The Public Bicycle as a Feeder Mode for Metro Commuters in the Megacity Beijing: Travel Behavior, Route Environment, and Socioeconomic Factors

The last mile between metro stations and the homes or workplaces of commuters has become a central topic in relation to metro ridership in megacities. Regarding the different origins and destinations of different commuters and various travel preferences, it is difficult but necessary for governments and city planners to develop applicable solutions for the last-mile problem. The last-mile problem requires more attention and discussion for three main reasons. First, transport services for the last-mile problem would directly affect the accessibility of the metro system (Zuo et al. 2020). At the same time, how to motivate people to use public transport such as metro to control and reduce air pollution is always important but challenging (Chen and Wang 2018). Second, in many cities, especially growing megacities of developing countries, road congestion has been worsening, which makes commuting inefficient (Zhao and Hu 2019). Thus, minimizing time and monetary cost on the last mile could alleviate congestion and improve the efficiency of transport systems. Third, many commuters in megacities may choose to live far away from metro stations in order to reduce housing costs, which leads to a long-distance commute (Zhao et al. 2020). Under such circumstances, providing a better last-mile service for long-distance commuters is conducive to social welfare. However, at present, experts and scholars have mainly studied the last-mile issue from the logistics distribution perspective (Halldórsson and Wehner 2020; Janjevic and Winkenbach 2020; Kapser and Abdelrahman 2020), only a few studies have dealt with this issue from the perspective of individual travel behavior (Li et al. 2019; Zuo et al. 2020).

The public bicycle is widely believed to be one of new ways to solve the last-mile problem (Liu et al. 2012; Ma et al. 2015; Ni and Chen 2020). It can improve the accessibility to public transportation systems by directly connecting the origin/destination and metro stations (Liu et al. 2012; Zhang et al. 2019). Compared with other feeder modes, such as walking, electromobiles, and private cars, public bicycles have significant advantages. For example, a study in Hamilton County, Ohio, showed that the transit access distance covered by bicycling may be tripled compared with walking, such that bicycling enhances transit equity (Zuo et al. 2020). A study in Beijing pointed out that people prefer to use dockless bike sharing instead of taxis as a transfer mode, especially in less economically developed areas (Ni and Chen 2020). Furthermore, promoting public cycling provides direct health benefits at the population level (Mueller et al. 2015), along with substantial environmental and economic advantages (Woodcock et al. 2009; Handy and Xing 2011; Handy et al. 2014). Thus, promoting the integration of public bicycles and the metro system has the potential to be a great solution for the last-mile problem (Ji et al. 2018; Liu et al. 2020).

Among the many studies on bicycle use, previous research has focused mainly on which kind of people tend to use this mode and which factors influence their choice. People who are younger and more educated are thought to use cycling as the primary mode to travel to work (Daniel et al. 2011). Various factors have been examined to study the determinants of public bicycle ridership (Fishman et al. 2015; Verma et al. 2016; Scott and Ciuro 2019), which can be divided into four main types. These include the built environment (El-Assi et al. 2017), individual characteristics (Titze et al. 2008), travel distance (Zhao and Li 2017), and some natural conditions, such as weather and temperature (An et al. 2019), which show mixed effects on travel behavior in relation to public bicycle use. However, the determinants of commuters' use of public bicycle as a feeder mode are scarcely discussed. Moreover, since the knowledge on public bicycle use is not transferable among transnational cities (Lin et al. 2018), there is still a lack of research on Chinese megacities.

This paper aims to contribute to this topic by looking at Beijing as a case study. As a travel mode with high efficiency and good patency, the metro has become an important travel choice for most commuting and non-work trips in megacities of China. In 2018, the average daily passenger volume of the 16 metro lines in Beijing was 8.537 million and the maximum daily passenger volume was 10.907 million on August 17, 2018. Up to 16.2% of travelers took the metro as their travel mode, and this proportion had increased by 0.8 percentage points from 2017 (BMCT 2019). Policymakers and urban planners are seeking strategies to develop an integrated public transit system that allows riders to travel between their final destinations and metro stations easily. Promoting public bicycles may provide a possible solution to last-mile problem. Despite the benefits of cycling, it is difficult to encourage riders to use bicycles. Evidence has shown that the percentage of taking bicycle as a travel mode in mega cities in China is quite low. In Beijing, only 12.40% of work-based trips (excluding walking) were by bicycle (BTI 2016), and in 2018, only 11.50% of trips were by bicycle (BTI 2019).

