Abstract
The emerging Omicron variant poses a serious threat to human health. Public transports play a critical role in infection spread. Based on the data of nearly 4 billion smartcard uses, between January 1, 2019 and January 31, 2021 from the Mass Transit Railway Corporation of Hong Kong, we analyzed the subway travel behavior of different population groups (adults, children, students and senior citizens) due to the COVID-19 pandemic and human travel behavior under different interventions (e.g. work suspension, school closure). Due to the pandemic, the number of MTR passengers (the daily number of passengers in close proximity in subway carriages) decreased by 37.4% (40.8%) for adults, 80.3% (78.5%) for children, 71.6% (71.6%) for students, and 33.5% (36.1%) for senior citizens. Due to work from home (school suspension), the number of contacted adults (students/children) in the same carriage during the rush hours decreased by 39.6% (38.6%/43.2%). If all workers, students, and children were encouraged to commute avoiding rush hours, the possible repeated contacts during rush hour of adults, children and students decreased by 73.3%, 77.9% and 79.5%, respectively. Since adults accounted for 87.3% of the total number of subway passengers during the pandemic, work from home and staggered shift pattern of workers can reduce the infection risk effectively. Our objective is to find the changes of local travel behavior due to the pandemic. From the perspective of public transports, the results provide a scientific support for COVID-19 prevention and control in cities.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Since first emerging in the end of 2019, COVID-19 has been threatening human lives and societies. As the Omicron variant first appeared at the end of 2021 (Del Rio et al., 2022), it spread rapidly around the globe. By the end of October 2022, more than 623 million infections and 6.6 million deaths had been reported globally (WHO 2022). The Omicron variant is highly infectious with a basic reproductive number R0 of 4.5–10, which is several times that of the Delta variant (Baker et al. 2021; Halamicek et al. 2022; Davido et al. 2022). As one of the cities with the highest population density, Hong Kong had a total of 2.4 million (32% of the total population) confirmed COVID-19 cases by the end of December 2022 (NHKG 2022a). Of the 10,000 plus reported cases after February 2022, more than 80% of these cases were infected with the Omicron variant (NHKG 2022b). Due to the high infectivity of the Omicron variant, interventions which had been effective for prevention and control of previous variants (e.g. Delta) were unable to meet the current needs, and a serious outbreak was triggered in Hong Kong.
Close contact (short-range airborne and large droplet), long-range airborne and fomite transmission are the three potential transmission routes for SARS-CoV-2 (Lotfi et al. 2020; Rahman et al. 2020). Many studies have shown that close contact is the dominant route of COVID-19 transmission (Karia et al. 2020; Zhao et al. 2020), Maintaining a social distance of 1.5 m is considered to be the most effective intervention for most respiratory infectious diseases including COVID-19 transmission (Qian and Jiang 2022; Sun and Zhai 2020; Aquino et al. 2020). and researchers have emphasized the importance of social distancing in disease prevention through modeling and simulation (Chinazzi et al. 2020). However, assessing people’s travel behavior is an important way to quantify social distancing (Anderson et al. 2020). Many countries and cities have implemented social distancing measures such as work suspension (Ruiz-Frutos et al. 2021), school closure (Zhang et al. 2020) and limits on bars and restaurants (Abouk and Heydari 2021).
Public transport is important for the local travel needs of residents. It not only provides a venue for infection spread, but also connects people from different regions (Zheng et al. 2020). Therefore, prevention and control of COVID-19 on public transport is particularly important. Taking influenza as an example, without mask wearing, about 4% of infections occur during travel on subways (Cooley et al. 2011). Therefore, for public transportation using the subway as an example, it is very important to formulate effective epidemic prevention and control strategies according to the travel behavior of passengers. Especially because of the omicron variant with a high basic reproductive number of R0 pose a great risk of transmission.
Based on data analysis and simulations, many studies have proposed interventions for COVID-19 prevention and control on public transport (Ozdemir et al. 2022; Shen et al. 2020). However, these studies had a number of shortcomings. Firstly, individual differences were often ignored (Zhang et al. 2021a), for example, susceptibility to viruses (Vakili et al. 2020), as well as transport system usage, differ according to age. Secondly, due to the lack of data concerning real human close contact behaviors, it is difficult to accurately evaluate close contact transmission in subways (Nissen et al. 2020). Finally, many studies did not base their results on real public transport operational data, and therefore cannot propose accurate strategies for COVID-19 prevention and control (Guan et al. 2020).
In Hong Kong, the subway, known as the Mass Transit Railway (MTR), is the most used form of public transport, accounting for more than 40% of local passengers (Zhang et al. 2021b). For this study, we obtained nearly 4 billion smartcard use data for four population groups (adults, children, students and senior citizens) from January 1, 2019 to January 31, 2021. Taking the four waves of COVID-19 outbreaks during this period as examples, we analyzed changes in local travel behavior due to these pandemic waves. Based on the real people’s travel behavior before the pandemic, different interventions (work from home, school suspension, staggered shift travel pattern, and reduction on subway riding) are proposed to ensure social distance, and by simulating the behavior of people under different epidemic prevention measures. This provides scientific support for strategies to deploy for COVID-19 prevention and control in the subway system.
Method
Data sources
Nearly 4 billion smartcard use data from January 1, 2019 to January 31, 2021 was obtained from the Mass Transit Railway Corporation (MTRC) of Hong Kong. This data included entry and exit station, the entry and exit time at the second level, the type of smartcard, etc. All transport system users were divided into four categories based on the card type: child (aged between 3 and 11), student (aged between 12 and 25 enrolled in primary/secondary/high school, university, or higher education institution), adult (aged between 19 and 65 excluding students), and senior citizen (aged over 65 years). In addition, pandemic-related data (e.g. daily number of confirmed cases) was obtained from the Center for Health Protection of Hong Kong (HKCHP 2021). The detailed data of smartcard swiping is shown in Table 1.
