Modal synergies between dockless electric bikes and rail transit in Lausanne Switzerland

Abstract

The integration of cycling with public transport is a key lever for reducing car dependency. Despite the expansion of shared micromobility, empirical evidence on its integration with rail remains limited in medium-sized European cities. This study develops a framework to assess integration between dockless shared e-bikes (DBS) and rail transit in Lausanne, Switzerland. Using high-resolution operator data, we reconstruct e-bike trajectories and identify trips compatible with bike-and-ride transfers through a spatiotemporal “Inside Station Catchment Area” (ISCA) proxy. Descriptive and accessibility analyses characterise DBS–rail integration and quantify station catchment extensions relative to walk-and-ride scenarios. Findings indicate that a substantial share of DBS trips supports rail access and extends station catchments. OLS models across temporal periods identify the key accessibility, land use, and sociodemographic determinants of potential intermodal behaviour. The ISCA framework offers a transferable approach for evaluating first- and last-mile solutions, with implications for station-area planning and integrated mobility policy.

Introduction

Promoting a substantial shift toward sustainable modes of transport has become a central objective of mobility and climate policies. However, reducing car dependency and greenhouse gas emissions requires not only improvements in public transport supply, but also effective access solutions capable of making transit a competitive alternative to private car use. In this regard, expanding the effective station accessibility zones, commonly referred to as transit catchment areas, constitutes a key challenge for both researchers and practitioners¹. The literature consistently shows that improvements to public transport alone are insufficient to significantly reduce private car use, particularly in dispersed or low-density territories shaped by urban sprawl². Beyond infrastructure or service quality, the ability of travellers to conveniently reach stations and connections largely determines modal shift^3,4. Transit-oriented development (TOD) strategies aim to strengthen the relationship between public transport and the built environment, but their operationalisation remains predominantly focused on walking, typically assumed to define catchment radii of 400–800 m^5,6. As a result, extending station accessibility beyond walking distances has emerged as a prerequisite for increasing transit ridership. Bike-and-ride opportunities offer one of the most promising levers in this regard. Combining cycling with public transport enables users to cover substantially longer distances than walking at low energy cost⁷, thereby extending the spatial reach of transit services up to several kilometres^8,9. This potential has led to the development of extended planning frameworks such as Bicycle-based TOD^10,11, Micromobility-friendly TOD¹², or interestingly TOD-based 15-Minute Cities¹³. In recent years, the rapid rise of micromobility has further diversified intermodal access possibilities. Electric bikes, whether privately owned or accessed through sharing schemes, offer performance levels comparable to conventional bicycles^14,15. Among these emerging modes, dockless e-bikesharing (DBS) holds particular promise for first- and last-mile connectivity, as its spatial flexibility removes the infrastructural constraints associated with station-based systems and allows access to transit in areas where walking alone is insufficient. Despite this potential, empirical research explicitly examining the integration of DBS with public transport remains limited. Existing studies have largely focused on walk-and-ride or bike–train combinations using personal bicycles, leaving shared dockless systems comparatively understudied^16,17. Moreover, research on micromobility services has primarily emphasised safety issues rather than mobility behaviour¹⁸, despite growing evidence that such modes are likely to play an increasingly central role in multimodal chains^14,19. In the Swiss context, both the Federal Statistical Office and the Federal Office for Spatial Planning identify the first- and last-mile (FLM) challenge as a strategic analytical priority²⁰. In addition, existing empirical evidence remains geographically imbalanced, with a strong concentration on large metropolitan areas in East Asia and North America, while European medium-sized cities are underrepresented²¹. This limits the external validity of current findings and leaves important blind spots regarding regional rail systems and non-metropolitan contexts²². From a methodological perspective, research continues to rely predominantly on surveys and interviews, despite the increasing availability of high-resolution mobility data. Leveraging real-world trip traces, therefore, represents a key opportunity to better understand how emerging modes such as DBS integrate with public transport networks²³. Methodologically, explaining the determinants of potential intermodal DBS behaviour calls for regression frameworks capable of handling count outcomes and continuous mobility measures within a consistent comparative structure. Global specifications such as Poisson generalised linear model (GLM) and ordinary least squares (OLS) provide parsimonious and interpretable estimates of average associations between contextual factors and mobility outcomes, and have been widely applied in shared micromobility and transit integration research^24,25. Geographically weighted regression (GWR) extends this framework by allowing coefficients to vary across space²⁶, but its added value depends on whether spatial non-stationarity is pronounced enough to justify the additional complexity²⁷. When calibrated bandwidths approach the full sample size, local estimation converges toward the global solution, and the inferential gain from spatial weighting is negligible. Whether heterogeneity is substantial enough to warrant local modelling is therefore an empirical question, addressed here by comparing model fit across global and spatially varying specifications. Among the emerging literature on synergies between shared micromobility services and public transport networks, several studies have applied spatial regression approaches in large metropolitan contexts, including Beijing, Nanjing, Shanghai, and New York City^{24,28,29,30,31}. More recent contributions include multiscale GWR and geographically and temporally weighted regression, which incorporate multiple spatial or temporal scales^25,32. These approaches document substantial spatial heterogeneity in large Asian and North American cities, but it remains an open question whether comparable heterogeneity exists in compact European metropolitan contexts with more uniform transit coverage. Mobility demand for shared e-bikes further varies across time of day, and temporal segmentation allows the hierarchy of determinants to be examined separately across commuting and leisure contexts²⁵.

This study examines whether dockless bikesharing (DBS) functions as an access and egress mode to rail transit and thereby expands public transport accessibility. We analyse Bird shared e-bike operations in Lausanne, including the University of Lausanne campus. Using trip-level DBS data, we identify potential bike-and-ride segments connecting with rail stations through an ‘Inside Station Catchment Area’ (ISCA) proxy for intermodality.

