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Vizmpa, C.; Botzoris, G.; Lemonakis, P.; Galanis, A. Micromobility in Urban Transportation. Encyclopedia. Available online: https://encyclopedia.pub/entry/53419 (accessed on 17 May 2024).
Vizmpa C, Botzoris G, Lemonakis P, Galanis A. Micromobility in Urban Transportation. Encyclopedia. Available at: https://encyclopedia.pub/entry/53419. Accessed May 17, 2024.
Vizmpa, Chrysa, George Botzoris, Panagiotis Lemonakis, Athanasios Galanis. "Micromobility in Urban Transportation" Encyclopedia, https://encyclopedia.pub/entry/53419 (accessed May 17, 2024).
Vizmpa, C., Botzoris, G., Lemonakis, P., & Galanis, A. (2024, January 04). Micromobility in Urban Transportation. In Encyclopedia. https://encyclopedia.pub/entry/53419
Vizmpa, Chrysa, et al. "Micromobility in Urban Transportation." Encyclopedia. Web. 04 January, 2024.
Micromobility in Urban Transportation
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This entry is adapted from the peer-reviewed paper 10.3390/land12122181

Micromobility is a novel approach to urban transportation that provides options for short-distance travel, such as first- and last-kilometer trips. Its primary attraction is the provision of an on-demand, affordable, eco-friendly, and adaptable transportation option, reducing reliance on private vehicles for short distances.

urban trail paths micromobility 15-minute city

1. Evolution, Trends, and Impacts on Urban Transportation

Micromobility is a novel approach to urban transportation that provides options for short-distance travel, such as first- and last-kilometer trips. Its primary attraction is the provision of an on-demand, affordable, eco-friendly, and adaptable transportation option [1], reducing reliance on private vehicles for short distances [2][3]. Micromobility solutions comprise an assortment of small, lightweight gadgets or mini-vehicles that operate at an average maximum speed of 45 km/h. Examples of these devices include bikes, scooters, skateboards, segways, and hoverboards. They can be either electric or human-powered, privately owned, or shared [4][5]. The benefits of micromobility solutions for cities include a shift toward sustainable and low-carbon forms of transportation, with the potential to disrupt the use of private vehicles, especially for short-distance travel.
Bicycle-sharing systems have gained a lot of traction in recent years in numerous cities worldwide [6]. The majority of passenger trips in China, the EU, and the USA were shorter than 5 km, accounting for 50–60% of the total passenger-km traveled. They could even assist with longer trips up to 20 km, provided that suitable and secure infrastructure is ensured, particularly in inner urban areas [4][7][8].
Analyzing the historical evolution and expansion of micromobility involves considering various factors, including technological advancements, environmental influences, and societal changes. In this context, a scientific analysis of the evolution of micromobility unfolds as follows:
  • Early forms of micromobility: The roots of micromobility can be traced back to early forms of small-scale transportation, such as bicycles, skateboards, and rollerblades.
  • Technological innovations: Advances in engineering, materials, and manufacturing processes have played an important role in the evolution of micromobility. A characteristic example is the transition from human-powered bicycles to electrically assisted bikes and scooters. Technological innovations have made these vehicles more lightweight, efficient, and accessible to people [9].
  • Internal combustion to electric power: The shift from internal combustion engines to electric power sources marks a significant turning point in the evolution of micromobility. Electric propulsion offers a cleaner and more sustainable alternative, aligning with contemporary environmental concerns. Bicycles, as a distinctive mode of micromobility, have notably evolved and benefited from this shift, with electric bikes playing a pivotal role in facilitating the widespread adoption of cycling [9].
  • Urbanization and changing transportation needs: With the progress of urbanization, the demand for space-saving and efficient transportation solutions for short-distance travel has increased. Micromobility options have emerged as a response to the challenges posed by congestion and the need for agile, easily maneuverable vehicles in densely populated urban areas [9].
  • Post-industrialization and economic factors: The post-industrial era presented changes in work patterns and economic structures, influencing transportation needs. Micromobility became more attractive for short-distance commuting and addressing ‘first/last mile’ needs in urban public transportation [10].
  • Lifestyle changes: Social changes, including an increasing emphasis on sustainability and an active lifestyle, have contributed to the expansion of micromobility. The desire for more personal mobility options that align with health and environmental awareness has driven the adoption of bicycles, electric scooters, and other compact vehicles [11][12][13].
  • Regulatory frameworks: The development of regulatory frameworks has played a vital role in shaping the evolution of micromobility. Safety concerns and the need for standardized instructions and guidelines have also influenced the design and operation of micromobility vehicles [5][14][15][16].
  • Integration of information technology: The integration of information technology, including GPS, smart mobile applications, and connectivity features, has renovated the way micromobility solutions are accessed and accomplished. Smart technologies have enhanced user experiences, improved fleet management, and contributed to the efficiency of micromobility services [17][18].
  • Globalization and market dynamics: The globalization of markets and the interconnectedness of economies have facilitated the spread of micromobility solutions across different regions. Market dynamics, including competition among companies and evolving consumer preferences, have driven innovation and improvements in micromobility options [10].
  • Environmental awareness and sustainability: Increasing awareness of environmental issues, coupled with a commitment to sustainability, have influenced the historical evolution of micromobility. The development and adoption of electrically powered vehicles align with efforts to reduce carbon footprints and promote eco-friendly transportation alternatives [12][19].