On June 16, 2012, the launching ceremony of the pilot of Beijing’s public bicycle service system was held in Dongcheng District, one of the two pilot areas in Beijing. The government has regarded the promotion and development of urban public bicycle services as an important part of optimizing the public transportation structure and improving the public transportation service quality (BMCT 2012). According to an interview from the Parking Management Office of the Beijing Municipal Commission of Transport in 2018, there were at least 110,000 docks in Beijing. The average turnover was 2.7, and there were about 200,000 orders per day (BMCT 2018). In recent years, another type of bicycle services, called share bicycles, have grown rapidly in Beijing. However, shared bicycles have long suffered from illegal parking, potential safety risks, damage and disposal, occupation of roads, and deposits difficult to be sent back (JinwanNews 2020). Compared with shared bicycles, public bicycles have advantages in parking management, and subsidies from the government make public bicycles more affordable than shared bicycles.

This paper intends to explore how to solve the last-mile problem by using public bicycle as a feeder mode. By applying cluster analysis and binary logit model, this paper aims to find out who are the users of public bicycle and how to encourage people to take public bicycles as a feeder mode. The results will enhance our understanding of public bicycle as a feeder mode to solve the last-mile problem and provide new evidence for policymaking on promoting integrated public transit systems in megacities.

The remainder of this paper is organized as follows. The next section, “Literature Review: Factors Involved in Cycling”, reviews earlier studies on this subject and the contribution that the current study makes. The section “Research Method” provides a description of how the survey data were collected, followed by the “Results” section, which outlines the modeling methodology applied for the analysis. The next section presents the sample analysis and modeling results, and the final section, “Discussion”, summarizes the findings and concludes with recommendations for policy.

The built environment is generally considered to have effects on cycling behavior. For example, a high-density design is significantly related to a high level of cycling (Guo et al. 2007; Parkin et al. 2008). Diverse land use types also increase the use of public bicycles (Litman 2008; Zhang et al. 2017). These may entail a mixture of significant environmental variables, including the presence of stores, offices, restaurants, and hospitals (Moudon et al. 2005). Most cases from US cities (Dill and Carr 2003), Toronto (El-Assi et al. 2017), and Beijing (Zhao 2014) have shown that a higher level of bicycle infrastructure (e.g., bicycle lanes) resulted in greater use of bicycles. In addition, the impact of one single built environment factor is often associated with that of another, such as land use and bicycle infrastructure (Mitra and Nash 2018; Cervero et al. 2019; Liu and Lin 2019). However, some studies have drawn different conclusions in which transportation infrastructure and built environment have influence on ridership (Cervero et al. 2009; Scott and Ciuro 2019). Since the impacts of the built environment on ridership remain unclear, together with the fact that most studies have discussed general cycling instead of public bicycle systems, the role of the built environment in public bicycle use requires more exploration.

Previous studies have suggested that many individuals frame their commutes based in part on the perceived safety of the environment (Riggs 2019). A study using an electronic survey showed that perceived crash risk, adverse weather conditions, and lack of safety are the most relevant discouraging factors for cycling (Useche et al. 2019). Another study based on a survey in Amsterdam found statistically significant positive associations between safety and cycling, especially for women (Timmermans et al. 2019). However, researches have also pointed out that the measurement of safety mainly comes from individuals’ subjective perceptions related to the cycling infrastructure (Ng et al. 2017; Riggs 2019). This means that there is no universal measurement for “safety” (Riggs 2019). Therefore, our paper applies measurable and objective indicators, such as traffic flow and the ratio of bicycle lanes, to reflect the safety level of the route environment. In this way, the interference caused by the cognitive differences among survey respondents can be largely alleviated.