The objectives of this paper are to (i) quantify the extent of potential DBS–public transport intermodality, (ii) describe its spatial and temporal patterns around rail stations, (iii) examine and assess these patterns using accessibility, built environment, land use, and sociodemographic factors, and (iv) discuss implications for integrated mobility and transit-oriented planning.

The primary contribution of this study is the development of a transferable analytical framework to detect, quantify, and explain potential integration between shared micromobility and rail transit. The DBS dataset and descriptive analyses serve to validate the framework empirically, while global GLM and OLS models estimated across temporal periods are used to explain the determinants of potential intermodal behaviour, with geographically weighted specifications serving as robustness checks to assess the degree of spatial non-stationarity.

Methods

The methodological framework is designed to support a single analytical objective: Identifying and characterising potential dockless e-bike integration with rail transit. Data sources, descriptive metrics, and modelling tools are therefore presented as complementary components of this unified pipeline. Given the limitations of shared micromobility datasets—namely, heterogeneous GPS accuracy, missing values, noisy distance records, and the absence of continuous trajectories—our approach combines reproducible data cleaning procedures, network-based routing, and complementary spatial modelling. The workflow begins with systematic validation of numeric, temporal, and geographic fields, followed by network-constrained route reconstruction and the detection of trips compatible with bike-and-ride behaviour. Bike-and-ride identification based on large-scale spatial data tends to overestimate actual intermodality relative to smart-card or trajectory-based methods. To address this limitation, we introduce the concept of ‘Inside Station Catchment Area’ (ISCA) trips, defined as DBS trips whose endpoints fall within station-accessible areas and occur during public transport operating hours. ISCA thus represents a proxy for potential intermodality rather than a direct observation of transfers. We first introduce our case study, then describe the data cleaning workflow and the reconstruction of network-constrained routes using OpenTripPlanner. We subsequently detail the spatiotemporal procedure used to extract ISCA trips. Finally, to investigate the influence of accessibility, built environment, and sociodemographic factors on shared e-bike usage, we estimate a set of regression models, including global specifications (GLM and OLS) and GWR models across temporal slices. The latter are used as a diagnostic tool to assess whether relationships exhibit meaningful spatial and temporal heterogeneity beyond global model assumptions, and to evaluate the added value of local modelling in this context.

Case study

The extent to which DBS services integrate with public transport depends on local governance, the spatial logic of deployment, and the availability of empirical data to observe usage at scale. The Lausanne–Morges agglomeration (PALM) provides a relevant setting: It combines a dense rail backbone with persistent short-distance car use, and has placed multimodality and station accessibility at the core of its strategic planning. In line with these objectives, Lausanne introduced its first dockless electric bikesharing (DBS) service in April 2024. Operated by Bird, the system runs 24/7 and relies on virtual parking zones. Since launch, coverage has expanded across most of the city and the University of Lausanne (UNIL) campus, offering an opportunity to examine whether DBS availability can support transfers to rail at the metropolitan scale. Within the canton of Vaud, the Lausanne–Morges metropolitan area has been structured since 2007 through the Lausanne–Morges Agglomeration Project (PALM), integrated into the Plan Directeur Cantonal (PDCn) under measure R11³³. Because DBS flows are primarily concentrated within Lausanne and the Dorigny campus (UNIL), we adopt a more granular perimeter and focus on four PALM sectors (schémas directeurs): SDCL, SDOL, SDEL, and SDNL, excluding Région Morges to ensure a more focused analysis. PALM accounts for 286,000 inhabitants and 210,000 jobs (37% of the cantonal population and 48% of cantonal employment)³³. Within our study area, a dense and integrated public transport network provides extensive regional and local coverage. The rail backbone is structured around the RER Vaud, operated by SBB CFF FFS since 1999, with Lausanne, Renens, and Prilly-Malley acting as major interchange hubs. At the local scale, the TL network complements rail services through two metro lines (M1, M2), trolleybus and bus lines, and the LEB railway line integrated into the TL system. The fare system is integrated under Mobilis, enabling seamless transfers across operators and modes. According to the Swiss Mobility and Transport Microcensus (MTMC 2021), private cars remain the dominant mode in Switzerland (69% of trips), while public transport accounts for 20%³⁴. Car ownership is widespread (78% of households own at least one car), and bicycle ownership is also substantial (61% of households)³⁴. Cycling trips, including bicycles and e-bikes, remain shorter than average daily travel distances, with marked regional differences across cantons. In Vaud, the public transport modal share remains lower than in leading Swiss cantons such as Basel-Stadt and Zurich (Fig. 1)^34,35.

Fig. 1: Bivariate map of cycling and public transport commuting shares in Switzerland.

The figure presents the joint distribution of cycling and public transport commuting modal shares across the 26 Swiss cantons, based on data from the 2021 Mobility and Transport Microcensus (MTMC). The upper choropleth map encodes two variables simultaneously using a bivariate colour scheme. The transit modal share dimension is represented in blue, with three classes: Below 18.0% (light blue), 18.0–24.0% (medium blue), and above 24.0% (dark blue). The cycling modal share dimension is represented in red, with three classes: Below 6.9% (light red), 6.9–9.1% (medium red), and above 9.1% (dark red). Combinations of high values on both dimensions appear in dark purple-grey. Cantons are labelled with their official two-letter abbreviations. The lower scatter plot positions each canton according to its cycling modal share (horizontal axis, 2.5–22.5%) and transit modal share (vertical axis, 10.0–45.0%), with each canton represented by a filled circle coloured according to the same bivariate scheme as the map. Notable patterns include Basle-City (BS) as an outlier combining the highest cycling share (above 20%) with a high transit share, while Vaud (VD) and Neuchâtel (NE) exhibit high transit shares alongside low cycling shares.