Study area
This study aims to reduces the interpersonal contacts by changing travel behaviors to reduce the infection risk in subways. A typical subway train in Hong Kong consists of eight carriages, and each carriage is 22 m long and 3.2 m wide (Baidu, 2022).
Due to the huge difference on local travel behavior between weekdays and weekends, local travel behavior and efficiency assessment for interventions for both weekdays and weekends were analyzed. Considering that many workers in Hong Kong need to work on Saturday, in this study, only Sunday was regarded as weekend.
Rush hours: Rush hours are the times of the day when the number of passengers in the carriage reach the peak. The rush hours for adults (Children/students) were 7:30–9:00 and 18:00–19:30 (7:00 to 8:00 and 15:30 to 16:30).
Non-rush hours: Train operation period except rush hours.
There were four waves of COVID-19 outbreaks in Hong Kong between January 1, 2020 and January 31, 2021. During each wave, we denoted the week with the highest total number of infections to be the pandemic week. Therefore, there were four pandemic weeks during our study period, the weekly number of reported confirmed cases is shown in Fig. S1. Detailed daily pandemic data from January 23, 2020 to May 31, 2021 is shown in Fig. S2. The four pandemic weeks covered in this study were: Mar. 26 to Apr. 1, 2020; Jul. 24 to 30, 2020; Dec. 4 to 10, 2020; Jan. 18 to 24, 2021. To analyze changes in local travel behaviors, we obtained data from 4 weeks in 2019 corresponding to the same periods as the pandemic weeks of 2020 and 2021, to act as control groups (Mar. 26 to Apr. 1, 2019; Jul. 24 to 30, 2019; Dec. 4 to 10, 2019; Jan. 18 to 24, 2019). The above smartcard swipe data was collected and analyzed, to determine: how local travel behavior had changed due to the pandemic and how non-pharmaceutical interventions (e.g. work from home, school suspension, staggered shift travel pattern, and travel reduction) influenced interpersonal contacts in subways.
Data processing
Not all smartcard data was valid to use, and the following three screening methods were used to screen the raw data.
-
(1)
The swiping record of smartcard for both enter and leave the station should exist simultaneously.
-
(2)
The entry and exit stations of a travel should be different.
-
(3)
The time of smartcard swiping should be within the subway’s operation time.
After the above data screening, nearly 4 billion card swiping data from were obtained, and less than 5% of them were invalid.
Hong Kong Metro had 10 lines (excluding Light Rail and High Speed Rail) and 98 heavy rail stations (MTR of Hong Kong, 2022) (Fig. S3). We utilized Dijkstra’s algorithm to generate the shortest path from any station A to any station B for MTR, Hong Kong. The shortest distance is defined as the minimum number of boarding stations, which includes the actual boarding stations and the equivalent stations for the inter-line transfer. The common inter-line transfer is considered as one station interval except for a few special inter-line transfers, that is, 3 stations for the transfer between Central and Hong Kong, 4 stations for the transfer between Kowloon and Austin, 5 stations for the transfer between East Tsim Sha Tsui and Tsim Sha Tsui and the transfer between Kowloon and Tsing Yi. We also obtained a weekly schedule for each train in each MTR line from the MTR website and Google map, which means that each train has its own number. And then we allocated each passenger to the corresponding train based on the following two principles. The first principle is to allocate each passenger an optimal route, including the boarding lines and stations, using the enter/leave stations of each passenger and the shortest path determined by Dijkstra’s algorithm. The second principle is to allocate each passenger to the corresponding train on the shortest path based on the enter/leave station time and the weekly schedule of each train. The passenger should arrive at the station platform before the arrival time of each train. Note that we also consider a 2-min walk from the station gate to the station platform, a 2-min walk for the common inter-line transfer and a 5-min walk for the special inter-line transfer. Finally, we allocated 1.5 billion passengers to the corresponding MTR train in Hong Kong successfully.
In this study, travel reduction-related interventions for COVID-19 prevention and control in subways including work from home (or AB work shift), class suspension, staggered shift travel pattern, and travel reduction were considered. Since the local travel behavior before the pandemic without any interventions represented the real condition, the efficiency of interventions in subways were analyzed based on normal local travel behaviors during the non-pandemic weeks. All interventions for COVID-19 prevention and control in subways considered in this study are introduced in Supplementary Information.
Result
Changes on local travel behavior due to the pandemic
Due to the pandemic, the total number of MTR passengers decreased by an average of 41.0% (37.4%, 80.3%, 71.6% and 33.5% for adults, children, students and senior citizens, respectively). During four non-pandemic weeks, 78.9% (n = 1.9 million), 3.5% (n = 0.08 million), 7.3% (n = 0.18 million), and 10.2% (n = 0.25 million) of MTR passengers used adult, child, student and senior citizen cards, respectively. Where during four pandemic weeks, 83.7% (n = 1.2 million), 1.2% (n = 0.02 million), 3.5% (n = 0.05 million), and 11.6% (n = 0.17 million) of MTR passengers used adult, child, student and senior citizen cards, respectively.
Although the number of passengers decreased significantly, the daily number of subway trains remained unchanged (Fig. S4). Figure 1A shows the probability distribution of the number of passengers in the subway. Due to the pandemic, daily number of passengers on the same train (DPST) of adults, children, students and senior citizens during weekdays (weekends) was decreased by 37.2% (52.2%), 41.3% (50.7%), 43.4% (53.7%), and 33.5% (49.8%), respectively. Children had the lowest DPST of 637, while adults had the highest of 792.