Individual characteristics also have significant impacts on cycling. In areas with adequate bicycle infrastructure, individual determinants even outperform the role of the built environment (de Bas et al. 2008). Gender and age are the two most widely discussed factors. Generally, men (Stinson and Bhat 2004; Ogilvie and Goodman 2012; Julii et al. 2019) and young people (Fishman et al. 2015; Dill and McNeil 2013) are thought to be more likely to cycle. For example, a study in London found that females made 1.63 fewer bicycle trips per month than males (Ogilvie and Goodman 2012). As age increases, the preference for participating in active travel such as cycling will decline (Julii et al. 2019). Income level also has an influence on cycling, although it has different influences among cities and research conclusions may even be contrasting (Ogilvie and Goodman 2012; Mitra 2013; Goodman and Cheshire 2014; Handy et al. 2014; Julii et al. 2019; Wang and Akar 2019). For example, in the Netherlands, people of low income were found more likely to be bicycle users (Shelat et al. 2018), while in the United States (Zahran et al. 2008) and the United Kingdom (Parkin et al. 2008), people of higher income were associated with a higher probability of cycling. A study in Australia found that those with relatively high incomes had increased odds of public bicycle membership (Fishman et al. 2015). Individual factors such as the attitude toward cycling and personal experience also have some impact, although they are difficult to measure and less discussed in the literature. Those who prefer affordable travel are more likely to cycle (Zhao and Li 2017), while commuters who encountered personal bicycle stolen tend to use public bicycles more often (Ji et al. 2016). Furthermore, gender also indirectly affect cycling behavior through influencing people’s sensitivity toward the built environment. For example, a study in Toronto found that the effects of built environment-related factors were different between male and female riders (Mitra and Nash 2018). Another study in New York also found that installing more bicycle racks affects women more than men (Wang and Akar 2019).

Travel distance is another important factor influencing choosing bicycles or other transport modes (Zhao and Li 2017; Kang and Fricker 2018). Evidence shows that bicycles are more likely to be used for short and home-based trips. For example, a survey in Beijing found that 80% of respondents made cycling transfers within 5 km from home (Zhao and Li 2017), while a study in Spain found that the threshold distance of university students who cycled was 5.1 km (Chillón et al. 2016). Travel distance around 1.5–2 km has been found to have the highest potential for people to use bicycles to replace other public transport modes (Sun and Zacharias 2017). Within the threshold distance, people’s attitudes toward cycling may become more active and they would cycle more when the commute journey intensifies (Heinen et al. 2011). Furthermore, there are some differences among bicycle types. A comparison between public and private bicycles found that the main journey length travelled by private bicycles was 700–800 m longer than public bicycles (​Castillo-Manzano et al. 2016).

Cycling as a feeder mode has been attracting an increasing amount of research interests (Bachand-Marleau et al. 2011; Ji et al. 2016; Zhao and Li 2017; Ma et al. 2018). Along with walking, cycling is found to function as a significant access and egress mode for transit travelers (Shelat et al. 2018). Thus, an integration of cycling and transit such as the metro has become a hot topic of research (Krygsman et al. 2004; Bachand-Marleau et al. 2011; Zhao and Li 2017; Shelat et al. 2018; Zhang et al. 2019). Some studies in China have suggested that bike sharing as a feeder mode to a metro station could help solve the last-mile problem. For example, a study using smartcard data in Nanjing explored the general characteristics of metro–bicycle transfer trips. Results showed that commuting on workdays is a significant and common purpose of metro–bicycle integration, and that improving the route environment for metro–bicycle integration is of great significance to alleviate traffic congestion in megacities (Ma et al. 2018). Promoting cycling as a feeder mode would bring benefits to both cycling and transit (Ji et al. 2018; Liu et al. 2020). For example, a quantitative analysis of the pioneering large-scale bicycle sharing system called Vélib' in Paris, France, confirmed this positive effect. The results showed that close coupling of transit and vehicle-sharing systems is beneficial through improvement in the utilization rate of the public transit (Nair et al. 2013). A successful integration between the two will most likely result in an increase in the subsequent ridership of transit, the efficiency of transit, and the overall demand for cycling (Krizek and Stonebraker 2010; Liu et al. 2020; Zuo et al. 2020).

The use of bicycle as a feeder mode in metro–bicycle integration is influenced by many factors. As for the built environment, mixed land use, parks and public squares, and transportation facilities such as bicycle lanes have been found to be positively related to integrated use (Guo and He 2020). Areas that are less economically developed with fewer signalized intersections are more favored by those who use metro–bicycle integration (Ni and Chen 2020). However, research conclusions have differed among cities. In cities in China, such as Beijing and Nanjing, the combined use with bicycle as a feeder mode occurs more in high-density areas, while in cities in the United States, such as Minneapolis, MN, and Washington, DC, areas with low population density attract more ridership (Martin and Shaheen 2014; Lin et al. 2018). As for individual factors, people with a high school degree or even below may use public bicycles as a feeder mode more frequently (Liu et al. 2020). Female, older, and low-income commuters are less likely to use public bicycles to access transit (Ji et al. 2016; Yang et al. 2016). The lack of a second car in a household may be one reason for people choosing the metro–bicycle integration (Liu et al. 2020). As for travel-related characteristics, travel purpose shows a significant impact. A study in Nanjing, China, found that integration occurs more frequently for non-time-sensitive trip purposes, such as visiting friends and shopping (Chen et al. 2012). Moreover, travel with long time and distance tends to have a higher possibility of metro–bicycle integration (Yang et al. 2016; Liu et al. 2020).