Mobility is a key climate challenge in Vaud: Transport accounts for a substantial share of cantonal greenhouse gas emissions³⁶. Despite a dense rail network, short car trips remain frequent in the Lausanne agglomeration³⁷, indicating that access and egress conditions still constrain public transport use. At the same time, Lausanne has a comparatively low motorisation rate³⁸, and multimodality is relatively common, with trips often involving more than one segment³⁹. The rapid uptake of e-bikes has further reshaped cycling dynamics and motivates renewed attention to bicycle-based station accessibility^37,40. To support modal shift, national and cantonal strategic documents stress the need to improve station accessibility and to facilitate intermodality between transit, walking, and cycling⁴¹. In Vaud, a large majority of residents and jobs are located within a few kilometres of a railway station, a range well suited to cycling as a feeder mode^42,43. Mobility and land use policies in Switzerland are increasingly shaped by binding climate objectives at national, cantonal, and municipal levels. Switzerland’s second Nationally Determined Contribution commits to reducing greenhouse gas emissions by at least 65% by 2035 relative to 1990 levels⁴⁴. This trajectory is reinforced by the Federal Act on Climate Protection, Innovation and Energy Security (2022), which sets sector-specific targets for transport, including a 57% reduction by 2040 and net-zero emissions by 2050⁴⁵. At the cantonal scale, the Perspectives Mobilité 2050 for the Canton of Vaud identifies only the most ambitious scenario (‘Sobriety’, S3) as compatible with long-term climate commitments, projecting a substantial increase in rail and cycling shares alongside a decline in car use⁴⁶. These orientations are embedded in the Plan Directeur Cantonal, which aims to reduce the modal share of private cars to 50% by 2050³³, and in the cantonal climate strategy. At the municipal level, Lausanne has committed to achieving zero direct mobility-related emissions by 2030⁴⁷. Cycling plays a central role in this transition. The Federal Act on Cycling Infrastructure, in force since 2023, requires all cantons and municipalities to develop and implement comprehensive cycling networks by 2042^45,48. In Vaud, the Plan Directeur Cantonal and the Plan Climat Vaudois target a tripling of cycling distance travelled and a modal share of at least 10% by 2035^33,49. Lausanne’s climate strategy further reinforces this ambition, aiming for a major expansion of cycling activity by 2030⁴⁷. Rail transport constitutes the backbone of Switzerland’s low-carbon mobility strategy. The Programme de développement stratégique de l’infrastructure ferroviaire (PRODES) plans a substantial increase in service frequency by 2035, with half-hourly or quarter-hourly services on most main lines⁵⁰. This expansion is integrated into the SBB CFF FFS 2030 Strategy, which explicitly emphasises seamless FLM access, notably through improved active mobility connections and bicycle parking around stations⁵¹. At the cantonal and metropolitan levels, rail-oriented strategies place strong emphasis on intermodality. The Stratégie ferroviaire vaudoise seeks to anchor rail at the core of daily mobility, supported by enhanced walking and cycling connectivity⁴³. National and regional planning documents similarly highlight the Lausanne–Geneva metropolitan area as a priority for strengthening active modes as feeder services to mass transit^37,41. Within this framework, intermodal interfaces and short-distance access to stations are identified as key levers for achieving modal shift^33,52. Together, these strategic orientations underscore the central role assigned to cycling, rail transport, and their integration within Swiss climate and mobility policies. In this context, shared micromobility services are increasingly viewed as promising instruments to address FLM constraints and to operationalise intermodality objectives in dense and topographically constrained urban regions such as Lausanne.

Implementation of a semi-floating e-bike service in Lausanne

The Bird shared e-bike service was launched in Lausanne on April 22, 2024, as a pilot project supported by the municipal administration^53,54,55. The initial fleet (100 e-bikes) expanded to 300 units by May 2024⁵³, and the service was extended to the UNIL campus in October 2024⁵⁶. Users can travel within the operational perimeter subject to parking rules and geofenced constraints (Fig. 2).

Fig. 2: Parked Bird DBS around two main multimodal hubs.

Pictures of parked Bird DBS around Gare de Lausanne and Gare Prilly-Malley multimodal hubs. The two photographs illustrate the physical presence of Bird dockless electric bikesharing (DBS) vehicles at two multimodal hubs in the Lausanne metropolitan area. A Two Bird electric bicycles are parked in front of one of the entrances of Gare de Lausanne railway station. B Several Bird electric bicycles are parked at Malley metro station, near Gare Prilly-Malley, representing a multimodal hub located in the western part of the city. Both photographs show the ‘semi-floating’ operational model of the Bird service, in which bicycles may be parked within designated zones.

Trips are made on app-unlocked, GPS-enabled electric-assist bicycles with geofenced parking. The operation follows a semi-floating model: Users can start and end trips without physical docks, but parking is restricted to predefined zones (Fig. 3), some of which require locking to designated racks⁵⁷. This hybrid configuration aims to combine the flexibility of dockless systems with improved management of public space and safety. The service complements the existing station-based system in the Lausanne–Morges area (PubliBike Velospot)^58,59. The geofencing scheme distinguishes functional zones (operation, mandatory parking, low-speed areas, and prohibited areas) to ensure orderly integration into the urban environment^56,58.

Fig. 3: Spatial distribution of DBS parking spots within the study area.

The figure presents the location of dockless bikesharing (DBS) designated parking spots across the main part of the Lausanne city area. The upper panel displays the spatial distribution of DBS parking spots across the Lausanne administrative area. Yellow-filled circles indicate individual DBS parking spots. The dark grey detached polygon to the left indicates the University of Lausanne (UNIL) campus of Dorigny. Parking spots are visibly concentrated in the central city and along major transit corridors, with sparser coverage in peripheral and residential areas. The lower-left inset shows the location of the Canton of Vaud (VD) within Switzerland, highlighted in light grey, providing national geographic context. The lower-right inset displays the Lausanne municipal and metropolitan areas in the Canton de Vaud.

Pricing follows a standard pay-per-use model (unlock fee and per-minute fare), complemented by subscription options and occasional promotions⁶⁰.