2. Factors Influencing the Adoption of Micromobility in Urban Transportation

The implementation of new mobility technologies and services depends on an understanding of the major variables influencing micromobility adoption, as well as an investigation of the factors influencing the adoption of new mobility technologies. An analysis of the variables influencing the uptake of micromobility vehicles was conducted by Zhang and Kamargianni [20]. Age, gender, income, and education level are sociodemographic factors that have a significant impact on the use of micromobility. In particular, young to middle-aged males are more likely to adopt shared bicycles, according to the article’s results. Disparities in the results of education and income are observed as well. For instance, in cities like Melbourne and Brisbane [21], population groups with higher incomes and levels of education tend to use shared bicycles more frequently, while Campbell et al. [22] claimed that in Beijing, China, low-income and less educated groups are more likely to use shared bicycles. Many factors, including cultural differences, can account for this divergence. The selection of micromobility is also influenced by factors related to travel behavior. The population groups most likely to use micromobility are those that prefer to walk and cycle and travel shorter distances. An individual’s intention to use micromobility is more strongly influenced by the built environment and the weather. For instance, it was discovered that being close to shared bicycles was a significant factor. In addition, separate bike lanes and high-quality pavement are beneficial features [20].
A systematic review of the variables influencing user behavior in micromobility sharing systems was conducted by Elmashhara et al. [23]. The paper divides the factors into three categories: weather, spatial, and temporal. These categories include weather-related factors, the built environment (land use), infrastructure for micromobility sharing systems, distance, topography, and weather. Convenience and utility, financial considerations, accessibility, usability, service quality, vehicle features and quality, rules, and app-related issues are some of the system-related factors. Lastly, sociodemographic characteristics, attitudes, green and sustainable motivations, social factors, safety and security concerns, perceived values, health issues, hedonistic values, the purpose of use, and perceived behavioral control are all factors related to users.
Bretones and Marquet [24] investigated the sociopsychological aspects of people adopting and using electric micromobility. The factors are divided into non-functional (emotional, social, and epistemic values) and functional (money, time, and other convenience values) categories based on a systematic literature review. Financial cost, usefulness, and comfort, accessibility and flexibility, and time savings are examples of functional factors. Safety is another example. A few instances of non-functional factors are interest in innovation and technology, riding experience, social perception, health and well-being benefits, and environmental awareness. The findings show that non-functional factors—like interest in new technologies, a sense of belonging, and environmental concerns—can have an even greater impact on modal choice than more conventional functional factors like cost, speed, and time savings. This provides more evidence that sociopsychological elements must be taken into account in any analysis of travel behavior [25].