However, there are still some research gaps in studies on public bicycles. First, although there are many studies investigating the adoption of public bikes, there is little discussion on how a public bicycle system could better integrate with a metro system. Moreover, further analysis is needed for planners and policymakers to identify which factors can influence the metro–bicycle integration. For example, many studies have paid attention to public bicycle usage and noted that several factors could influence people’s choice, including infrastructure such as bicycle lanes (Dill and Carr 2003; Faghih-Imani et al. 2014; Verma et al. 2016; Assunçao-Denis and Tomalty 2019; Cauwenberg et al. 2019) and the connectivity of streets (Moudon et al. 2005; Dill and Voros 2007; Titze et al. 2008; Beenackers et al. 2012). In addition to built-environment factors, studies have also focused on the association between people’s individual characteristics and their cycling behavior (Cervero et al. 2009; Scott and Ciuro 2019). It seems that there is already some consensus on the value of public bicycles, as well as which factors may influence their usage. However, the use of a public bicycle as a feeder mode is much less discussed in the literature. Although metro–bicycle integration has shown great potential (Zhang et al. 2019), there is a lack of agreement on how to promote this integration in megacities. Thus, further studies are needed to provide more indications for planners and policymakers. Second, although many studies have explored the correlation between the usage of public bicycles and the built environment attributes around the destination or the origin, few of them have paid attention to the factors that could influence the whole journey (such as tree shade along the route or the directness of the trip). Thus, more explorations are needed to see how built environment along the route affects travel behavior for metro–bicycle integration and whether different attributes influence each other.

Based on a survey at two metro stations with high traffic, high density, and mixed land use in Beijing, this research investigated the travel behavior of commuters who use public bicycle as a feeder mode to metro stations. We asked three questions: (1) Which factors influence the mode choice between cycling and walking? (2) What types of routes do commuters prefer when cycling to a metro station? (3) For what distance are public bicycles primarily used as feeder mode, and do they apply to solving the last-mile problem? Based on the binary logit modeling results and statistical analysis, three points are discussed to understand the travel behavior and preference of commuters when connecting their origins/destinations with metro stations. Furthermore, the discussion provides a reference for policymakers on promoting public bicycle as a feeder mode to cover the last mile.

First, the results from the binary logit model confirm the influence of socioeconomic individual factors when choosing a feeder mode between bicycling and walking. Among the sampled travelers, older people showed a higher likelihood to use public bicycles as a feeder mode to gain access to/from metro stations, while young people may prefer walking. This result appears to be in contrast with some previous studies that determined that young people and low-income groups were more likely to cycle (Dill and McNeil 2013; Guo and He 2020). Considering the direct health benefits of bicycling, this physical activity makes a contribution to lower the odds of being overweight or obese (Ming and Chris 2008) and increases those of having a higher level of cardiovascular fitness, as well as an overall reduction in cardiovascular risk (Mark and Yoichi 2008). There is also evidence that commuters who take up cycling for transport can reduce body fat (Møller et al. 2011). Thus, a possible explanation could be that, in China, many people aged between 45 and 60 years old may be more likely to do cycling as an exercise activity. Thus, the differences between cities should be considered when drawing research conclusions. Moreover, since the sampled travelers were all under 60 years old, this result does not mean that elderly people cycle more when they are over age 60. In fact, due to the need to be physically fit in order to ride a bicycle, people over 60 may turn to other alternative modes. As for income, the middle-income group tend to be the main user of the public bicycle system around metro stations in Beijing, while the low-income group and the high-income group are less likely to take public bicycle as a feeder mode. This finding indicates that planners and managers of public bicycle system should adopt appropriate charging strategies according to the consumption capacity and preferences of middle-income groups to achieve a larger user base for public bicycling. However, the influence of income differs among cities. In the United States, bicycle usage was reported to be negatively associated with income levels, which may indicate higher usage among people with more-limited transfer options (Krizek and Stonebraker 2010). In the Netherlands, the opposite result was reported, stating that people with high income levels are more likely to use bicycles and rail transit in combination (Krygsman and Dijst 2001; Bachand-Marleau et al. 2011).