Dockless bikesharing data collection

The dataset analysed in this study originates from the Bird DBS system operating in Lausanne and the UNIL campus and was made available through a research collaboration between the University of Lausanne (HEC Lausanne) and the operator. Bird granted secure access to anonymised operational data extracted from its backend infrastructure. Data access, confidentiality, and use were governed by formal agreements, and data handling complied with applicable data protection requirements.

The dataset covers 459 consecutive days (April 22, 2024–July 24, 2025) and comprises 63,322 ride records (Supplementary Fig. 2). It includes anonymised user identifiers, trip metadata, and spatiotemporal information (Table 1). Each record corresponds to a completed ride and provides start and end coordinates (WGS84), start and end timestamps (ISO 8601), and a distance variable (cleaned_distance) produced after operator preprocessing. Descriptive exploration indicates 7364 unique users over the observation period, with heterogeneous usage intensity (median use markedly lower than the mean). The dataset provides sufficiently granular origin and destination information to support routing and network-based analyses, while the absence of continuous GPS trajectories limits analyses to start and end locations and inferred routes. This description motivates the analytical framework, which details the data cleaning, routing procedures, and the identification of segments potentially connected to public transport networks.

Table 1 Raw data description

Data cleaning workflow

All cleaning and validation steps were performed using reproducible Python workflows, combining trip-level exclusions with geo-enabled consistency checks. Ten sequential exclusion criteria were applied to remove incomplete, inconsistent, or implausible records (Supplementary Fig. 1).

Missing values (df_cleaning_01). We removed all records containing at least one missing field across identifiers, timestamps, coordinates, the city label, or the distance variable. This step eliminated 1385 trips (2.19%) and 44 users (0.60%), leaving 61,935 trips and 7320 users. Missingness was primarily concentrated in the distance-related timestamp ride_completed_at. Duplicate trip identifiers (df_cleaning_02). To preserve a strict one-to-one mapping between rows and trips, we screened the dataset for repeated ride_id values. No duplicates were detected, leaving counts unchanged at 61,935 trips and 7320 users. Ensuring identifier uniqueness safeguards valid aggregation and supports reliable standard-error estimation. Date-format validation (df_cleaning_03). We checked that all timestamp fields—dt, ride_started_at, and ride_completed_at—were well-formed and parsable, adhering to ISO-compliant formats and valid calendar values. No records required removal, confirming consistent timestamp formatting. Counts therefore remained 61,935 trips and 7320 users. Temporal consistency (df_cleaning_04). We assessed each trip for plausibility by verifying that timestamps fell within the study window (April 22, 2024 to July 24, 2025) and that trip durations were neither unrealistically short (below a minimal operational threshold) nor excessively long (beyond a conservative upper bound). No additional records were removed at this stage. Textual geographic scope (df_cleaning_05). We restricted the dataset to trips whose start_city field was tagged ‘Lausanne’. This step removed 214 trips (0.35%) and 103 users (1.41%), resulting in 61,721 trips and 7217 users. Coordinate validity (df_cleaning_06). We validated the start and end coordinates for numerical range and formatting, ensuring that longitudes fell within [−180, 180] and latitudes within [−90, 90], and that all coordinate pairs in start_longitude, end_longitude, start_latitude, and end_latitude (WGS84) were non-zero. No records were removed at this stage, indicating full syntactic validity of the geospatial fields. Counts therefore remained 61,721 trips and 7217 users. Identical origin and destination (df_cleaning_07). We excluded trips whose departure and arrival coordinates were exactly identical, as these records most likely reflect logging artefacts or negligible repositioning events. Removing these cases avoids inflating near-zero distances. This step removed 184 trips (0.30%) and 15 users (0.21%), leaving 61,537 trips and 7202 users. After these seven steps, the trip-level data frame can be confidently converted into a geospatial object suitable for routing. This allows the cleaned dataset to be spatialized (Supplementary Fig. 1) and provides a solid basis for generating network-constrained itineraries for all subsequent modelling stages.

ErroneousOpenTripPlannerbicycle routes (gdf_cleaning_08). We cross-checked all trips against OpenTripPlanner (OTP) routing results and removed those flagged as implausible, such as clearly infeasible. This step excluded 16 trips (0.03%) and 3 users (0.04%). Additional details on the routing procedure are provided in the subsection ‘Network-constrained route computation using OpenTripPlanner’ (p. 7). Coordinate and location consistency (gdf_cleaning_09). We performed a second check to ensure internal consistency across all geospatial fields and verified that each trip was indeed located within the Lausanne agglomeration boundary, even when start_city was labelled ‘Lausanne’. No additional removals were required, leaving 61,521 trips and 7199 users.

Distance outliers (gdf_cleaning_10). We removed rides shorter than 100 m and rides at or above 10,000 m to discard probable GPS noise, stationary events, or mislogged long-distance trips falling outside the system’s operational profile^61,62. This threshold-based step excluded 1992 trips (3.24%) and 221 users (3.07%). The resulting cleaned analysis dataset, gdf_cleaned_df, contains 59,529 trips and 6978 users, corresponding to net reductions of 5.99% and 5.24%.

Network-constrained route computation using OpenTripPlanner

To obtain realistic distances and travel times, we reconstructed cycling routes between recorded origins and destinations using OpenTripPlanner (OTP). Service-reported distances and durations often reflect GPS imprecision and include non-riding components such as unlocking or parking time. OTP routing allows us to generate geometrically valid shortest-path itineraries constrained by the actual street network, providing a consistent basis for subsequent analyses. OTP was deployed locally and configured exclusively in BICYCLE mode using an OpenStreetMap (OSM) extract of Switzerland. Elevation data were deliberately excluded, as slope effects are largely mitigated by electric assistance in shared e-bike systems. Each trip was routed by submitting origin and destination coordinates to the OTP API. Valid routes were stored as LineString geometries along with routed distance and travel time attributes (Fig. 4). Trips for which routing failed were removed at this stage.