During the same period during a day, DPST changes little for all population groups (Fig. 1B). In the weekday before (during) the pandemic, the ratio of the hourly number of passengers taking the same train (HPST) of whole day, rush hours and non-rush hours was 1: 1.9(1.8): 0.8(0.8). In the weekend before (during) the pandemic, the ratio of the hourly number of passengers taking the same train (HPST) of whole day, rush hours and non-rush hours was 1: 1.3(1.8): 0.9.
In rush hours of the weekday before the pandemic, there were significant differences in HPST among four population groups (p < 0.05). The HPST for adults, children, students and senior citizens were 120.3, 68.3, 102.0 and 64.9, respectively. Adults and students have significantly higher HPST than children and senior citizens because they have to go to work and school.
The distribution on contact time of four population groups was different (Fig. S5). In pandemic (non-pandemic) weekday, the average daily duration on the same train of adults, children, students and senior citizens on weekends was 4.0 (4.0), 4.0 (3.8), 4.0 (3.8) and 4.1 (4.3) minutes, respectively, which were 8.4% (5.6%), 6.9% (8.0%), 9.9% (6.1%) and 4.6% (3.4%) higher than them on weekday, respectively.
A person may take the MTR with the same passenger many times per day. The probability distribution of daily number of passengers on the same train showed a monotonically logarithmic decrease (Fig. S6). More than 99% of possible daily repeated contacts in the same train (DRC) were only once. The number of DRC during the non-pandemic weekdays was 1.6 times higher than it during the pandemic weekdays. Due to the pandemic, DRC decreased significantly (p < 0.01), the average number of DRC of adults, children, students and senior citizens during weekdays (weekends) decreased by 66.8% (75.4%), 94.7% (83.8%), 88.6% (83.3%), and 59.5% (73.4%), respectively. Adults had the highest number of DRC with others (the detailed distribution is shown in Fig. S7).
The frequency of possible repeated contacts on the same train (FRC) in subways changes significantly with time and population group (Fig. 2). Before the pandemic, the rush hours for adults were 7:30–9:00 and 18:00–19:30. Due to fixed residential area and work places, many workers had a high FRC during the morning and evening hours, up to 60,000. During pandemic weekdays, there was still two significant rush hours because work from home was not implemented for all companies. Due to the pandemic, FRC of adults in the rush hours (non-peak hours) decreased by 58.1% (70.8%).
Children/students has an earlier and shorter rush hour than adults (7:00 to 8:00 and 15:30 to 16:30). Due to the pandemic, FRC of children/students was reduced by 97.8% (92.6%) during rush (non-rush) hours. Before the pandemic, comparing with weekdays, children and students reduced their FRC during weekends by 7.6% and 19.4%, respectively. However, due to class suspension, during the pandemic, FRC of children and students during weekends were increased by 182.3% and 18.5%, respectively. The pandemic influenced on travel behaviors, especially for children and students. Between 20:30 and 21:00 during non-pandemic weekdays, adults and children had a much higher peak of FRC than it during both morning and evening rush hours. However, due to the pandemic, the FRC for adults and children was decreased by 92.0% and 99.0% during this period, respectively. This showed that the pandemic had a significant impact on non-essential travel.
The senior citizen had the minimal difference (3.2%) of FRC between weekdays and weekends before the pandemic. However, due to the pandemic, the FRC of senior citizens in weekends was 32.1% lower than it in weekdays.
Due to the pandemic, the number of passengers on the same carriage (PSC) of adults, children, students and senior citizens during weekdays (weekends) decreased by 34.5% (47.8%), 80.0% (78.3%), 72.9% (70.3%), and 30.2% (42.9%), respectively (Fig. 3). During the rush hour of pandemic weekdays, the PSC of adults, children and students decreased by 32.6%, 88.1% and 81.4%, respectively. Comparing with the weekdays, the PSC of adults, students, and senior citizens in pandemic (non-pandemic) weekends was decreased by 9.6% (28.0%), 22.9% (15.4%), and 11.1% (27.3%), respectively. However, children had 55.3% (66.7%) more PSC during pandemic (non-pandemic) weekends than during weekdays. During the non-pandemic weekdays, there averagely were 94 adults, 5 children, and 10 students in a carriage. Compared with non-pandemic period, the reduction of PSC of senior citizens during the pandemic was the smallest.
Due to the pandemic, number of passengers in the same carriage (PSC) was reduced during weekdays for all population groups, especially for children (Fig. 4A). Adults, children, students, and senior citizens reduced PSC with children by 81.6%, 90.3%, 81.6% and 78.9%, respectively. Considering the proportion of each population group, passengers tended to have contacts with same-type passengers (Fig. 4B). During pandemic weekdays, contacts between children and senior citizens increased significantly, while during pandemic weekends, adults had much more contacts with senior citizens.
Compared with the rush hours before (during) the pandemic, the number of contacts between adults, children, students, senior citizens and adults in the same carriage decreased by 22.9% (25.8%), 21.1% (26.8%), 26.7% (32.4%) and 21.4% (24.5%), respectively. The number of contacts between adults, students, senior citizens and children in the same carriage increased in weekends compared in weekdays. Compared to the weekend, the number of adults, students and senior citizens contacted with children in the same carriage in weekdays before (during) the pandemic increased by 87.7% (105.4%), 48.1% (74.9%) and 81.0% (79.3%), respectively. However, the contacts between children in the same carriage decreased by 25.3% due to school suspension.
