With the intensification of urban commuting pressures, electric bicycles (e-bikes) have emerged as a vital supplement to conventional transportation modes due to their flexibility, costefficiency, and low environmental impact. Compared to traditional bicycles, e-bikes offer improved efficiency and convenience, making them particularly suitable for short- and medium-distance travel. While recent national and local policies have encouraged their adoption, there remains a notable research gap concerning the systematic spatial planning and environmental adaptation for e-bike travel. This study investigates the factors influencing e-bike commuting behavior in the main urban area of Jinan, China. Drawing on 2019 household travel survey data encompassing 15 990 households and over 38 000 individuals, the study integrates multi-source geospatial data—including land use, POIs, road networks, and real estate prices—and constructs built environment and socioeconomic indicators for both residential and workplace locations. Using a Gradient Boosting Decision Tree (GBDT) model, the study quantifies the nonlinear impacts and threshold effects of built environment features on the probability of choosing e-bike commuting and further examines how these effects vary among income groups. Key findings are summarized as follows: Distance-Dependent Mode Choice. E-bike commuting exhibits a clear nonlinear response to commuting distance. The probability of selecting an e-bike peaks at around 2 300 meters (with a maximum probability of 55%), indicating strong suitability for mid-range commuting (1 000–3 500 meters). However, this probability declines sharply beyond 3 500 meters and is nearly negligible beyond 20 kilometers. In contrast, cars dominate for long-distance commuting (>6 000 meters), while walking is most prevalent under 1 000 meters.Built Environment Effects. The impact of built environment characteristics on commuting decisions is complex and nonlinear. Residential location factors—particularly building density, land-use mix, public service land ratio, and road network density—exert more substantial influence compared to workplace factors. High residential building density, for instance, enhances e-bike usability by ensuring population clustering and proximity to service facilities. Conversely, extremely dense commercial or road networks, particularly in workplace zones, may deter e-bike usage due to congestion, safety risks, and limited parking availability. Socioeconomic Differentiation. There is significant heterogeneity in commuting behavior responses across income groups. Low-income individuals are more sensitive to spatial accessibility and heavily reliant on e-bikes due to limited access to private vehicles or public transport. Their commuting choices are predominantly shaped by residential environment features such as the availability of public services and road connectivity.Middle-income groups are influenced by both residential and workplace built environments, reflecting more dispersed employment locations and greater interdistrict mobility. High-income individuals prioritize commuting efficiency and comfort, showing greater responsiveness to road infrastructure quality and multimodal transport integration (e. g., proximity to public transport nodes). Their residential preferences are aligned with conventional residential areas characterized by clear functional zoning. Built Environment Threshold Effects. GBDT model outputs reveal specific tipping points for built environment variables. For example, residential green space ratios exceeding 15% significantly boost e-bike commuting probability, while public service land ratios above 30% show a U-shaped relationship. Excessive land-use mixing (e.g., mix indices >1.0) may reduce commuting necessity, thus diminishing e-bike attractiveness. Policy Implications. The findings underscore the need for differentiated spatial interventions and transportation policies. To encourage e-bike commuting, urban planners should focus on enhancing building density, service facility accessibility, and road connectivity in residential neighborhoods. In employment zones, balanced land-use planning—avoiding overly centralized commercial or industrial zoning—is essential to maintain e-bike feasibility. Furthermore, policy measures must address commuting equity by prioritizing infrastructure development in low-income areas and tailoring multimodal transit integration strategies for high-income groups.In conclusion, e-bike commuting is well-suited for short- to mid-distance urban travel and offers a promising path toward sustainable mobility. However, its adoption is constrained by spatial, infrastructural, and socioeconomic factors that exhibit complex, nonlinear dynamics. This study contributes to the evolving discourse on green urban mobility by integrating machine learning with spatial planning theory, offering a robust empirical basis for refined, equity-oriented transportation planning.Nevertheless, some limitations persist. The study focuses on a single temporal snapshot and does not account for temporal dynamics in commuting behavior. Additionally, potential confounding variables—such as lifestyle preferences and employer-provided transport services—were not fully controlled. Future research could leverage multi-temporal datasets to model commuting behaviors dynamically and extend the analysis to other cities for broader applicability.