Cross-city transfer learning for optimal e-scooter parking station deployment: Evidence from 25 European cities

Abstract

The rapid growth of shared e-scooters has presented new challenges for urban management, especially in cities newly introducing the service, where scientifically planning parking stations to prevent disorganized parking is a time-consuming and costly problem. This paper proposes a cross-city transfer learning framework designed to rapidly predict rational layouts for fixed e-scooter parking stations in data-sparse new cities. The method utilizes operational data from 25 European cities and multi-source urban open-space data, constructing a transfer prediction model by discretizing cities into hexagonal grids and embedding spatial feature vectors. The results indicate that the effectiveness of group-based transfer learning is significantly influenced by geographic location, population size, and economic level, with the most effective transfers occurring between economically similar cities (an average F1-score of 0.801 for the super-high-income group). Additionally, our multi-dimensional city similarity matching strategy—based on socio-economic, point-of-interest (POI) distribution, and spatial structure features—demonstrates better stability and generalization, particularly in achieving the Top-3 similarity match. This research provides city planners and operators with data-driven insights to design shared e-scooter parking infrastructure efficiently.

Graphical abstract

Keywords

shared e-scooter / parking planning / cross-city / transfer learning / urban similarity

References

[1] Abouelela M, Chaniotakis E, Antoniou C, (2023). Understanding the landscape of shared-e-scooters in North America; Spatiotemporal analysis and policy insights. Transportation Research Part A, Policy and Practice, 169: 103602

[2] Almaskati D, Kermanshachi S, Pamidimukkala A, (2024). Convergence of emerging transportation trends: A comprehensive review of shared autonomous vehicles. Journal of Intelligent and Connected Vehicles, 7( 3): 177–189

[3] Bieliński T, Ważna A, (2020). Electric scooter sharing and bike sharing user behaviour and characteristics. Sustainability, 12( 22): 9640

[4] Brown A, (2021a). Micromobility, macro goals: Aligning scooter parking policy with broader city objectives. Transportation Research Interdisciplinary Perspectives, 12: 100508

[5] Brown AKlein N JThigpen C (2021b). Can you park your scooter there? Why scooter riders mispark and what to do about it. Findings

[6] Brown A, Klein N J, Thigpen C, Williams N, (2020). Impeding access: The frequency and characteristics of improper scooter, bike, and car parking. Transportation Research Interdisciplinary Perspectives, 4: 100099

[7] Button K, Frye H, Reaves D, (2020). Economic regulation and E-scooter networks in the USA. Research in Transportation Economics, 84: 100973

[8] Cao Z, Zhang X, Chua K, Yu H, Zhao J, (2021). E-scooter sharing to serve short-distance transit trips: A Singapore case. Transportation Research Part A, Policy and Practice, 147: 177–196

[9] Chen J, Li K, Li K, Yu P S, Zeng Z, (2021). Dynamic planning of bicycle stations in dockless public bicycle-sharing system using Gated Graph Neural Network. ACM Transactions on Intelligent Systems and Technology, 12( 2): 1–22

[10] Colovic A, Prencipe L P, Giuffrida N, Ottomanelli M, (2024). A multi-objective model to design shared e-kick scooters parking spaces in large urban areas. Journal of Transport Geography, 116: 103823

[11] Corbane C, Pesaresi M, Kemper T, Politis P, Florczyk A J, Syrris V, Melchiorri M, Sabo F, Soille P, (2019). Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data, 3( 2): 140–169

[12] Curtale R, Liao F, (2023). Travel preferences for electric sharing mobility services: Results from stated preference experiments in four European countries. Transportation Research Part C, Emerging Technologies, 155: 104321

[13] Deveci M, Gokasar I, Pamucar D, Chen Y, Coffman D, (2023). Sustainable E-scooter parking operation in urban areas using fuzzy Dombi based RAFSI model. Sustainable Cities and Society, 91: 104426

[14] Emami E, Ramezani M, (2024). Integrated operator and user-based rebalancing and recharging in dockless shared e-micromobility systems. Communications in Transportation Research, 4: 100155

[15] Eurostat (2025). The home of high-quality statistics and data on Europe. Available at the website of ec.europa.eu/eurostat

[16] Fazio M, Giuffrida N, Le Pira M, Inturri G, Ignaccolo M, (2021). Bike oriented development: Selecting locations for cycle stations through a spatial approach. Research in Transportation Business & Management, 40: 100576

[17] Fearnley N (2020). Micromobility – regulatory challenges and opportunities. In A. Paulsson & C. H. Sørensen (Eds.), Shaping Smart Mobility Futures: Governance and Policy Instruments in times of Sustainability Transitions. Emerald Publishing Limited. 169–186