All population groups tended to contact with same-type passengers during both rush and non-rush hours. During the rush hours of the weekend, the contact between the four groups and senior citizens in the same carriage was significantly reduced, which indicate that senior citizens avoided unnecessary travel during rush hours during the pandemic. During the rush hours of the weekend before the pandemic, contacts between all population groups and students increased sharply comparing with it during the non-rush hours. The detailed distribution was shown in Figs. S8 and S9.
Travel reduction-related interventions
Based on the normal travel behaviors before the pandemic period, this section analyzed how interventions including work from home, school suspension, staggered shifts travel pattern and reduction on subway riding of different population group influence the interpersonal contacts in subways.
Work from home and school suspension
Work from home and school suspension can significantly reduce interpersonal contacts of adults, children and students (Fig. S10). Due to the work from home (school suspension), the number of passengers in the same carriage (PSC) of adults (students/children) during their rush hours decreased by 39.6% (38.6%/43.2%). Due to more passengers were adults, work from home in weekday can significantly reduce the PSC of all population groups in the rush hours.
From Fig. 5, when work from home, school suspension, and both work and school suspension were implemented, the FRC of adults during the rush hours (7:30–9:00 and 18:00–19:30) were reduced by 76.3%, 2.9% and 77.8%, respectively, and the FRC of students (children) during the rush hours (7:00–8:00 and 15:30–16:30) was reduced by 8.7% (4.6%), 76.0% (81.3%) and 79.5% (82.6%), respectively.
If both work and school suspension were implemented, the FRC of adults, children, students and senior citizens would be decreased by 37.8%, 50.2%, 45.3% and 11.8%, respectively. If only work from home was implemented, the average FRC of adults, children, students and senior citizens would be decreased by 35.9%, 8.2%, 13.4% and 9.8%, respectively.
If both work and school suspension were implemented, the daily number of passengers in the same train (DPST) of adults, children, students and senior citizens decreased by 21.4%, 23.7%, 23.0% and 9.7%, respectively, and children had the lowest DPST of 647. The detailed distribution is shown in Fig. S11.
Staggered shifts travel pattern
When the staggered shift travel patterns of workers, students, and children were implemented, the FRC of workers, students, and children during their rush hours were reduced by 73.3%, 79.5%, and 77.9%, respectively (Fig. 6A). The number of passengers in the same carriage (PSC) of children and students during the rush hours (single day) of school was reduced by 50.7% (25.6%) and 50.9% (20.2%), respectively, and the PSC of adults during the rush hours (single day) was reduced by 51.7%. During the rush hours, the number of adults, children and students in the same carriage was 42.5, 2.3 and 5.0 respectively (Fig. 6B).
Travel reduction of different population groups
If the travel of adults was reduced by 90% during the weekday (weekend) before the pandemic, the DPST would be decrease by 84.8% (83.6%) for adults, 65.3% (70.2%) for children, 68.6% (70.1%) for students, and 69.5% (70.1%) for senior citizens (Fig. S12). If the travel of children, students and senior citizens was reduced by 90%, the DPST of children, students and senior citizens would be reduced by 42.5%, 51.3% and 48.0%, respectively.
Discussion
The COVID-19 has threatened almost all areas with people in the world, especially in large cities with high population density (Chen et al. 2020). Taking Hong Kong as an example, MTR is the main public transport. Local travel behaviors on subway was greatly affected by the COVID-19 pandemic and changes on local travel behaviors also had a significant impact on the spread of COVID-19 (Megahed and Ghoneim 2020; Iacus et al. 2020). Between January 1, 2020 and January 31, 2021, Hong Kong had experienced four pandemic waves. Take MTR data during 2019 as control groups, approaching 4 billion valid card swiping data were collected to analyze the changes on local travel behaviors due to the pandemic. The impact of travel reduction-related interventions (e.g. work from home, school suspension, staggered shift travel pattern, travel reduction) on interpersonal contacts were simulated and analyzed.
Due to the COVID-19 pandemic, people reduced their travel significantly (Jenelius and Cebecauer 2020). International travels were banned in many countries due to the pandemic (Sun et al. 2021). Residents deliberately avoid to take public transports (e.g. subway) and turned to use private cars (Chang et al. 2020). COVID-19 has affected people’s travel behavior in the MTR in Hong Kong severely. We found that, due to the pandemic, Hong Kong adults, children, students and senior citizens reduced their subway riding by 37.2%, 41.3%, 43.4%, and 33.5%, respectively. Subway is one of the most important public transports (Wu and Hong, 2017), especially in Hong Kong. It is important to carry out an effective strategy on COVID-19 prevention and control in subways (Feng et al. 2020).
Most of passengers taking public transports were adults, especially during the rush hours. However, due to the short social distance, the infection risk via close contacts would be very high. During the serious pandemic, half of Europeans worked at home (Galanti et al. 2021), working from home has become a policy priority in many countries (Vyas and Butakhieo 2021). Therefore, the local government should take measures of work from home or off-peak commuting to reduce the infection risk on public transports. Working from home for jobs that require attendance at work (e.g. construction workers, healthcare, agriculture, and hospitality). The policy of working from home can be implemented for some occupations, but this will increase the danger of exposure if employees commute a significant distance (Lo et al. 2011). Work from home was usually taken by adults (Dubey and Tripathi 2020) to avoid taking public transportation, and flexible working hours could also ensure the effective business operations (Purwanto et al. 2020). A fair allocation of adults who work from home can not only lower turnover rates, boost worker productivity (Baker et al. 2007), and ensure that the city’s daily operations run smoothly.
