PDF(Pubmed)
Abstract：
Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents\' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model\'s dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.
摘要：
2019年冠状病毒病(COVID-19)给我们的日常生活带来了戏剧性的变化，特别是自2020年以来的人类流动性。作为大多数城市综合交通系统的主要组成部分，出租车出行代表了居民城市流动性的很大一部分。因此，量化新冠肺炎对全市出租车需求的影响，有助于更好地理解重塑的出行模式，优化公共交通运营战略，并在这场大流行的压力下收集紧急经验。为了实现目标,本研究采用地理和时间加权回归(GTWR)模型分析了COVID-19对出租车需求的影响机制。8月1日起全市出租车出行数据,2020年7月31日，纽约市的2021年被收集为模型的因变量，和COVID-19病例率，人口密度，道路密度,车站密度，选择兴趣点(POI)作为自变量。通过将GTWR模型与传统的普通最小二乘(OLS)模型进行比较，时间加权回归模型(TWR)和地理加权回归模型(GWR),对于GTWR，在时空滑行数据上的拟合优度明显更好。此外，时间分析,对GTWR模型结果进行了空间分析和疫情边际效应。本研究的结论如下：（1）病毒和医疗保健成为后疫情时代出租车需求的主要抑制和刺激因素。（2）在寒冷天气下，COVID-19对出租车需求的抑制水平更高。(3)COVID-19对出租车需求的抑制水平受到宵禁政策的严重影响。（4）尽管这种病毒在大多数时间和地点减少了出租车的需求，它仍然可以在某些特定的时间和地点增加出租车的需求。（5）随着COVID-19，体育设施和旅游业成为后疫情时代大多数地方和时间出租车需求增长的障碍。这些发现可以为政策制定者和利益相关者提供有用的见解，以在COVID-19大流行的剩余时间内提高出租车的运营效率。
