ARCHIVES

Shared transportation systems are increasingly playing a pivotal role in urban mobility. Micro-mobility solutions, in particular, have become essential as people seek fast, convenient ways to travel during their daily routines, avoid the hassles of parking their personal vehicles, and face challenges accessing public transportation at their preferred times or locations. Among the emerging shared transportation options, station-less electric scooters (e-scooters) have gained global popularity due to their ease of parking, environmental benefits, cost savings, and ability to alleviate traffic congestion. As a potential solution to first and last-mile problems, academic studies on e-scooter technology are expanding. This study analyzes data from a shared station-less e-scooter service operating in Türkiye to explore how weather-related parameters influence users’ behavior. Additionally, it offers an innovative perspective on geographic and regional variations in users’ behavior by comparing findings with studies from different cities and countries presented in the literature. This research also provides valuable insights for mobility service providers and city planners seeking to optimize shared transportation systems. Key Word: Shared e-scooter, log records, micromobility, user behavior, trip patterns REFERENCES 1. ‘World Cities Report 2022: Envisaging the Future of Cities | UN-Habitat’. Accessed: Nov. 21, 2024. [Online]. Available: https://unhabitat.org/world-cities-report-2022-envisaging-the-future-of-cities 2. G. Celik, U. D. Ulusar, A. Sarsenbay, and A. Taleb-Ahmed, ‘Innovative Parking Solutions in Smart Cities’, in 2023 8th International Conference on Computer Science and Engineering (UBMK), Sep. 2023, pp. 51–56. doi: 10.1109/UBMK59864.2023.10286713. 3. U. D. Ulusar, G. Celik, and F. Al-Turjman, ‘Wireless Communication Aspects in the Internet of Things: An Overview’, in 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops), Oct. 2017, pp. 165–169. doi: 10.1109/LCN.Workshops.2017.82. 4. N. Saum, S. Sugiura, and M. Piantanakulchai, ‘Short-Term Demand and Volatility Prediction of Shared Micro-Mobility: a case study of e-scooter in Thammasat University’, in 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), Nov. 2020, pp. 27–32. doi: 10.1109/FISTS46898.2020.9264852. 5. R. B. Noland, ‘Scootin’ in the rain: Does weather affect micromobility?’, Transp. Res. Part Policy Pract., vol. 149, pp. 114–123, Jul. 2021, doi: 10.1016/j.tra.2021.05.003. 6. A. A. Campbell, C. R. Cherry, M. S. Ryerson, and X. Yang, ‘Factors influencing the choice of shared bicycles and shared electric bikes in Beijing’, Transp. Res. Part C Emerg. Technol., vol. 67, pp. 399–414, Jun. 2016, doi: 10.1016/j.trc.2016.03.004. 7. H. Younes, Z. Zou, J. Wu, and G. Baiocchi, ‘Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C.’, Transp. Res. Part Policy Pract., vol. 134, pp. 308–320, Apr. 2020, doi: 10.1016/j.tra.2020.02.021. 8. D. J. Reck, H. Haitao, S. Guidon, and K. W. Axhausen, ‘Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland’, Transp. Res. Part C Emerg. Technol., vol. 124, p. 102947, Mar. 2021, doi: 10.1016/j.trc.2020.102947. 9. M. Abouelela, E. Chaniotakis, and C. Antoniou, ‘Understanding the landscape of shared-e-scooters in North America; Spatiotemporal analysis and policy insights’, Transp. Res. Part Policy Pract., vol. 169, p. 103602, Mar. 2023, doi: 10.1016/j.tra.2023.103602. 10. K. Gebhart and R. B. Noland, ‘The impact of weather conditions on bikeshare trips in Washington, DC’, Transportation, vol. 41, no. 6, pp. 1205–1225, Nov. 2014, doi: 10.1007/s11116-014-9540-7. 11. M. Hasan and V. P. Sisiopiku, ‘Shared E-Scooter Practices in Birmingham, Alabama: Analyzing Usage, Patterns, and Determinants’, Future Transp., vol. 4, no. 1, Art. no. 1, Mar. 2024, doi: 10.3390/futuretransp4010008. 12. J. K. Mathew, M. Liu, and D. M. Bullock, ‘Impact of Weather on Shared Electric Scooter Utilization’, in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Oct. 2019, pp. 4512–4516. doi: 10.1109/ITSC.2019.8917121. 13. U. D. Ulusar, G. Celik, E. Turk, F. Al-Turjman, and H. Guvenc, ‘Accurate indoor localization for ZigBee networks’, in 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, 2018, pp. 514–517. Accessed: Dec. 11, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8566532/ 14. G. Celik, M. Baimagambetova, and V. Abromavičius, ‘A Review of Fingerprinting Techniques for Smart Cities’, in 2022 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), Apr. 2022, pp. 1–4. doi: 10.1109/eStream56157.2022.9781754. 15. ‘GEZ’. Accessed: Dec. 12, 2024. [Online]. Available: https://gezmobility.com/ 16. ‘Meteoroloji Genel Müdürlüğü’. Accessed: Aug. 20, 2024. [Online]. Available: https://mevbis.mgm.gov.tr/