The prevention and control of COVID-19 in megacities is under large pressure because of tens of millions and high-density populations. The majority of epidemic prevention and control policies implemented focused on travel restrictions, which severely affected urban mobility during the epidemic. Considering the impacts of epidemic and associated control policies, this study analyzes the relationship between COVID-19, travel of residents, Point of Interest (POI), and social activities from the perspective of taxi travel. First, changes in the characteristics of taxi trips at different periods were analyzed. Next, the relationship between POIs and taxi travels was established by the Geographic Information System (GIS) method, and the spatial lag model (SLM) was introduced to explore the changes in taxi travel driving force. Then, a social activities recovery level evaluation model was proposed based on the taxi travel datasets to evaluate the recovery of social activities. The results demonstrated that the number of taxi trips dropped sharply, and the travel speed, travel time, and spatial distribution of taxi trips had been significantly influenced during the epidemic period. The spatial correlation between taxi trips was gradually weakened after the outbreak of the epidemic, and the consumption travel demand of people significantly decreased while the travel demand for community life increased dramatically. The evaluation score of social activity is increased from 8.12 to 74.43 during the post-epidemic period, which may take 3–6 months to be fully recovered as a normal period. Results and models proposed in this study may provide references for the optimization of epidemic control policies and recovery of public transport in megacities during the post-epidemic period.

Impact of COVID-19 on urban mobility during post-epidemic period in megacities: from the perspectives of taxi travel and social vitality

Zooming into mobility to understand cities: A review of mobility-driven urban studies

https://doi.org/10.1016/j.tranpol.2022.03.002

How does COVID-19 pandemic impact cities' logistics performance? An evidence from China's highway freight transport

As a crucial travel mode, taxi plays a significant role in residents' daily travel. Uncovering taxi traffic demand has become a hotspot in transport studies. Previous researchers pay more attention to the statistical characteristics of taxi trips, while few studies focus on the dynamic features in different periods of a day. In this article, we study the taxi travel demand by constructing dynamic networks based on taxi trajectory data. In addition, relationship between travel intensity and point of interest (POI) in Xiamen, China is discussed. Firstly, the study area is divided by 1km × 1km uniform cells. The pick-up and drop-off activities of passengers are recorded for each cell. Secondly, the networks are constructed by regarding each cell as a node and regarding taxi trips from a cell to another cell as an edge. On this basis, we divide a day into 12 periods by two hours and construct the networks for different periods. Finally, correlation between travel intensity and POI intensity is detected with regression analysis. Results show that the taxi trip networks have large clustering coefficient and small shortest path length, which indicates they are `small world' networks. Moreover, the taxi trip networks are disassortative networks that hotspot areas tend to connect with the common areas. Furthermore, the taxi trip length in a day follows a lognormal distribution and the peak hour of taxi trip appears around midnight. Finally, a cubic polynomial curve could fit the relationship between travel intensity and POI intensity. Our findings provide a new insight for understanding the traffic demand of taxi.

/pdf/revealing-urban-traffic-demand-by-constructing-dynamic-3q2qitxy46.pdf

Revealing Urban Traffic Demand by Constructing Dynamic Networks With Taxi Trajectory Data

As an important service industry in cities, taxis provide people with an all-weather travel mode. And its demand is greatly affected by the internal functions of the city. It is very important to understand the relationship between the mixed degree of urban internal functions and the residents' taxi travel demand to alleviate traffic congestion and formulate corresponding urban traffic strategies. This paper combined two heterogeneous data in the main urban area of Xi'an, urban points of interest (POIs) and taxi GPS. Firstly, a spatial information entropy model was constructed to quantitatively evaluate the mixed degree of functions in different spaces within the city. Secondly, the kernel density estimation method was used to analyze the spatial distribution evolution characteristics of residents' taxi travel demand. A geographically weighted regression (GWR) model was further used to study the spatial and temporal influences of the mixed degree of urban internal functions on taxi travel demand. Results indicate that there is an obvious spatiotemporal pattern in the impact of the mixed degree of urban functions on taxi travel demand. And the GWR model is used to study the impact is superior to the ordinary least squares (OLS). In more developed areas, improving the mixed degree of urban functions will be more attractive than backward areas. It is also found that although the single function of the city has an impact on the taxi travel demand, the result of the single function is not ideal. This study can provide a reference for the optimal combination of basic units of urban space in urban planning, promote the balance of supply and demand of urban taxis, rationalize urban taxis' operation and allocation, and solve the problems of urban transportation systems.

/pdf/impact-of-the-mixed-degree-of-urban-functions-on-the-taxi-17siilqu30.pdf

Impact of the mixed degree of urban functions on the taxi travel demand.

Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning.

/pdf/spatial-variation-of-taxi-demand-using-gps-trajectories-and-ehta7378bw.pdf

Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

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Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data