Second, the built environment represented by the four types of routes in our study did not have a significant effect on mode choice between bicycling and walking. Since multiple attributes of the built environment have been integrated into several categories of routes, the result indicates that, although each single factor has an effect [e.g., more bicycle lanes are found to be associated with higher use of bicycles (Dill and Carr 2003; El-Assi et al. 2017) and mixed land use often increases the use of public bicycles (Zhang et al. 2017)], when the built environment appears as an integrated factor, the effects become weakened. In a real-world situation, travelers are facing with a built environment composed of various attributes rather than a single element, and the offsetting of the positive and negative effects could lead to a failure of some current efforts for promoting cycling. Planning and designing around metro stations should be done from an overall perspective. Instead of only building bicycle lanes, street intersection, tree shade, and streetlamps should also be taken into consideration to work together to create an environment that promotes cycling as a feeder mode. What is more, there exists a difference in built-environment preferences among different groups of travelers. In general, the built environment that improves travel efficiency through higher directness is more attractive for cyclists, while bicycle lanes seem not to be crucial. Previous studies have mainly shown the effects of income and education level on deciding whether to cycle (Handy et al. 2014; Julii et al. 2019), but pay little attention to their effect on which route to choose. The results in this paper indicate that as income and education level grow higher, travelers shift toward a preference for a built environment with higher security and more comfort (such as lower traffic and increased tree shade). It seems that different groups have different expectations about transfer travel. People who are older or have a higher income level or a higher education level pay more attention to comfort and safety in the travel experience, while those who are younger, with a lower income or lower education level, may give priority to a highly efficient built environment.

Third, travel distance had a significant influence on mode choice for accessing metro stations. As previous studies have shown that, travel distance could be an important predictor for mode choice (Handy et al. 2014; Chillón et al. 2016). In our study, we found that longer transfer travel distance makes cycling more attractive than walking for commuters. Meanwhile, among commuters who choose a public bicycle as a feeder mode, the distribution of travel distance presents some specific characteristics: 67.9% of cycling trips were between 500 and 1,000 m, and up to 91.8% of cycling trips were under 2,000 m. The survey results also demonstrated that trips for accessing metro stations are mainly around 1 km, and that public bicycle has made a great contribution to cover this short distance. It also confirms that cycling could be one of the most direct, convenient, and pleasant options for many short-distance intra-urban trips (Woodcock et al. 2009). With the substantial improvement of the efficiency between metro stations in megacities like Beijing, the last mile from the origin to a metro station or from a station to the destination provides an opportunity for promoting public bicycles. By integrating public bicycle and metro as a combination, commuters could significantly reduce their travel time, while simultaneously benefitting the environment and their physical health.

For future policy, promoting public bicycle for short-distance travel and especially for accessing metro stations could be a target of policy efforts in China. To achieve this, socioeconomic individual characteristics of the potential users and the built-environment features along the transfer route should be studied. On the one hand, since older people (from 46 to 60 years old in our sample) and the middle-income group showed significantly higher preferences for cycling, the design of bicycle facilities and charging strategies could be tailored to the needs of that group. At present, in Beijing the public bicycle service implements a membership card for pricing and cycling within 1 hour is free and 1 CNY per hour if longer. Since the average income in Beijing reached 7,706 CNY per month in 2016, the cost of riding a public bicycle is affordable. On the other hand, the comfort and convenience of public bicycle as a feeder mode must be further improved. In addition to constituting the largest group of users, younger people and the lower or higher income groups could also be encouraged to use a public bicycle as a feeder mode through some targeted services, such as different types of charging rules.

Furthermore, promotional measures should be implemented not only around the public bicycle service station, but also along the whole route from metro stations to the bicycle stations nearby. All the built characteristics should be taken into consideration, including bicycle lanes, street intersection, tree shade, and so on, to jointly contribute to a bicycle-friendly built environment. Since 2018, Beijing has started the construction of bicycle lanes and management of a 928 km slow-traffic system in key areas (BTI 2019). A 6.5 km bicycle lane located between the Huilongguan Community and Shangdi District was put into operation in 2019. The construction of bicycle lanes does help to encourage residents to use bicycles more, but this measure alone is not enough, and the optimization of the built environment for cycling along the route is also of great importance. More trees, higher level of directness, more streetlamps, and fewer intersections all maybe useful to promoting the use of public bicycles. Moreover, to better promote the integration of the bicycle system and the metro system, public bicycle service stations should be located near metro stations or bus stations.

