The abrupt COVID-19 epidemic has disrupted the normal economic and social development in China and throughout the world, especially for megacities because of their large populations [1,2]. Strict epidemic control measures have been adopted in many cities such as lockdown and work suspension because of the necessity of epidemic prevention and control, which have played an important role in controlling the rapid and large-scale spread of COVID-19 [3,4,5,6]. Undoubtedly, those epidemic control policies reduced the number of social and economic activities and had significant impacts on urban transport systems such as the sudden decrease of trips and the change of travel mode. Recently, the epidemic situation has been gradually alleviated and the economic and social recovery plan has been put on the agenda by the government during the post epidemic period. Under this circumstance, urban transport system plays a crucial role in the process of social recovery and economic recovery as the basic guarantee of the city. Therefore, more attentions should be paid on the impacts of the epidemic on urban transport system as well as the travel behavior.

Human beings have taken the most extensive and strict control measures to face the unprecedented epidemic. Infectious diseases are a major problem in public health prevention because of its high contagiousness. Therefore, it is essential to control or block the spread of virus from person-to-person and treat the infected person with entire efforts [7,8,9]. Based on the experiences from another epidemic in 2002 called SARS (Severe Acute Respiratory Syndrome), many similar measures were applied at the beginning of the prevention and control of COVID-19, such as active case screening, contact tracing, isolation of infected people and all associated contacts, social distancing, and community containment [10]. In fact, countries or regions that have been seriously affected by SARS are more experienced in coping with COVID-19. Control measures adopted by China have quickly alleviated the spread of the virus [11]. Some other studies have demonstrated the significance of the control measures applied during the first 50 days and suggested that Hubei Province and other megacities in China should extend their control periods [12,13]. Many traditional public health control measures such as isolation and quarantine, social distancing, and community containment could be applied for the prevention and control of large-scale spread of COVID-19 [14]. At the same time, it is also effective to strengthen hospital surveillance and infection control [15], reduce the contact rate of susceptible and infected residents, and isolate the infected people [16]. An extension of the traffic control bundling has been proved to be able to interrupt the community-hospital-community transmission cycle and thus reduce the impact of COVID-19 [17]. Research on epidemic specific containment measures and death rates in European countries showed that the speed of response along with the decision to suspend international flights might determine the impact of epidemic outbreak on fatality [18].

The essence of epidemic control measures is to restrict the movement and gathering of people which can normally be conducted by travel restrain. Statistical data have indicated that the number of national railway, highway, waterway, and civil aviation passengers in China during the Spring Festival in 2020 is only 49.7% of that number in 2019 [19]. Meanwhile, the operation of urban public transport travels were significantly affected by the epidemic. For example, bus and urban rail transit have taken certain measures which includes the reduction of frequency, extension of departure interval, and adjustment of operation time based on the travel demand. Therefore, the total passenger traffic in central cities across China in February 2020 was 50.3% of the same period in 2019, which continuously decreased to 43.4% in March 2020 [20]. Public transport users have dropped by more than 90% in some European cities [4,21]. Interestingly, some studies have demonstrated that the interventions to control the COVID-19 outbreak led to an improvement of air quality which could bring health benefits for non-COVID-19 deaths and potentially outnumber the confirmed deaths caused by COVID-19 in China [22].

Several concerns about the impacts of the epidemic on transport system and travel behavior are emerged after the outbreak of COVID-19. For example, many scholars are interested in the differences in the travel behavior of residents compared to the normal period and what factors may affect the travel behavior of residents as well as the level of economic and social recovery in the post epidemic period. Huang et al. quantified the impact of COVID-19 on transportation-related behaviors of the public based on the navigation record, indicating that the COVID-19 epidemic did cause a great impact on transportation-related behaviors of the public in Mainland China [23]. Wilbur et al. founded that there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77% vs. 58%) in Nashville, and they believed that the epidemic has a greater impact on low-income groups [24]. Arellana et al. used official and secondary data from the top seven most populated cities in Colombia to analyze the impacts on air transport, freight transport, and urban transport. The results showed that national policies and local decisions have reduced the demand for the transport system [25].

The travel mode choice behavior was also influenced by the epidemic. During the epidemic period, people try to avoid crowded places because they are required to keep a social distance. However, since it is difficult to satisfy the requirement of a social distance of more than 2 meters on most public transport vehicles, many people treat public transport as an unsafe transport mode during the epidemic period [24]. It is obvious to all that the trips of public transport decreased dramatically during the epidemic period, and many researchers believe that the trips of public transport will maintain a low level for a long time, which will be replaced by the increase of other transport modes such as private vehicles, non-motorized vehicles, and walking [26]. Public transport must change the impression of insecurity for attracting more passengers, and it is necessary for the government to strengthen the policy support for public transport in the post epidemic period because public transport has a great impact on many social issues, such as social equity and sustainable development [27].