Due to the weak awareness of personal protection and relative short social distance, children and students would tend to be infected (Iachini et al. 2021) if all types of people had the same susceptibility. The Education Bureau will ensure the quality of distant learning (Lau, EYH, Lee K 2021), and children and students should implement mandatory school closure measures, which can considerably protect their safety. However, study showed that children and students have a higher resistance to COVID-19 than adults and senior citizens (Jing et al. 2020), school suspension is not that effective as we expected. In addition, the number of subway travels of children was the smallest among all population groups, which means changes on children’s travel behavior influence the pandemic least. However, school suspension has also been adopted in many areas to relieve the spread of the pandemic (UNESCO 2020). For other respiratory infectious diseases such as influenza, which is highly susceptible for children and students (Smith et al. 2017), school suspension would be very effective (Kao et al. 2012). School suspension reduced the average frequency of possible repeated contacts on the same train (FRC) of students and children during rush hours by 76.0% and 81.3%, respectively.
Among four population groups, senior citizens showed the least changes on local travel behavior due to the pandemic, and still maintained a relatively high frequency of subway riding. However, many older people suffered from chronic diseases (e.g. diabetes mellitus), which lead to lower resistance and more complications (Dhama et al. 2020; Csiszar et al. 2020). The susceptibility and mortality rate of COVID-19 are 1.6 and 5.1 times of that of adults, respectively (Jing et al. 2020; Lv et al. 2021). We found that senior citizens had the longest daily exposure time in subways, which led to the greatest risk of exposure in carriages (Lo et al. 2021). Unfortunately, the vaccination rate decreases with age (Thanapluetiwong et al. 2021). In Hong Kong, less than 20% of older care home residents were fully vaccinated against COVID-19 when Omicron came at the end of February 2022 (Ma and Parry 2022). The vaccination rate of people over 60 is only 62% of that of adults (Smith et al. 2022). Hong Kong is a rapidly aging city (Jayantha et al. 2018), therefore, government should prioritize vaccination for senior citizens to reduce their susceptibility and mortality (Monahan et al. 2020). Moreover, it is important to strengthen the awareness of pandemic prevention and control for older people because most of their travel are unnecessary.
In the rush hours of commuting workers (7:30–9:00 and 18:00–19:30) and students (7:00–8:00 and 15:30–16:30), there were high FRC because large flow of passengers. Therefore, the government should implement measures that stagger peak travel, such as mandating adults commute at different times during the day; Children and students go to and from school in various grades (kindergarten, elementary school, junior high school and high school). We also found that the highest peak on FRC for both adults and children was between 20:30 and 21:00 during weekdays other than the morning and evening rush hours as we expected. The main reason may be that they usually go to shopping centers, entertainments, restaurants, and other places for non-essential activities during this period. Local governments should take relevant interventions such as closing shopping malls earlier and restricting population flow in public indoor environments, to reduce the close contacts during this period. Reducing repeated contacts among people in the social network can greatly reduce the transmission of the virus (Block et al. 2020). This study found that more than 99% of passengers repeated contacts in the same train (DRC) were only once. But in the weekend before the pandemic, the DCR of adults reached a maximum of 15. No matter what DRC is, in all population groups, adults have the highest frequency of contacts. Dispersing the commuting time of workers would be effective for repeated contact reduction (Tirachini and Cats 2020). In addition, increasing the departure frequency of subway trains can also reduce the density of passengers, thus reducing the infection risk.
The infection risk of susceptible population increased significantly with the passenger flow, and the infection risk varies with time (Li et al. 2022). There was a negative correlation between COVID-19 spread and social distance, while short social distance would lead to (Seong et al. 2021). Moreover, high passenger volume was associated with the higher infection rate of destinations (e.g. bars, restaurants, and sport events) (Zhang et al. 2021b) and the least deprived area (Ha et al. 2022). It not only promotes the spread of virus to other parts of the city but also may lead to an outbreak in carriages (Hamidi and Hamidi, 2021). Governments should pay more attention to crowded public destinations around the stations of public transports.
During the pandemic weeks, the number of subway travels in weekdays was 0.3 times higher than that of weekends. However, it is difficult to keep long time on the target of “dynamic zero”(dynamic zero means that preventive measures should be strengthened when cases appear. Under normal circumstances, moderate epidemic prevention measures with Rt =1 or slightly higher than 1 can be maintained) during weekdays on public transports because of large number of necessary travels (e.g. go to work/school). Children took subways more during weekends with most of travels to entertainment areas. The local government should strengthen the Omicron prevention and control on hot subway lines or limit unnecessary travels. In order to achieve the requirement of “dynamic zero “, Rt needs to be controlled smaller than 1 (Guo et al. 2022). If the pandemic is difficult to be controlled in weekdays, the government can slightly reduce the prevention and control requirements during weekdays (Rt slightly higher than 1) and strictly control the pandemic on weekends (Rt < 1) to finally make the average Rt of 1 during the whole week. This optimization method can effectively reduce the cost of the pandemic prevention and control.
Several limitations were existed in the study. Firstly, four population groups were divided according to the types of smartcards, however, as the default card without any discount, adult’s cards may be used by other population groups. Secondly, the determination of each passenger’s route is based on the shortest path method, which assumes the same time spent between stations and neglects differences in routes chosen due to fares, personal habits, etc. Finally, the selected four pandemic weeks may be biased due to other factors, such as the intervention made by the local government. In the future, real close contact behaviors of different types of passengers should be detected to analyze the infection risk via clos contact route in detail. Moreover, the study on virus transmission could be extended from subway to other public transports such as bus and taxi.