[18] Fuady S N, Pfaffenbichler P C, Susilo Y O, (2024). Bridging the gap: Toward a holistic understanding of shared micromobility fleet development dynamics. Communications in Transportation Research, 4: 100149

[19] Gössling S, (2020). Integrating e-scooters in urban transportation: Problems, policies, and the prospect of system change. Transportation Research Part D, Transport and Environment, 79: 102230

[20] Guo B, Li J, Zheng V W, Wang Z, Yu Z, (2018). CityTransfer: Transferring inter- and intra-city knowledge for chain store site recommendation based on multi-source urban data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1( 4): 1–23

[21] Guo Z, Liu J, Zhao P, Li A, Liu X, (2023). Spatiotemporal heterogeneity of the shared e-scooter–public transport relationships in Stockholm and Helsinki. Transportation Research Part D, Transport and Environment, 122: 103880

[22] Hawa L, Cui B, Sun L, El-Geneidy A, (2021). Scoot over: Determinants of shared electric scooter presence in Washington D.C. Case Studies on Transport Policy, 9( 2): 418–430

[23] He SShin K G (2020). Dynamic flow distribution prediction for urban dockless e-scooter sharing reconfiguration. Proceedings of the Web Conference 2020, 133–143

[24] Heumann M, Kraschewski T, Brauner T, Tilch L, Breitner M H, (2021). A spatiotemporal study and location-specific trip pattern categorization of shared e-scooter usage. Sustainability (Basel), 13( 22): 12527

[25] Huang Y, Song X, Zhu Y, Zhang S, Yu J J, (2023). Traffic prediction with transfer learning: A mutual information-based approach. IEEE Transactions on Intelligent Transportation Systems, 24( 8): 8236–8252

[26] Hurlet P, Manout O, Diallo A O, (2024). Policy implications of shared e-scooter parking regulation: An agent-based approach. Procedia Computer Science, 238: 444–451

[27] Khan KRehman S UAziz KFong SSarasvady S (2014). DBSCAN: Past, present and future. The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), 232–238.

[28] Klein N, Brown A, Thigpen C, (2023). Clutter and compliance: Scooter parking interventions and perceptions. Active Travel Studies, 3( 1): 1–16

[29] Krauss K, Gnann T, Burgert T, Axhausen K W, (2024). Faster, greener, scooter? An assessment of shared e-scooter usage based on real-world driving data. Transportation Research Part A, Policy and Practice, 181: 103997

[30] Kuang S, Liu Y, Wang X, Wu X, Wei Y, (2024). Harnessing multimodal large language models for traffic knowledge graph generation and decision-making. Communications in Transportation Research, 4: 100146

[31] Li A, Zhao P, Liu X, Mansourian A, Axhausen K W, Qu X, (2022). Comprehensive comparison of e-scooter sharing mobility: Evidence from 30 European cities. Transportation Research Part D, Transport and Environment, 105: 103229

[32] Liu Y, Guo B, Zhang D, Zeghlache D, Chen J, Hu K, Zhang S, Zhou D, Yu Z, (2021a). Knowledge transfer with weighted adversarial network for cold-start store site recommendation. ACM Transactions on Knowledge Discovery from Data, 15( 3): 1–27

[33] Liu Y, Guo B, Zhang D, Zeghlache D, Chen J, Zhang S, Zhou D, Shi X, Yu Z, (2021b). MetaStore: A task-adaptative meta-learning model for optimal store placement with multi-city knowledge transfer. ACM Transactions on Intelligent Systems and Technology, 12( 3): 1–23

[34] Liu Y, Wu F, Liu Z, Wang K, Wang F, Qu X, (2023). Can language models be used for real-world urban-delivery route optimization. The Innovation, 4( 6): 100520

[35] Liu ZShen YZhu Y (2018). Inferring dockless shared bike distribution in new cities. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 378–386

[36] Ma C, Xue F, (2024). A review of vehicle detection methods based on computer vision. Journal of Intelligent and Connected Vehicles, 7( 1): 1–18

[37] Ma X, Cao R, Jin Y, (2019). Spatiotemporal clustering analysis of bicycle sharing system with data mining approach. Information, 10( 5): 163

[38] Miller H J, (2004). Tobler’s First Law and Spatial Analysis. Annals of the Association of American Geographers, 94( 2): 284–289

[39] Naidu GZuva TSibanda E M (2023). A review of evaluation metrics in machine learning algorithms. In Computer science on-line conference. Cham: Springer International Publishing. 15–25