Using public bicycle as a feeder mode for access to/from metro stations has been shown to have great potential. Promoting the integration of public bicycle and the metro system provides an effective solution for the last-mile problem. By taking Beijing as a case study, this paper answers the questions that often confuse urban planners and politicians: which groups of people are more likely to use public bicycle as a feeder mode to metro stations, and how can we promote metro–bicycle integration by designing the built environment? Differently from most of the previous studies, this research considered the built environment along the route instead of only in isolated spots. A method of classification was adopted because the travelers faced several types of built environment rather than independent single factors. Therefore, this study provides a new perspective for investigating the influence of the built environment and individual factors on travel behavior.

Commuters who are older (the oldest respondent in our sample is aged 60) and with a middle-income level were found to be more likely to use public bicycle systems, whereas the subgroups of gender, occupational class, and education level did not show significant differences. In addition to mode choice, people with different individual socioeconomic characteristics also demonstrated different preferences in terms of route choice. With higher education and income levels, commuters were more concerned about comfort and safety of the route rather than efficiency. Although built-environment characteristics do have an influence on transfer route choice, when the factors appear as a “whole environment” instead of single and separate elements, positive effects may be offset by some negative effects. It is thus important to consider not only more bicycle lanes, but also higher directness, more streetlamps, and more green spaces with trees to jointly build a bicycle-friendly environment. Since up to 91.8% of the respondents used a public bicycle to access metro stations ride within a distance of 2 km, promoting a public bicycle system in Beijing would absolutely make a contribution to solve the last-mile problem. Thus, the public bicycle rental stations and supporting facilities could be located near metro stations and other public transport, such as bus stations. Moreover, pricing for the public bicycle system may be integrated with the fare system of subways, which will offer some incentives to attract commuters to use a bicycle for transfers. Taking all these effects into account, a public bicycle system has great potential to be developed as a green, efficient, and healthy beneficial feeder mode to solve the last-mile problem. Political measures should be taken to specifically encourage metro–bicycle integration. Through the guarantee of infrastructure, the design and adjustment of charging rules, and the advocacy and promotion of a social atmosphere, the integration of bicycles and metro could attract more commuters as well as travelers for other purposes.

The study contributes to the urban planning literature through three main aspects. First, the findings and conclusions can enhance our existing understanding of public bicycle as a feeder mode to solve the last-mile problem. We find that commuters who are older (the oldest respondent in our sample is aged 60) and with a middle-income level were found to be more likely to use public bicycle systems. However, the subgroups of gender, occupational class, and education level did not show significant differences. What is more, up to 91.8% of the respondents who used a public bicycle to access metro stations ride within a distance of 2 km. Thus, promoting the integration of public bicycle and a metro system would be beneficial in megacities such as Beijing. Second, the findings provide a view of how to build a friendly environment for better usage of public bicycle in megacities. Our study shows that comfortable and safe routes can encourage commuters to use public bicycle as a feeder mode, especially for those with higher educational and income levels. Furthermore, to build a friendly environment for metro–bicycle integration, multiple factors should be considered jointly. For example, a route with more bicycle lanes may not encourage people to cycle if the directness level is quite low. Third, the findings provide new policy implications that promoting the integration of public bicycles and public transit should be adapted to the local conditions. Since the results of our study in Beijing differ from those in other cities, an effective policy must consider the unique travel preferences of local people. The incentives that worked in the United States, such as increasing bicycles lanes, may not work that well in China if other factors, such as streetlamps and tree shade, are ignored. Local survey and study are crucial for policymaking and urban planning.

In terms of future research in this area, there remain some limitations of this study that could be addressed. First, the survey area focused on two similar metro stations, which could represent only one type of land use with similar high density and fixed usage. Therefore, the conclusions may not apply to other districts and cities. And due to the limited scope and time range of the survey, valid samples may not cover all kinds of travelers. In this study, our sample is skewed toward young and middle-aged commuters. Second, the difficulty of collecting spatial data makes it challenging to explore all the built environment elements. Factors that were not included in our study might also influence cycling behavior. For example, the accessibility of public bike stations and the scale of the bicycle supply may also influence commuters’ behavior. Third, the travel situation faced by travelers differs, and there are differences in personal habits, lifestyle, and preferences that may also have an influence on their travel decisions. Further investigations should study more metro station areas with different land use and even in different cities, and comparisons should be made to determine whether the influence of age and income varies across areas. In addition, reasons and explanations for the influence of individual factors and different preferences between groups must be further examined.

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

This paper is funded by National Natural Science Foundation of China (41925003) and Research Councils of United Kingdom Global Challenges Research (R48843).