Conclusion
Due to the pandemic, the total number of MTR passengers decreased by an average of 41.0%, and the number of passengers on the same carriage of adults, children, students and senior citizens during weekdays (weekends) decreased by 34.5% (47.8%), 80.0% (78.3%), 72.9% (70.3%), and 30.2% (42.9%), respectively. During the rush hour of pandemic weekdays, the PSC of adults, children and students decreased by 32.6%, 88.1% and 81.4%, respectively. Moreover, we found that work from home and staggered shift pattern of workers can reduce the infection risk effectively. However, school suspension is not that effective as we expected due to small number of children/students and relatively high resistance to COVID-19.
Availability of data and materials
Data cannot be made available for reasons disclosed in the data availability statement.
References
Abouk R, Heydari B (2021) The immediate effect of COVID-19 policies on social-distancing behavior in the United States. Public Health Rep 136:245–252
Anderson M, Karami A, Bozorgi P (2020) Social media and COVID-19: Can social distancing be quantified without measuring human movements? Proc Assoc Inf Sci Technol 57:e378
Aquino EM, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JAD, Rocha ADS, Lima RTDRS (2020) Social distancing measures to control the COVID-19 pandemic: potential impacts and challenges in Brazil. Ciencia Saude Coletiva 25:2423–2446
Baidu (2022) Hong Kong Metro Train. Accessed 4 May 2022. https://baike.baidu.com/item/%E9%A6%99%E6%B8%AF%E5%9C%B0%E9%93%81%E5%88%97%E8%BD%A6/19279483
Baker E, Avery GC, Crawford JD (2007) Satisfaction and perceived productivity when professionals work from home. Res Pract Hum Resource Manag 15:37–62
Baker MR, Hawthorne EL, Rogge JR (2021) COVID 19: Open source model for rapid reduction of R to below 1 in high R0 scenarios. arXiv preprint arXiv 2112:13044. https://doi.org/10.48550/arXiv.2112.13044
Block P, Hoffman M, Raabe IJ, Dowd JB, Rahal C, Kashyap R, Mills MC (2020) Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nat Hum Behav 4(6):588–596
Chang HH, Meyerhoefer C, Yang FA (2020) COVID-19 Prevention and Air Pollution in the Absence of a Lockdown (No. w27604). J Environ Manag 112:Q53
Chen Y, Wang Y, Wang H, Hu Z, Hua L (2020) Controlling urban traffic-one of the useful methods to ensure safety in Wuhan based on COVID-19 outbreak. Saf Sci 131:104938
Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, Vespignani A (2020) The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Sci 368:395–400
Cooley P, Brown S, Cajka J, Chasteen B, Ganapathi L, Grefenstette J, Hollingsworth CR, Lee BY, Levine B, Wheaton WD, Wagener DK (2011) The role of subway travel in an influenza epidemic: a New York City simulation. J Urban Health 88:982–995
Csiszar A, Jakab F, Valencak TG, Lanszki Z, Tóth GE, Kemenesi G, Tarantini S, Fazekas-Pongor V, Ungvari Z (2020) Companion animals likely do not spread COVID-19 but may get infected themselves. GeroScience 42:1229–1236
Davido B, Dumas L, Rottman M (2022) Modelling the Omicron wave in France in early 2022: Balancing herd immunity with protecting the most vulnerable. J Travel Med 29:taac005
Del Rio C, Omer SB, Malani PN (2022) Winter of Omicron—the evolving COVID-19 pandemic. JAMA 327:319–320
Dhama K, Patel SK, Kumar R, Rana J, Yatoo MI, Kumar A, Tiwari R, Dhama J, Natesan S, Singh R, Harapan H (2020) Geriatric population during the COVID-19 pandemic: problems, considerations, exigencies, and beyond. Front Public Health 8:574198
Dubey AD, Tripathi S (2020) Analysing the sentiments towards work-from-home experience during covid-19 pandemic. J Innova Manag 8(1):13–19
Feng Y, Marchal T, Sperry T, Yi H (2020) Influence of wind and relative humidity on the social distancing effectiveness to prevent COVID-19 airborne transmission: A numerical study. J Aerosol Sci 147:105585
Galanti T, Guidetti G, Mazzei E, Zappalà S, Toscano F (2021) Work from home during the COVID-19 outbreak: The impact on employees’ remote work productivity, engagement, and stress. J Occup Environ Med 63:e426
Guan L, Prieur C, Zhang L, Prieur C, Georges D, Bellemain P (2020) Transport effect of COVID-19 pandemic in France. Annu Rev Control 50:394–408
Guo Y, Zhang N, Hu T, Wang Z, Zhang Y (2022) Optimization of energy efficiency and COVID-19 pandemic control in different indoor environments. Energy Build 261:111954
Ha J, Jo S, Nam HK, Cho SI (2022) The Unequal Effects of Social Distancing Policy on Subway Ridership during the COVID-19 Pandemic in Seoul, South Korea. J Urban Health 99:77–81
Halamicek R, Schubert DW, Nilsson F (2022) How large fraction of a population must be vaccinated before a disease is controlled? Res Sq. https://doi.org/10.21203/rs.3.rs-1218033/v2
Hamidi S, Hamidi I (2021) Subway ridership, crowding, or population density: determinants of COVID-19 infection rates in New York City. Am J Prev Med 60:614–620
HKCHP (2021) COVID-19 case report. Accessed 2 June 2020. https://chp-dashboard.geodata.gov.hk/covid-19/zh.html
