Geovisualizing the changes in metro passenger flows of Kunming under the impact of COVID-19

1. Introduction

The outbreak of COVID-19 has seriously threatened people's health. Most countries have implemented border controls, lockdowns, social isolation, and other measures to inhibit the spread of COVID-19, which consequently affected numerous aspects of people's lives, especially their use of public transport (Mützel et al., 2021). Metro systems, as important mass transit within cities, undertake people's vast travel demands. However, during the sudden outbreak of disasters, it is difficult to hastily make the most reasonable response, because transportation departments usually lack a comprehensive, rapid, and quantitative understanding of the performance of metro systems and the existing travel demand (Steenhuisen, 2005). Some studies have confirmed that COVID-19 would significantly reduce metro passenger flows, which is difficult to recover in a short time. Meanwhile, there are differences in passenger flow changes of different types of stations (Chang et al., 2021). In China, metro passenger flow during the pandemic was more affected by the supply side, while the period after the lifting of restrictions, were mainly affected by travel demand (Xiang et al., 2021). This short study aims to explore the spatio-temporal differentiation of metro passenger flows at the station level through geo-visualization of metro passenger flows before, during, and after the pandemic. The results provide empirical evidence for administrators to formulate reasonable public transport plans and urban construction.

2. Materials and methods

Kunming is a fast-growing metro and second-tier city in western China. According to the Kunming Urban Master Plan (2011−2020), we delineated the boundaries of the old and new city centers (the new one is also the main non-recreational employment center in Kunming). By 2019, the operational mileage of the metro in Kunming was 88.7 km, ranking 23rd among 40 cities in China. Taking Kunming as the sample can provide evidence for many second-tier cities in the stage of fast metro construction.

We define the ratio of passenger flows during the pandemic (Feb 2020) to that before the pandemic (Nov 2019) as ratio A, and the ratio after the pandemic (May 2020) to that before the pandemic (Nov 2019) as ratio B. The normalized ratio A and B are divided into five equal parts by the equal interval classification, and geo-visualized in the form of choropleth maps in two images respectively. Both the size and color of the circles represent the ratios. The time periods were chosen for several reasons. First, the absence of holidays throughout November 2019 could depict the normal state of Kunming. Second, Kunming restricted people's movement to the greatest extent from February 5, 2020, to March 1 (From February 1st to 21st, only seven metro stations were operational). Moreover, the local government encouraged people to resume their normal life in April and metro travel revived rapidly in May 2020.1 Metro passenger flow data were obtained from the metro smart card data of Kunming Metro Group Co.

3. Result

3.1. Stations of old city, new city, and non-city center areas

In Fig. 1 , the mean value of stations' ratio A in the new city center (0.254) was higher than that in the old city center (0.209) but similar to that in non-city center areas (0.236). The reason for these differences may be due to the new city center being mainly non-recreational employment-based, and that some people still chose metro for commuting during the pandemic. These differences also reflected that people avoid metro travel to old city centers with the highest population densities to reduce the risk of infection. Meanwhile, no significant differences existed between the mean value of ratio B in new city centers (0.528) and that in old city centers (0.58); however, both were lower than that in non-city centers (0.674), indicating that after the pandemic, people preferred metro traveling to less densely populated areas in case of infection.

Fig. 1.

Changes of metro passenger flows in Kunming during and after the pandemic.

3.2. Stations being still operational and closed during the pandemic

To understand the differences between stations being operational and closed by administrative order during the pandemic, this study analyzed not only the differences of ratio A and B between operational and closed metro stations during the pandemic, but also that among operational ones.

In Fig. 1, the mean value of ratio A in operational stations during the pandemic was 0.840, which was much higher than that of others (0.149). Station's passenger flows were largely affected by administrative orders rather than the spontaneous choice of travelers. Moreover, contrary to expectations, the mean value of ratio B in operational stations during the pandemic was only 0.575, even lower than that of others (0.638). This showed that people were more willing to use the metro at these closed stations after the pandemic, than during the pandemic.

Metro stations can further be divided into two types, based on land-use types around the operational stations during the pandemic: transfer stations (including bus and train stations for inter-city travel) and job-residence stations (residential and commercial land account for more than 70% of the 800-m buffer area around the station).2 In Fig. 1, the mean value of ratio A in transfer stations (0.813) was less than that of job-residence stations (0.860). Meanwhile, the mean value of ratio B in transfer stations (0.535) was also less than that of job-residence stations (0.605), indicating that people would take the metro to the transfer station for intercity travel during and after the pandemic (Wang et al., 2021), and that the demand is less than that to job-residence stations.

4. Conclusion

Based on passenger flow data, we found that in Kunming some people still took the metro to the employment center during the pandemic, largely for commuting purposes. Furthermore, during and after the pandemic, people would avoid traveling to high population density areas by metro to reduce the risk of infection. Solving this concern may be an efficient way to promote the passenger flow of these places.

In addition, the transportation department believed that operational stations (transportation transfer and job-residence stations) were more important than others (such as shopping or scenic stations) during the pandemic. However, the higher ratio A and lower ratio B of operational stations during the pandemic reflected that the administration did not satisfy travel demand after the pandemic. Moreover, travel demand to job-residence stations for commuting were greater than that to the transfer stations for inter-city travel during and after the pandemic. In the future, the transportation department should consider how to make a comprehensive and rapid judgment on the performance evolution of the metro system and the relevant intra-city commuting travel demand when metro systems suffer a disturbance.

Funding

National Natural Science Foundation of China (42001123).

CRediT authorship contribution statement

Qinran Zhang: Data curation, Formal analysis, Writing – original draft, Visualization. Haoran Yang: Conceptualization, Supervision, Methodology, Resources, Writing – review & editing, Funding acquisition.