Hosseinzadeh et al. [26] examined how various factors affect shared micromobility services. Their two main goals were, on the one hand, to thoroughly examine how various weather-related factors, major holidays, and special events affect micromobility services, and, on the other hand, to compare the effects of these factors on e-scooters and bikeshares in Louisville, Kentucky. The study’s findings demonstrated that the temperature index, the quantity of rain, and the presence of thunderstorms all had a significant impact on both micromobility systems. However, only e-scooter use was proved to be significantly increased by special events and major holidays. Finally, the findings demonstrated that the day of the week had a significant impact on shared e-scooter and bikeshare trips in various ways.
Christoforou et al. [27] examined neighborhood features in Bordeaux, France, that encouraged micromobility for both locals and visitors. The study found that the percentage of young people in an area had a strong positive impact on the results. The use of e-scooters was also positively correlated with income, with a stronger relationship observed in low-income areas. Additionally, the research revealed that employment density and housing density had a favorable impact, suggesting that dense, compact neighborhoods and areas with a high concentration of businesses are conducive to the use of e-scooters and are ideal candidates for the implementation of e-scooter sharing systems. The number of trips was strongly negatively affected by distance to the city center. On the positive side, the number of stores, pubs, and restaurants was found to positively influence the number of e-scooter trips. High positive coefficients for stations indicated strong synergies between e-scooters and the public transportation system. Network characteristics, such as the influence of nearby roads and cycling infrastructure, also had a favorable effect. High-density areas and places where parking is expensive and difficult appeared to be good options for shared micromobility schemes.
Table 1 provides a summary of the key factors influencing the adoption of micromobility in the urban environment.
Table 1. Key factors influencing the adoption of micromobility in urban environments.
Factor References Factor’s Items
Socio-demographic [20][21][23][24][25][26]
−
Age
−
Gender
−
Level of education
−
Income
−
Culture
Mobility and travel-behavior-related patterns [20][24][25][26]
−
Travel distance
−
Driving frequency
−
Accessibility
−
Flexibility
−
Time savings
Weather
and environment		 [20][23][26]
−
Temperature
−
Weather
−
Air quality
−
Noise
Built environment
and neighborhood
characteristics		 [20][23][26]
−
Infrastructure
−
Topography
−
Distance to the city center
−
Amenities
−
Availability of vehicles
Attitudes [23][24][25]
−
Environmental concern, sustainability
−
Privacy concerns
−
Safety concerns
−
Financial concerns
Technology-acceptance-related factors [23][24]
−
Perceived difficulty
−
Perceived usefulness
−
Convenience
−
Perceived values
−
Purpose of use
Motivations [23]
−
Green motivations
−
Health concerns
Personal traits [23]
−
Interest for new technologies
−
Perceptions of increased well-being
Temporal variables [26][27]
−
Weekday/weekend/holidays
−
Tourists/locals