Iachini T, Frassinetti F, Ruotolo F, Sbordone FL, Ferrara A, Arioli M, Pazzaglia F, Bosco A, Candini M, Lopez A, Caffo AO, Cattaneo Z, Fornara F, Ruggiero G (2021) Social distance during the COVID-19 pandemic reflects perceived rather than actual risk. Int J Env Res Pub He 18:5504
Iacus SM, Natale F, Santamaria C, Spyratos S, Vespe M (2020) Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact. Saf Sci 129:104791
Jayantha WM, Qian QK, Yi CO (2018) Applicability of ‘Aging in Place’in redeveloped public rental housing estates in Hong Kong. Cities 83:140–151
Jenelius E, Cebecauer M (2020) Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts. Transp Res Interdiscip Perspec 8:100242
Jing QL, Liu MJ, Zhang ZB, Fang LQ, Yuan J, Zhang AR, Dean NE, Luo L, Ma MM, Longini I, Kenah E, Ying L, Ma Y, Yalali N, Yang ZC, Yang Y (2020) Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study. Lancet Infect Dis 20:1141–1150
Kao CL, Chan TC, Tsai CH, Chu KY, Chuang SF, Lee CC, Li ZRT, Wu KW, Chang LY, Shen YH, Huang LM, Lee PI, Yang CL, Compans R, Rouse BT, King CC (2012) Emerged HA and NA mutants of the pandemic influenza H1N1 viruses with increasing epidemiological significance in Taipei and Kaohsiung, Taiwan, 2009–10. PLoS One 7:e31162
Karia R, Gupta I, Khandait H, Yadav A, Yadav A (2020) COVID-19 and its modes of transmission. SN Compr Clin Med 2:1798–1801
Lau EYH, Lee K (2021) Parents’ views on young children’s distance learning and screen time during COVID-19 class suspension in Hong Kong. Early Educ Dev 32(6):863–80
Li P, Chen X, Ma C, Zhu C, Lu W (2022) Risk assessment of COVID-19 infection for subway commuters integrating dynamic changes in passenger numbers. Environ Sci Pollut Res Int:1–10
Lo AS, Cheung C, Law R (2011) Hong Kong residents’ adoption of risk reduction strategies in leisure travel. J Travel Tour Mark 28:240–260
Lotfi M, Hamblin MR, Rezaei N (2020) COVID-19: Transmission, prevention, and potential therapeutic opportunities. Clin Chim Acta 508:254–266
Lv G, Yuan J, Xiong X, Li M (2021) Mortality rate and characteristics of deaths following COVID-19 vaccination. Front Med 8:670370
Ma A, Parry J (2022) When Hong Kong’s “dynamic zero” covid-19 strategy met omicron, low vaccination rates sent deaths soaring. BMJ-BRIT Med J 377:o980
Megahed NA, Ghoneim EM (2020) Antivirus-built environment: Lessons learned from Covid-19 pandemic. Sustain Cities Soc 61:102350
Monahan C, Macdonald J, Lytle A, Apriceno M, Levy SR (2020) COVID-19 and ageism: How positive and negative responses impact older adults and society. Am Psychol 75:887
MTR of Hong Kong (2022) Itinerary Guide (Route Construction). Accessed 9 March 2022. https://www.mtr.com.hk/ch/customer/jp/index.php
News of Hong Kong Government (NHKG) (2022a) New Coronavirus Infections - Hong Kong Update. Accessed 20 Dec 2022. https://chpdashboard.geodata.gov.hk/covid-19/zh.html
News of Hong Kong Government (NHKG) (2022b) The number of novel coronavirus positive cases increased by over 25,000. Accessed 7 March 2022. https://www.news.gov.hk/chi/2022/03/20220307/20220307_180507_220.html?type=category&name=covid19&tl=t
Nissen K, Krambrich J, Akaberi D, Hoffman T, Ling J, Lundkvist Å, Svensson L, Salaneck E (2020) Long-distance airborne dispersal of SARS-CoV-2 in COVID-19 wards. Sci Rep 10:19589
Ozdemir S, Ng S, Chaudhry I, Finkelstein EA (2022) Adoption of preventive behaviour strategies and public perceptions about COVID-19 in Singapore. Int J Health Policy Manag 11:579–591
Purwanto A, Asbari M, Fahlevi M, Mufid A, Agistiawati E, Cahyono Y, Suryani P (2020) Impact of work from home (WFH) on Indonesian teachers performance during the Covid-19 pandemic: An exploratory study. Int J Eng Sci 29(5):6235–6244
Qian M, Jiang J (2022) COVID-19 and social distancing. J Public Health 30:259–261
Rahman HS, Aziz MS, Hussein RH, Othman HH, Omer SHS, Khalid ES, Abdulrahman NA, Amin K, Abdullah R (2020) The transmission modes and sources of COVID-19: A systematic review. Int J Surg 26:125–136
Ruiz-Frutos C, Ortega-Moreno M, Allande-Cussó R, Domínguez-Salas S, Dias A, Gómez-Salgado J (2021) Health-related factors of psychological distress during the COVID-19 pandemic among non-health workers in Spain. Saf Sci 133:104996
Seong H, Hong JW, Hyun HJ, Yoon JG, Noh JY, Cheong HJ, Kim WJ, Jung JH, Song JY (2021) Correlation between the level of social distancing and activity of influenza epidemic or COVID-19 pandemic: a subway use-based assessment. J Clin Med 10:3369
Shen J, Duan H, Zhang B, Wang J, Ji JS, Wang J, Pan L, Wang X, Zhao K, Ying B, Zhang J, Liang C, Sun H, Lv Y, Li Y, Li T, Li L, Liu H, Zhang L, Wang L, Shi X (2020) Prevention and control of COVID-19 in public transportation: Experience from China. Environ Pollut 266:115291
Smith DJ, Hakim AJ, Leung GM, Xu W, Schluter WW, Novak RT, Marston B, Hersh BS (2022) COVID-19 mortality and vaccine coverage—Hong Kong special administrative region, China, January 6, 2022–March 21, 2022. Morb Mortal Wkly Rep 71:545