Fig. 4: Flow map of all cleaned DBS segments across the metropolitan area.

Flow map of all cleaned DBS trips and access and egress segments across the Lausanne metropolitan area. The figure displays network-constrained flow maps of routed dockless bikesharing (DBS) trips across the Lausanne metropolitan area, with routes aggregated onto road network edges using 10-m buffers and coloured using a linear scale according to the level of trip aggregation, ranging from dark purple (low aggregation, minimum: 1 trip) through red to light yellow (high aggregation, maximum: 5094 trips). The dark polygon delineates the Lausanne city boundary. A The upper panel shows the full cleaned DBS sample of 59,529 trips, routed on a graph of 11,991,485 points and 55,123 edges. The accompanying circular inset provides a zoomed view within a 3-km buffer around the urban centre station. B The lower panel shows the Inside Station Catchment Areas (ISCA) subsample of 16,335 first-mile and last-mile trips, routed on a graph of 3,280,836 points and 40,479 edges. The accompanying circular inset similarly zooms in on the city centre.

OTP-derived distances and durations were compared with service-reported values. Routed distances were on average 9.05% shorter than reported distances, while routed travel times differed by 1.58%. These discrepancies reflect known GPS and reporting artefacts and confirm that OTP provides a reliable approximation of network-constrained travel behaviour.

Extraction and detection of ‘Inside Station Catchment Areas’ segments

From the cleaned dataset, we identified trips potentially connected to public transport using a three-step procedure combining spatial, origin–destination, and temporal criteria (Fig. 5).

Fig. 5: Spatiotemporal detection of ‘Inside Station Catchment Areas’ DBS segments (ISCA) within the dataset.

The diagram illustrates the three-stage filtering pipeline applied to the cleaned dockless bikesharing (DBS) database to identify trips classified as Inside Station Catchment Areas (ISCA) segments. The input database (gdf_cleaned_df) contained 59,529 trips made by 6978 users. Detection 1 applied a spatial filter retaining only trips whose origin or destination fell within the merged 100-m station catchment areas, reducing the dataset to 17,253 trips and 3096 users, corresponding to a removal of 42,227 trips (71.00%) and 3882 users (55.63%). Detection 2 applied an origin–destination matrix filter to ensure that retained trips exhibited a valid intermodal origin–destination pair, resulting in no additional data loss. Detection 3 applied a temporal filter restricting trips to the operational window of train and metro services, removing a further 918 trips (5.32%) and 63 users (2.03%). The final ISCA database (gdf_cleaned_isca_df) comprised 16,335 trips made by 3033 potentially intermodal users, representing an overall reduction of 43,194 trips (72.56%) and 3945 users (56.55%) relative to the cleaned database.

Identifying trips occurring inside influence areas for DBS rental (gdf_isca_01). The first and most decisive exclusion step consisted of defining pedestrian catchment areas around each station entrance and exit, thereby delimiting the ‘socially acceptable’ area within which users are willing to walk to access a DBS. This definition relies on a rule-based spatial threshold to identify trips compatible with rail access or egress, operationalised here using buffers of 100 m or 1 min. These spatiotemporal thresholds are consistent with the radius-based sensitivity analysis conducted by Ju et al.⁶³ across four Californian cities, as well as with other empirical studies employing similar parameters³². Our work also builds on the study by Rieder²², which uses a 60-m walking radius around public transport stations to capture dockless bike and e-scooter trips in Zurich, a threshold they identify as the most commonly applied in the literature.

To ensure that this choice does not drive the results, we assess the sensitivity of key ISCA indicators to alternative cutoffs between 50 and 200 m. Figure 6 reports the relative position of several ISCA indicators with respect to DBS benchmarks across thresholds, including peak-hour incidence, proximity to multimodal hubs, and the share of trips below a 2.4 km reference distance (see the subsection ‘Spatial and temporal distances’, p. 16, for the determination of this threshold). Across this range, these indicators vary only moderately. An auxiliary marginal analysis further shows that trips added when expanding the catchment area beyond this threshold progressively move closer to DBS reference values. We therefore retain this value as the reference threshold in the remainder of the analysis.

Fig. 6: Conditional stability of ISCA identification.

The four panels display the sensitivity of key Inside Station Catchment Areas (ISCA) characteristics to variations in the spatial proximity threshold, ranging from 50 to 200 m. The reference threshold used in the analysis is 100 m. ISCA modal share (%): The proportion of the cleaned DBS sample classified as ISCA trips increases monotonically with the threshold, from 16.29% at 50 m to 49.25% at 200 m, reflecting the mechanical expansion of the catchment area perimeter. ISCA in multimodal hubs (%): the share of ISCA trips associated with multimodal hubs decreases as the threshold increases, from 76.25% at 50 m to 58.59% at 200 m, suggesting that hub-proximate trips are spatially concentrated close to station entries. ISCA during peak hours (%): The share of ISCA trips occurring during peak hours remains stable across all thresholds, ranging narrowly between 42.65 and 43.90%, indicating that the temporal composition of detected intermodal trips is robust to the choice of spatial threshold. ISCA below 2413 m (%): The share of ISCA trips with a routed distance below the 2413-m reference value remains stable across thresholds, varying between 84.07 and 87.01%, confirming that the distance distribution of detected trips is insensitive to moderate changes in the spatial criterion. In all panels, the horizontal axis represents the spatial threshold in metres and the vertical axis represents the share in percentage. Annotated values indicate the exact share at each threshold.

As an original contribution of this paper, we further generated isochrones of 1 min walking distance to better reflect the urban reality shaped by physical barriers and potential detours. The resulting isochrones were then merged for each public transport stop. Among the 119,058 origin and destination points projected within the Lausanne metropolitan area, and given the precision limitations inherent to GPS-based data, we complemented the initial spatial detection (gdf_isca_01) with a spatial point-clustering procedure. The objective was to account for spatial groupings of points in order to compensate for GPS inaccuracies and thus introduce greater spatial tolerance into the detection process. To this end, we applied Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), an unsupervised clustering algorithm grounded in point density and hierarchical structure, which automatically identifies the most stable e-bike clusters^64,65. This step yielded 789 clusters of origin and destination points, which were subsequently overlaid with the merged entrance–exit isochrones of each public transport node (Fig. 7).