The assessment of the impact of the epidemic on public transport and social economy is very important for the reconstruction work in the post epidemic period. The economic development has been slowed down and the transport industry has been seriously impacted due to the epidemic [28]. It is difficult to predict what the city will look like after the epidemic, but it is assured that the economic recovery will not be achieved overnight [29]. Meanwhile, it is necessary to assess the extent of socio-economic and transport impacts caused by the epidemic for the better guidance of the economic recovery. Tang et al. proposed a Bayesian Network Model based on a function-oriented resilience framework and ontological interdependence among 10 system qualities to probabilistically assess the general resilience of the road transport system in Beijing from 1997 to 2016 [30]. A resilient transport system will enhance its ability to resist risks and ensure that it can continue to play a role under the influence of emergencies. Resilient and sustainable infrastructure will continue to be critical to addressing evolving natural and man-made hazards in the 21st Century [31]. Wang et al. applied the complex network theory to establish a model of air sector network in China and examined a series of characteristic parameters with an empirical analysis on its vulnerability and resilience [32]. From the perspective of mobility, Huang et al. proposed two new economic indicators as the complementary measures to domestic investments and consumption activities by using data from Baidu Maps [33]. Gössling et al. analyzed the long-term impact of the epidemic on tourism and discusses the recovery assessment of tourism in the future [34]. Dang et al. evaluated the economic recovery of Vietnam in the post epidemic period. A web-based rapid assessment survey was implemented and analyzed in Vietnam to investigate household finance and future economic expectations in developing countries [35].

As an important supplement of public transport in most megacities, taxies can record the exact time and location of departure and arrival, and the boarding and alighting locations of taxi passengers are closer to the origin and destination of trips compared to other public transport modes [36]. The 24-h continuous operation of taxies can reflect the demand and dynamic change of urban traffic [37]. Therefore, it is more suitable to use data of taxi travel to conduct the travel temporal-spatial analysis based on the several reasons mentioned above. Point of interest (POI) is the precise positioning of urban function points, which has been proved to have a strong correlation with travel behaviors [38]. Taxi trajectory data combined with POI data is usually used to analyze the relationship between travel behavior and urban land use in many studies [39,40].

Most recent research which studied the interaction between COVID-19 and mobility mainly focused on the impact of the epidemic on the travel trips by analyzing the changes of number of trips between the normal period and the epidemic period. However, fewer studies were conducted on the changes in the temporal and spatial dimension of travel. Meanwhile, it is widely believed that the travel behavior of residents is strongly related to social activities and epidemic control policies during the epidemic period. In this case, we hope to explore the impacts of different control policies on travel behavior during the epidemic period in this study. Moreover, the main driving factors of travel and the changes occurred with the impact of urban land use are also investigated. The answers to these questions are of great importance to the improvement of epidemic prevention and control as well as the planning and construction of sustainable cities and sustainable transport in the future. Current transport related research during the epidemic period mainly focused on the changes of trips such as bus, rail transit, and aviation. However, there are still many restrictions on these modes of transport, such as the limitations on travel time and travel area. For example, most public transport vehicles usually stop operating at night and cannot reach anywhere in the city. Therefore, it is novel and creative to study the impact of COVID-19 on travel behavior and transport system from the perspective of taxi trips.

In addition, the epidemic has had a serious impact on the economy that the average income of people have dropped sharply and many people even lost their jobs [41,42,43]. Under this circumstance, economic recovery is treated as the primary task for the post epidemic period. Compared to the assessment based on investigation or statistics which is usually expensive and time-costing, it is important to formulate the economic policies by evaluating the social vitality in a relatively short time.

By considering several research gaps mentioned above, this paper analyzes the impact of COVID-19 on travel of people from the perspective of taxi travel and epidemic control policies. First, changes in the characteristics of taxi trips at each period of the epidemic were analyzed. Next, the relationship between POIs and taxi travels was established by the GIS method, and the spatial lag model (SLM) was introduced to explore the changes in taxi travel driving force. Finally, a social activities recovery level evaluation model was proposed based on the taxi travel datasets to evaluate the recovery level of social activities.

The implications and contributions of this study are summarized as follows:

This paper supplements the lack of research on the impact of COVID-19 on taxi travel and enriches the research on the impact of the epidemic on urban traffic. Due to the fact that the taxi has the advantages of 24-h operation and can operate in any area of the city, the scope of time and space for this research is expanded and travel characteristics of residents are captured in the whole day and the whole city to study the spatial and temporal changes of taxi trips. Moreover, operation information included in the taxi datasets are used to study the change of taxi operation and analyze the degree and trend of the impact of the epidemic on the income of drivers.

In this study, the trip information and travel behavior are related to POI by the GIS method. The spatial econometric model is introduced to evaluate the change of taxi travel driving force and the relationship between the spatial-temporal evolution of travel and urban functional structure during the epidemic period. A previous study mentioned that POIs have numerous attribute information which are related to the types of land use [44]. Therefore, POIs can accurately represent the distribution characteristics of urban function points and have a strong correlation with travel purpose [45]. Moreover, this study is able to study the relationship between the distribution of POI and origin and destination of taxi trips because the spatial information of taxi travel is also included in taxi datasets. Since the construction of the social activity recovery level evaluation model during the post epidemic period is only relied on the travel data of taxi, this study also proposed an alternative model which can evaluate the recovery level with relatively small data sample.