Abstract
Shared micromobility refers to the shared use of small, lightweight vehicles that are operated by a single person, with a slow speed and a short travel distance (Shaheen et al., 2020). Besides the advantages of being price-affordable and use-flexible, it has the potential to reduce carbon emissions, improve connections to public transport, provide travel alternatives, and promote a healthy lifestyle, and thus will significantly reshape human mobility and our urban future (Kong et al., 2020; Teixeira et al., 2022; Zhang & Mi, 2018). The root of shared micromobility can be traced back to the 1960s, when the idea of bike-sharing began in Amsterdam (the Netherlands) with the city’s White Bike Plan, which featured a small fleet of white-colored bikes provided to the public completely free of charge (Abduljabbar et al., 2021). Since then, several generations of bike-sharing systems have been developed with the help of advanced technologies like smart card technology, GPS devices, and mobile phone applications. Nowadays, more free-floating systems have been adopted in the world, allowing users to enjoy a higher flexibility to park at any permitted location (Zhang et al., 2019). Besides bike-sharing, shared e-bikes and e-scooters have also been rising in popularity recently, providing additional benefits like making travelling easier, longer, or faster. In 2021, the ridership of shared micromobility in the United States reached 112 million trips, almost doubling 2020’s ridership of 65 million trips (Brady, 2022). According to the McKinsey report “The Future of Mobility” released in April 2023, the global micromobility market is worth about $180 billion now, and its value could more than double by 2030 to reach about $440 billion.
Future cities are the scientific imagination of the places where we work, live, study, and play. They are knowledge-based, mainly driven by innovation and the widespread use of emerging advanced technologies, covering all facets of urban life. The cities nowadays are facing serious urban issues, like congested traffic systems, high housing prices, environmental pollution, and social inequity. Ideally, all these issues should be carefully considered and well addressed in a future city. Associated with various socioeconomic and environmental benefits, shared micromobility can be regarded as a “smart” choice to address current urban issues and a key component to establishing future cities that are more sustainable, inclusive, and livable. In a future sustainable city, it is reasonable to imagine that most people would prefer to use public transport and shared mobility, without the necessity to own bikes, scooters, or cars.
On the other hand, in recent decade, benefitting from the open data movement, more and more user-generated micromobility data are available for public use and research purposes in many cities like New York City in the United States (Chen et al., 2022), London in the United Kingdom (Heydari et al., 2021), and Montreal in Canada (Faghih-Imani et al., 2014). Some leading service providers, including those in China (e.g., Hellobike and Meituan), are willing to provide anonymous data to support data-driven studies. In addition, the research of shared micromobility is greatly powered by advanced technologies in GIS, big data, and artificial intelligence, making it easier to reveal in-depth patterns from massive trip data. However, despite the fact that shared micromobility studies are booming, there remain a number of issues to be explored, especially considering we are facing the rapid development of the micromobility industry worldwide.
Against this background, we have organized this special issue, “Shared Micromobility and Future Cities”. The issue aims to explore the potential of emerging data sources and advanced technologies and methods to investigate shared micromobility and its influence on creating a better future city. It includes five papers, addressing the topics of trip purpose inference (Wang & Zhang, 2023), bike-metro integration (Lin et al., 2023), cycling corridor continuity (Huang et al., 2023), segmentation of early adopters (Wu et al., 2023), and the disruption of dockless bike-sharing emergence (Zhou et al., 2023). We further introduce these papers as follows.
Using a Latent Dirichlet Allocation-based analytical framework, Wang and Zhang (2023) inferred and compared trip purposes of dockless shared bikes and e-bikes in Ningbo, China. They successfully revealed seven typical trip purposes, i.e., transportation, work, lodging, eating, shopping, education, and others. Eating was the most prevalent trip purpose for both bike and e-bike users. The proportions of transportation-related trips were insignificant compared to previous observations, for both shared bikes and e-bikes. Moreover, they found that shared bikes played a more significant role than shared e-bikes in daily trips related to lodging and education, indicating that the role of shared e-bikes in these trips might have been overestimated previously. Their study is able to provide insights into the trip purposes of shared micromobility and inform relevant policy-making and transportation planning.
Bike-metro integration is regarded as an effective way to improve the access to the public transit systems. Lin et al. (2023) assessed the metro accessibility by cycling at a finer spatiotemporal scale using a real bike trajectory dataset generated by cyclists in Shanghai, China. To achieve this goal, they proposed a new indicator, i.e., a metro accessibility level (MAL), which explicitly integrates metro crowdedness into the accessibility measurement. They then introduced a method to examine the possibility of avoiding metro crowdedness by using cycling as the access mode. Results showed that bike-metro integration increases the accessibility to the metro system in terms of a larger population coverage and a higher accessibility level. Omitting the metro crowdedness leads to an overestimation of the accessibility to metro systems, and the overestimation for the morning peak is larger than that of the afternoon peak. These results provide a good reference for transportation planning, modeling, and policy-making to improve bike-metro integration.
Targeting at addressing the efficiency and safety concerns of urban cycling due to the unclear right-of-way and discontinuous non-motorized corridor, Huang et al. (2023) used the dynamic location data of the Meituan Bike (formerly Mobike) in the Hi-tech Park area of Shenzhen to analyze the spatial-temporal variations of bikeshare use, aiming at identifying the traffic corridors of cycling. Using the agent-based modeling and social force model, this research proposed a new approach of simulating the urban non-motorized traffic, and hence provided valuable insights for building bicycle lanes for cycling corridors. They demonstrated that adding bicycle lanes can reduce the traffic density of the non-motorized volume by 6% overall, and save the travel time of cyclists and pedestrians by 6.4% and 3.7%, respectively. The findings contribute to the development of urban cycling through the efforts of local government and operators.
With a specific focus on the segmentation of shared mobility services, namely niche market, Wu et al. (2023) explored the heterogeneous characteristics of potential early adopters of shared electric bikes among different transportation groups in the existing market. Using a questionnaire of 1,034 citizens in Shenzhen, China before the official launch of the shared electric bikes scheme in the city, they adopted binary logistic models and text sentiment analysis to understand the group-sensitive identification of potential early adopters. Results revealed that age, car ownership, commute time, and confidence level on the potential scheme are four significant variables to explain all respondents’ willingness to adopt shared electric bikes, regardless of their existing dominant transport mode. However, when it comes to subgroups with different travel preferences, the profile of potential early adopters varies distinctly. This study provides an analytical framework to comprehend the diverse features of prospective early shared electric bikes adopters.
To examine the evolution of public bike-sharing systems (PBSs) in China under the disruption of dockless bike-sharing systems’ (DBSs) emergence, Zhou et al. (2023) collected data on the start and end dates of PBSs and DBSs across the country, and utilized logistic regression and representative case studies to uncover the underlying structural and political factors affecting PBSs’ persistency. They found that the entry of DBSs disrupts PBS operations in many cities, and PBSs are less likely to cease operation in cities with subways and in coastal areas, but more likely to do so in densely populated cities. In addition, technical advancements, operational consistency, adherence to government guidelines, and a well-planned station layout all contributed to the continued viability of PBSs amidst the challenges posed by DBSs. This study provides useful insights on how PBSs evolved after DBSs emerged and how to improve the competitiveness of PBSs.
We hope this special issue could make a contribution to enrich our understanding of the development of shared micromobility from a data-driven perspective and in a Chinese urban context.