Fig. 7: Spatial clustering of DBS trips within Gare de Lausanne, Lausanne-Flon, and Grancy 100-m Station Isochrones.

The map displays the spatial distribution of dockless bikesharing (DBS) origin or destination points around three transit stations in central Lausanne: Gare de Lausanne-Flon (top, multimodal hub), Gare de Lausanne (centre, multimodal hub), and Grancy (bottom, metro station). Purple-filled circles indicate trips classified as Inside Station Catchment Areas (ISCA) trips, whose points fall within the merged station catchment area. Orange-filled circles indicate trips classified as Outside Station Catchment Areas (OSCA) trips, which do not meet the spatial proximity criterion. Blue-filled polygons represent merged isochrones of 100 m around each station entry and exit, derived from the pedestrian network. Blue open circles represent merged circular buffers of 100 m. Black-filled circles indicate individual station entry points. Transit infrastructure visible on the map includes Metro Line 2 (M2), Metro Line 1 (M1), and the Lausanne–Échallens–Bercher regional railway (LEB).

From the multiple isochrones generated for each of the 43 stations under study, we retained only those trip origins and destinations located within their boundaries. This spatial exclusion step eliminated 42,277 DBS trips and 3882 unique users, leaving a total of 17,253 trips and 3096 users (Fig. 5).

Adopting a trip-chain logic (gdf_isca_02). The second criterion used to detect ISCA segments rests on the exclusion of trips that, although spatially located within clusters intersecting the walk-accessible isochrones around rail stations, have both their origin and destination falling within the same observation. In other words, when the start and end points of a shared e-bike trip lie within the same isochrone or within two different isochrones, the trip is excluded as it is highly likely to be non-intermodal and instead to substitute for a metro or train journey. This exclusion criterion did not indicate any such trip in our dataset (Fig. 8). Nevertheless, to the authors’ knowledge, this logic has not been applied in previous research and may prove relevant when implemented on larger datasets. Identifying trips occurring during transit schedules (gdf_isca_03). A further innovative aspect of this potential bike-and-ride trip extraction method consisted of excluding trips that took place outside the operating hours of the public transport system. For this purpose, we examined the Swiss GTFS data (July 21, 2025), retaining only trips occurring between the first morning departure or arrival and the last evening service. In general, this resulted in a continuous service window between 4 AM and 1 AM. By applying this procedure to all 43 stations, we excluded 918 trips that, although spatially connected to station catchment areas, occurred outside the train and metro service window, an exclusion proportion almost identical to that reported by Ju, Martin, and Shaheen⁶³. This corresponded to 63 fewer users attributable to the temporal criterion (Fig. 5). This final filtering step refines the spatiotemporal detection of ‘Inside Station Catchment Areas’ (ISCA) trips, ultimately yielding a subsample of 16,335 trips made by 3033 potentially intermodal users (Fig. 4B).

Fig. 8: Advancing bike-and-ride detection through ISCA spatiotemporal filtering.

The figure contrasts the conventional bike-and-ride spatial detection approach with the proposed Inside Station Catchment Areas (ISCA) spatiotemporal detection method through two schematic diagrams. The upper panel illustrates the existing bike-and-ride detection approach, which relies solely on a circular buffer of 100 m around a station centroid (large light blue filled circle). DBS points falling within the buffer are indiscriminately classified as intermodal regardless of the actual pedestrian network geometry, transit service availability, or origin–destination logic, resulting in the inclusion of spatially proximate but functionally unrelated trips. The lower panel illustrates our own ISCA spatiotemporal detection method, which combines three successive filters applied within a 100-m perimeter. First, a spatial filter restricts eligible trip points to areas covered by network-derived isochrones (star-shaped light blue filled polygon), which reflect true pedestrian accessibility from station entries rather than a uniform radial buffer (light blue open circle). Second, an origin–destination matrix filter excludes trips whose OD pair does not conform to an intermodal logic. Third, a transit service consideration filter removes trips occurring outside the operational hours of train and metro services. Spatial clusters of retained trip endpoints are indicated by purple open circles. Purple-filled circles indicate ISCA trips, while orange-filled circles indicate OSCA trips.

Regression models

To address our final research objective, namely, identifying and quantifying the influence of accessibility, built environment, and sociodemographic factors on shared e-bike usage, we estimate a set of regression models on DBS trips classified as ISCA and OSCA (‘Outside Station Catchment Areas’). These include global specifications (GLM and OLS) as well as GWR models estimated across temporal slices, the latter used as a diagnostic tool to assess potential spatial and temporal heterogeneity. The following subsections present the selection of dependent and independent variables, detail the model specification and diagnostic tests, and finally compare the performance of global and local models.

Three dependent variables are considered, capturing complementary dimensions of shared e-bike usage: Usage intensity (Y1), defined as the number of trips per spatial unit, travel distance (Y2), and travel time (Y3). All variables are aggregated on a regular hexagonal grid (hex100) and standardised prior to estimation (Table 2).

Table 2 Specification of dependent variables Y

Explanatory variables are grouped into three analytical dimensions—accessibility, land use, and sociodemographic characteristics (Table 3)—reflecting complementary determinants of spatial behaviour. Each dimension integrates several thematic categories derived from official statistics and open geodata, allowing us to examine how spatial accessibility, functional land use, and social or material resources are associated with Bird mobility behaviours:

• Accessibility (D1), capturing the physical and network-based potential for movement within and towards multimodal nodes, including proximity to stations, road and cycling infrastructure, parking availability, and multimodal services;

• Land use (D2), representing the functional and morphological characteristics of urban space that influence trip generation, destination attractiveness, and activity density;

• Mobility tools and sociodemographic patterns (D3), summarising resident resources, mobility equipment, travel pass ownership, and household attributes that condition modal choice and trip frequency.

