人民的生活方式開始改變。2021年5月臺灣爆發第二波疫情,確診人數在數日內大幅上升,政府遂在5月19日宣布全國第三級疫情警戒。此後兩個月內,臺鐵及高鐵每日搭乘人次較去年同期下降8至9成,顯示臺灣民眾的旅次產生顯著的變化。本研究以顯示性偏好法設計問卷,並以多項羅吉特模型校估,建構旅運者於警戒前後中長程運具選擇之模式。其中以探索性因素分析法確認研究問項之潛在構面,發現警戒後影響旅運者運具選擇之因素存在防疫考量之因素。羅吉特模型校估結果顯示,警戒前後影響旅運者之選擇偏好明顯不同,警戒後也因加入防疫考量之方案特定變數,提升模型解釋力。
At the end of 2019, a global outbreak of the new coronavirus (COVID-19) emerged. To prevent the spread of this high-infectious and high-mortality virus, various governments have successfully implemented lockdown policies, and peoples lifestyles have begun to change. The number of confirmed cases rose sharply after the epidemics broke out again in Taiwan in May 2021. The government announced a national third-level epidemic alert on May 19 to control the spread of the virus. Within two months, the number of daily passengers on the Taiwan Railways and Taiwan High Speed Rail has dropped by 80% to 90% compared with the same period last year, showing that some people in Taiwan have changed their traveling behavior. In this study, the questionnaire was designed by the revealed preference method. We use the Multinomial Logit Model to calibrate the model, constructing a model for travelers’ choice of long-range transportation before and after the epidemic alert. Exploratory Factor Analysis is used to identify the latent variables, and it is found that the factors that affect the choice of transportation after the alert include “epidemic prevention considerations.” The result of the Multinomial Logit Model shows that the travelers’ preferences for transportation before and after the alert are significantly different. The addition of explanatory variable “epidemic prevention considerations” improved the model explanatory power after the alert.