Smith LE, Webster RK, Weinman J, Amlôt R, Yiend J, Rubin GJ (2017) Psychological factors associated with uptake of the childhood influenza vaccine and perception of post-vaccination side-effects: a cross-sectional survey in England. Vaccine 35:1936–1945
Sun C, Zhai JZ (2020) The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustain Cities Soc 62:102390
Sun X, Wandelt S, Zhang A (2021) Delayed reaction towards emerging COVID-19 variants of concern: Does history repeat itself? Transp Res Pt A-Policy Pract 152:203–215
Thanapluetiwong S, Chansirikarnjana S, Sriwannopas O, Assavapokee T, Ittasakul P (2021) Factors associated with COVID-19 vaccine hesitancy in Thai seniors. Patient Prefer Adherence 15:2389
Tirachini A, Cats O (2020) COVID-19 and public transportation: Current assessment, prospects, and research needs. J Publ Transp 22:1
UNESCO (2020) 290 Million students out of school due to COVID-19: UNESCO releases first global numbers and mobilizes response. Available from: https://en.unesco.org/news/290-million-students-out-school-due-covid-19-unesco-releases-first-global-numbers-and-mobilizes
Vakili S, Savardashtaki A, Jamalnia S, Tabrizi R, Nematollahi MH, Jafarinia M, Akbari H (2020) Laboratory findings of COVID-19 infection are conflicting in different age groups and pregnant women: a literature review. Arch Med Res 51:603–607
Vyas L, Butakhieo N (2021) The impact of working from home during COVID-19 on work and life domains: an exploratory study on Hong Kong. Policy Des Pract 4:59–76
World Health Organization (WHO) (2022) WHO Coronavirus (COVID-19) Dashboard. Accessed 20 Oct 2022. https://covid19.who.int/
Wu W, Hong J (2017) Does public transit improvement affect commuting behavior in Beijing, China? A spatial multilevel approach. Transport Res Part D-Transport Environ 52:471–479
Zhang M, Xiao J, Deng A, Zhang Y, Zhuang Y, Hu T, Li J, Tu H, Li B, Zhou Y, Yuan J, Luo L, Liang Z, Huang Y, Ye G, Cai M, Li G, Yang B, Xu B, Huang X, Cui Y, Ren D, Zhang Y, Kang M, Li Y (2021a) Transmission dynamics of an outbreak of the COVID-19 Delta variant B. 1.617. 2—Guangdong Province, China, May–June 2021. China CDC Wkly 3:584
Zhang N, Jia W, Wang P, Dung CH, Zhao P, Leung K, Su B, Cheng R, Li Y (2021b) Changes in local travel behaviour before and during the COVID-19 pandemic in Hong Kong. Cities 112:103139.
Zhang W, Wang Y, Yang L, Wang C (2020) Suspending classes without stopping learning: China’s education emergency management policy in the COVID-19 outbreak. J Risk Financ Manag 13:55
Zhao H, Lu X, Deng Y, Tang Y, Lu J (2020) COVID-19: asymptomatic carrier transmission is an underestimated problem. Epidemiol Infect 148:e116
Zheng R, Xu Y, Wang W, Ning G, Bi Y (2020) Spatial transmission of COVID-19 via public and private transportation in China. Travel Med Infect Dis 34:101626
Acknowledgements
We would like to thank the Mass Transit Railway Corporation (MTRC) for providing their passenger data.
Funding
This work was supported by the National Natural Science Foundation of China (grant numbers: 52108067).
Author information
Authors and Affiliations
Contributions
N.Z., Y.L., and R.C. conceived the idea. R.C. collected the data. S.S., W.J., S.Z., and B.S. analyzed the data. S.S. and W.J. prepared the figs. S.S. and N.Z. wrote the paper. R.C. and Y.L. made constructive amendments. All authors review the paper. The author(s) read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
Conflict of Interest Statement The authors declare no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Additional file 1: Figure S1.
Daily number of reported cases in Hong Kong during four pandemic weeks from January 1, 2020 to January 31, 2021. Figure S2. The number of new daily cases in Hong Kong from January 23, 2020 to May 31, 2021. Figure S3. MTR route map of Hong Kong. Figure S4. Number of trains in operation by time. Figure S5. The distribution on contact time in the same train of four population groups. (The small block diagram shows the probability distribution when the contact time of the four groups exceeds 100 minutes). Figure S6. Probability distribution of daily number of possible repeated passengers on the same subway of four population groups. Figure S7. Distribution of daily number of repeated contacts passengers on the same subway of four populations. Figure S8. Contact matrix in rush hours. (A) Absolute value; (B) relative value. Figure S9. Contact matrix in non-rush hours. (A) Absolute value; (B) relative value. Figure S10. The number of passengers in the same carriage (PSC) of adults, children and students under work from home or school suspension. Figure S11. Daily number of passengers in the same train. Figure S12. Change of daily number of passengers on the same train (DPST) of four population groups by travel reduction.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Shang, S., Jia, W., Zhang, S. et al. Changes on local travel behaviors under travel reduction-related interventions during COVID-19 pandemic: a case study in Hong Kong. City Built Enviro 1, 5 (2023). https://doi.org/10.1007/s44213-023-00006-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s44213-023-00006-z