Table 3 Independent variables and associated spatial layers used in the regression models (hex100 and constant across τ)

This variable framework builds on existing research, both regarding the components associated with these three analytical dimensions and mobility behaviour more broadly. For instance, Thao and Ohnmacht⁶⁶ draw on Swiss public surveys (STATPOP and MTMC) to examine how densities, POIs, transit accessibility, mobility tools, and sociodemographic attributes influence trip frequency and daily distance travelled across modes. Similarly, Beza, Demissie, and Kattan⁶⁷ investigate the spatiotemporal influence of integrating shared micromobility into first- and last-mile public transport journeys. Their regression model incorporates variables related to cycling infrastructure, the built environment, employment, POIs, and social groups. By applying GWR to explore the links between DBS and public transport, Li, Shang, Zhao, and Yang²⁵ rely on the well-established ‘5Ds’, which are also reflected in our indicators. By contrast, Zhang, Cui, Liu, Jia, Shi, and Yu³² extend this framework by integrating additional variables, such as housing prices or metro station passenger volumes, which are not available in our context.

Before estimating the regression models, we performed diagnostic checks to ensure the stability and interpretability of the coefficients. All predictors were standardised to z-scores to facilitate comparability and avoid scale-dependent effects. We then screened the predictor set for redundant information using pairwise Pearson and Spearman correlations.

Only one pair—distance to Lausanne-Flon (X1_B) and to Lausanne-Gare (X1_C)—exceeded ∣r∣ > 0.90, reflecting their close geographic proximity (Fig. 9); these were merged into a single measure of central-station accessibility. Variance Inflation Factors (VIFs) were subsequently computed, with all values remaining well below conventional thresholds (max VIF = 2.65), indicating negligible multicollinearity. These diagnostics confirm that the final predictor set is well-conditioned for regression analysis.

Fig. 9: Pearson Correlation Matrix among Independent Indicators.

The matrix displays pairwise Pearson correlation coefficients (r) between all 33 independent indicators considered in the regression models, grouped into three thematic domains along both axes: Accessibility (D1), Land Use (D2), and Social Factors (D3). Each cell is colour-coded according to the correlation value, ranging from dark blue (strong negative correlation, r = −1.00) through white (no correlation, r = 0.00) to dark red (strong positive correlation, r = 1.00). Indicators retained for inclusion in the models are displayed with coloured cells. Indicators excluded due to multicollinearity are displayed with greyed-out cells. The matrix is based on 442 valid pairs, with a mean absolute correlation of $| \bar{r}| =0.03$, indicating overall low collinearity among retained predictors. Accessibility indicators (D1) include distance to nearest station entrance, distance to Lausanne-Flon, distance to Gare de Lausanne, cycling network density, intersection density, slope, bicycle parking, car parking, bus stops, and public bikesharing stations. Land use indicators (D2) include population, sector 1–3 jobs, local, intermediate, and superior points of interest, and land use classes including residential, public services, commercial, industrial, farmland, and green space. Social factor indicators (D3) include driving licence ownership, motorisation rate, bicycle ownership, e-bike ownership, transit pass, public bikesharing pass, age, household size, and income (X12).

On this basis, we estimate a set of global regression models to quantify the relationships between explanatory variables and shared e-bike usage. For count outcomes (Y1), we estimate a Poisson GLM, while for continuous outcomes (Y2 and Y3), we estimate global OLS models.

To assess whether these relationships vary across space and time, we complement these global specifications with GWR models estimated across temporal slices. GWR is used here as a diagnostic tool to evaluate the extent of spatial and temporal non-stationarity relative to global models.

Temporal-slice GWR estimation relies on spatial and temporal bandwidths controlling the influence of neighbouring observations. Smaller bandwidths enhance local sensitivity, while larger values yield smoother coefficient surfaces. Model calibration is performed separately for five time periods corresponding to daily peaks, off-peak intervals, and weekends.

Based on the general form of a basic GWR model^68,69, we define temporal-slice GWR as follows (Formula (1)):

$${y}_{i}={\beta }_{0}({u}_{i},{v}_{i},{\tau }_{i})+{\sum }_{k}{\beta }_{k}({u}_{i},{v}_{i},{\tau }_{i}){x}_{ik}+{\varepsilon }_{i}.$$

(1)

where:

• i is the index of an observation located at (u_i, v_i) and time τ_i;

• y_i is the dependent variable at observation i;

• x_ik is the value of the kth independent variable for observation i;

• β₀(u_i, v_i, τ_i) is the local intercept at (u_i, v_i, τ_i);

• β_k(u_i, v_i, τ_i) is the local coefficient of the kth predictor at (u_i, v_i, τ_i);

• ε_i is the random error term.

The temporal dimension is discretized into five periods τ ∈ τ₁, …, τ₅ corresponding to morning and evening peaks, midday, night, and weekend intervals (Table 4). Bandwidths are selected through cross-validation by minimising the corrected Akaike Information Criterion (AICc) (Supplementary Fig. 3).

Table 4 Definition of periods τ

We evaluated the performance of global and spatially varying specifications across the three behavioural outcomes (Y1–Y3) and five temporal periods (τ₁–τ₅), following Hassam, Alpalhão, and Neto⁷⁰ (Table 5).

Table 5 Model performance comparison between global and local regression models per τ (Y1–Y3)

For Y1, GWR bandwidths ranged from 70 to 100% of active hexagons across periods, indicating convergence toward the global GLM. For Y2 and Y3, bandwidths covered 60–93% and 68–84% of observations, respectively. Although apparent R² gains are observed (ΔR² ≈ 0.22), the magnitude of these bandwidths indicates that improvements reflect broad spatial smoothing rather than locally concentrated heterogeneity.