[40] Peled I, Lee K, Jiang Y, Dauwels J, Pereira F C, (2021). On the quality requirements of demand prediction for dynamic public transport. Communications in Transportation Research, 1: 100008

[41] Pérez-Fernández O, García-Palomares J C, (2021). Parking places to moped-style scooter sharing services using GIS location-allocation models and GPS data. ISPRS International Journal of Geo-Information, 10( 4): 230

[42] Raczycki KSzymański P (2021). Transfer learning approach to bicycle-sharing systems’ station location planning using OpenStreetMap data. Proceedings of the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, 1–12

[43] Sandoval R, Van Geffen C, Wilbur M, Hall B, Dubey A, Barbour W, Work D B, (2021). Data driven methods for effective micromobility parking. Transportation Research Interdisciplinary Perspectives, 10: 100368

[44] Shah N R, Guo J, Han L D, Cherry C R, (2023). Why do people take e-scooter trips? Insights on temporal and spatial usage patterns of detailed trip data. Transportation Research Part A, Policy and Practice, 173: 103705

[45] Tier (2024). TIER Mobility API Documentation. Available at the website of tier-services. io

[46] Tuli F M, Mitra S, (2024). Dissecting shared e-scooters usage patterns and its impact on other transportation modes: A case study of Portland city. Travel Behaviour & Society, 36: 100812

[47] Vinagre Díaz J J, Fernández Pozo R, Rodríguez González A B, Wilby M R, Anvari B, (2024). Blind classification of e-scooter trips according to their relationship with public transport. Transportation, 51( 5): 1679–1700

[48] Wang L, Guo B, Yang Q, (2018). Smart city development with urban transfer learning. Computer, 51( 12): 32–41

[49] Wang S, Miao H, Li J, Cao J, (2022). Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks. IEEE Transactions on Intelligent Transportation Systems, 23( 5): 4695–4705

[50] World Population Review (2025). Europe Cities by Population 2025. Available at the website of worldpopulationreview.com

[51] Woźniak SSzymański P (2021). Hex2vec: Context-aware embedding H3 hexagons with OpenStreetMap tags. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, 61–71

[52] Xiao L, Xu W, (2024). Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data. Frontiers of Engineering Management, 11( 2): 231–246

[53] Xie S, Liao F, (2024). Incorporating personality traits for the study of user acceptance of electric micromobility-sharing services. Transportation Research Part F: Traffic Psychology and Behaviour, 107: 1015–1030

[54] Yan S, O’Connor N E, Liu M, (2024). U-Park: A user-centric smart parking recommendation system for electric shared micromobility services. IEEE Transactions on Artificial Intelligence, 5( 10): 1–15

[55] Yang Y, Zhan J, Liu Y, Qu X, (2025a). Policy-aware cross-city learning: A framework for sustainable urban governance. The Innovation, 6( 9): 100986

[56] Yang Y, Zhan J, Liu Y, Wang Q, (2025b). Cross-city transfer learning: Applications and challenges for smart cities and sustainable transportation. Communications in Transportation Research, 5: 100206

[57] Yin Z, Hardaway K, Feng Y, Kou Z, Cai H, (2023). Understanding the demand predictability of bike share systems: A station-level analysis. Frontiers of Engineering Management, 10( 4): 551–565

[58] Yue J, Long Y, Wang S, Liang H, (2024). Optimization of shared electric scooter deployment stations based on distance tolerance. ISPRS International Journal of Geo-Information, 13( 5): 147

[59] Yue Y, Zhuang Y, Yeh A G O, Xie J Y, Ma C L, Li Q Q, (2017). Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy. International Journal of Geographical Information Science, 31( 4): 658–675

[60] Zakhem M, Smith-Colin J, (2021). Micromobility implementation challenges and opportunities: Analysis of e-scooter parking and high-use corridors. Transportation Research Part D, Transport and Environment, 101: 103082

[61] Zhang F, Lv H, Xing Q, Liao F, (2025). Dynamic and personalized battery-swapping recommendations for electric micro-mobility vehicles: Leveraging deep reinforcement learning. Transportation Research Part E, Logistics and Transportation Review, 201: 104268

[62] Zhang Y, Lin D, Mi Z, (2019). Electric fence planning for dockless bike-sharing services. Journal of Cleaner Production, 206: 383–393

[63] Ziedan AShah N RBrakewood CCherry C (2023). A method for placing shared e-scooters corrals near transit stops. SSRN Electronic Journal

RIGHTS & PERMISSIONS

Higher Education Press