References

  1. Shaheen, S.; Cohen, A.; Chan, N.; Bansal, A. Chapter 13-Sharing strategies: Carsharing, Shared Micromobility (Bikesharing and Scooter Sharing), Transportation Network Companies, Microtransit, and Other Innovative Mobility Modes. In Transportation, Land Use, and Environmental Planning; Elsevier: Amsterdam, The Netherlands, 2020; pp. 237–262.
  2. Tiwari, A. Micro-Mobility: The Next Wave of Urban Transportation in India. YSJOURNAL. 17 January 2019. Available online: https://yourstory.com/journal/micro-mobility-edc6x8f1y1 (accessed on 7 August 2023).
  3. Populus. The Micro-Mobility Revolution: The Introduction and Adoption of Electric Scooters in the United States. A Populus Research Report 2018. Available online: https://trid.trb.org/view/1528426 (accessed on 2 August 2023).
  4. Dia, H. Banning ‘Tiny Vehicles’ Would Deny Us Smarter Ways to Get Around Our Cities. THECONVERSATION. 2 April 2019. Available online: https://theconversation.com/banning-tiny-vehicles-would-deny-us-smarter-ways-to-get-around-our-cities-113111 (accessed on 1 September 2023).
  5. Abduljabbar, R.L.; Liyanage, S.; Dia, H. The role of micro-mobility in shaping sustainable cities: A systematic literature review. Transp. Res. Part D Transp. Environ. 2021, 92, 102734.
  6. Fong, J.; McDermott, P.; Lucchi, M. Micro-Mobility, E-Scooters and Implications for Higher Education. UPCEA Center for Research and Strategy. 2019. Available online: https://upcea.edu/wp-content/uploads/2019/05/UPCEA_Micro_Mobility-White-Paper-May-2019.pdf (accessed on 1 September 2023).
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  8. Møller, T.H.; Simlett, J. Micromobility: Moving Cities into a Sustainable Future. EY, EYGM Limited. 2020. Available online: https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/automotive-and-transportation/automotive-transportation-pdfs/ey-micromobility-moving-cities-into-a-sustainable-future.pdf (accessed on 12 August 2023).
  9. Yanocha, D.; Allan, M. The Electric Assist: Leveraging E-Bikes and E-Scooters for More Livable Cities. Institute for Transportation & Development Policy. 2019. Available online: https://www.itdp.org/wp-content/uploads/2019/12/ITDP_The-Electric-Assist_-Leveraging-E-bikes-and-E-scooters-for-More-Livable-Cities.pdf (accessed on 30 November 2023).
  10. DuPuis, N.; Griess, J.; Klein, C. Micromobility in Cities. A History and Policy Overview. National League of Cities. Available online: https://www.nlc.org/wp-content/uploads/2019/04/CSAR_MicromobilityReport_FINAL.pdf (accessed on 30 November 2023).
  11. Nigro, M.; Castiglione, M.; Colasanti, F.M.; De Vincentis, R.; Valenti, G.; Liberto, C.; Comi, A. Exploiting floating car data to derive the shifting potential to electric micromobility. Transp. Res. Part A Policy Pract. 2022, 157, 78–93.
  12. Blazanin, G.; Mondal, A.; Asmussen, K.E.; Bhat, C.R. E-scooter sharing and bikesharing systems: An individual-level analysis of factors affecting first-use and use frequency. Transp. Res. Part C Emerg. Technol. 2022, 135, 103515.
  13. Popova, Y.; Zagulova, D. Aspects of e-scooter sharing in the Smart City. Informatics 2022, 9, 36.
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  20. Zhang, Y.; Kamargianni, M. A review on the factors influencing the adoption of new mobility technologies and services: Autonomous vehicle, drone, micromobility and mobility as a service. Transp. Rev. 2023, 43, 407–429.
  21. Fishman, E.; Washington, S.; Haworth, N.; Mazzei, A. Barriers to bikesharing: An analysis from Melbourne and Brisbane. J. Transp. Geogr. 2014, 41, 325–337.
  22. Campbell, A.A.; Cherry, C.R.; Ryerson, M.S.; Yang, X. Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transp. Res. Part C Emerg. Technol. 2016, 67, 399–414.
  23. Elmashhara, M.G.; Silva, J.; Sá, E.; Carvalho, A.; Rezazadeh, A. Factors influencing user behaviour in micromobility sharing systems: A systematic literature review and research directions. Travel Behav. Soc. 2022, 27, 1–25.
  24. Bretones, A.; Marquet, O. Sociopsychological factors associated with the adoption and usage of electric micromobility. A literature review. Transp. Policy 2022, 127, 230–249.
  25. De Witte, A.; Hollevoet, J.; Dobruszkes, F.; Hubert, M.; Macharis, C. Linking modal choice to motility: A comprehensive review. Transp. Res. Part A Policy Pract. 2013, 49, 329–341.
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  27. Christoforou, Z.; Psarrou Kalakoni, A.M.; Farhi, N. Neighborhood characteristics encouraging micromobility: An observational study for tourists and local users. Travel Behav. Soc. 2023, 32, 100564.
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Subjects: Transportation Science & Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Chrysa Vizmpa
,
George Botzoris
,
Panagiotis Lemonakis
,
Athanasios Galanis
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Update Date: 04 Jan 2024
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