Temporal comparisons indicate moderate variation in model fit across periods, with slightly higher fit during peak periods for Y1, and during off-peak periods for Y2 and Y3. Residual diagnostics further indicate modest reductions in spatial autocorrelation between global and GWR specifications (Supplementary Fig. 3).

Taken together, these results indicate that spatial non-stationarity remains limited in this context. Global GLM and OLS specifications are therefore retained as the primary analytical framework, while GWR results are reported in the Supplementary Information as a diagnostic and sensitivity analysis (Supplementary Tables 4–6).

Results

This section reports empirical findings on the potential integration of DBS with public transport in the Lausanne metropolitan area. Results are presented following the logic of the proposed framework. Descriptive analyses first establish the magnitude and plausibility of potential intermodal behaviour, accessibility indicators then quantify territorial effects, and regression analyses finally examine the determinants of different dimensions of shared e-bike usage. Throughout, results rely on consistent metrics to enable direct comparisons between ‘Inside Station Catchment Areas’ (ISCA) and ‘Outside Station Catchment Areas’ (OSCA) trips. This subsection establishes the empirical baseline for potential DBS–transit integration by characterising observed mobility behaviours within the ISCA subsample. We compare ISCA and OSCA trips in terms of counts, distance, and travel time, then disaggregate ISCA segments. Access and egress legs are analysed separately to assess their respective contributions, and spatiotemporal effort distributions are used to identify typical transfer profiles.

Overall ISCA modal split

ISCA trips involving rail account for 27.44% of all recorded DBS trips. Given a 10% margin of error, reflecting the assumption in the literature that around 90% of DBS stops near stations correspond to public transport access⁶³, it is therefore reasonable to infer that approximately 24.70% of DBS riders in Lausanne start or end their trip close to a rail station. When weighted by travel distance, the ISCA share reaches 26.41% in the cleaned database. For travel time, ISCA trips represent 26.14% of total observed duration (Table 7). Disaggregating by public transport network type, hubs capture the largest share of ISCA itineraries. The four multimodal hubs account for 67.25% of all ISCA trips and 18.45% of total DBS trips (Table 6). Gare de Lausanne alone concentrates 45.10% of ISCA transfers, corresponding to 12.38% of the full DBS sample.

Table 6 Distribution of transit types and main stations relative to the ISCA and OSCA samples

At the user level, 43.47% of individuals undertook at least one ISCA trip. When weighting users by their number of trips, the intermodal share decreases to 25.92%, suggesting that a smaller group of frequent travellers accounts for a disproportionate share of potential bike-and-ride movements. Among ISCA users, ISCA trips represent, on average, 59.64% of their personal activity, indicating that once adopted, potential bike-and-ride becomes a central and recurring component of individual mobility patterns (Table 7).

Table 7 Bike-and-ride shares according to different definitions and weighting approaches

The distribution of ISCA trips between access and egress segments is balanced (Supplementary Fig. 4). Access segments account for 51.45% of potential transfer trips, while egress segments represent 48.55%. When weighted by distance, access shares reach 51.70% and egress 48.30% (Table 7). To capture combined behaviours, we identified ISCA users who relied on the service both to reach and to leave a station on the same day (paired access–egress segments). In total, 345 users engaged at least once in this symmetrical modal combination, yielding 874 paired trips. This corresponds to 11.1% of ISCA users and 5.1% of trips in the potential bike-and-ride subsample. Notably, 36.7% of these pairs occurred during peak hours, suggesting that they mainly correspond to commuting journeys.

Temporal variations and trip purposes

We next characterise the temporal structure of DBS–transit integration by computing ISCA modal shares by day of week and hour of day. ISCA segments display a moderately commuter-oriented profile (Fig. 10). On weekdays, the ISCA share reaches 28.2%, compared with 25.5% on weekends. Over the diurnal cycle, ISCA behaviour shows a marked midday trough (around 25% between 10 AM and 3 PM), consistent with a higher prevalence of short, all-bike trips around lunchtime. Early-morning and late-evening shares are comparatively higher (around 31% at 05–06 AM and 10–11 PM), although low volumes warrant cautious interpretation (Fig. 11). In volume terms, ISCA activity is most concentrated between 3 PM and 6 PM (Supplementary Fig. 4).

Fig. 10: Hourly and weekly variations in access and egress DBS transfers.

Two polar charts display the temporal distribution of dockless bikesharing (DBS) classified as Inside Station Catchment Areas (ISCA) trips, with sector colour indicating the ISCA share, ranging from light yellow (low share, approximately 20%) to dark purple (high share, approximately 40%). A The weekly radar chart displays one sector per day of the week, with the ISCA share annotated for each day. Weekdays exhibit higher ISCA shares, peaking on Wednesday (29.2%) and Thursday (28.2%), while Sunday records the lowest share (23.6%), consistent with more leisure-oriented travel on weekends. Saturday (27.0%) and Monday (27.0%) show intermediate values. B The hourly radar chart displays one sector per hour of the day from 04:00 to 12:00 midnight. ISCA shares are at their highest during the early morning hours (04:00–06:00 AM, reaching 32.9 and 31.2% respectively) and remain moderately elevated during the evening hours (09:00–10:00 PM, 31.8–32.2%). Mid-morning and early afternoon hours (10:00 AM–02:00 PM) record the lowest intermodal shares, between 22.4 and 25.9%. Sector radius reflects the total sum of transfer trips per day (panel a, scale 0–3000) and per hour (panel b, scale 0–1500).

Fig. 11: Temporal asymmetry between access and egress DBS segments.

For each panel, the horizontal axis represents the hour of the day and the vertical axis represents the share (%) of ISCA trips. Across weekdays, access trips dominate the AM period, reflecting outbound commuters cycling to transit stations, while egress trips gain share throughout the PM period, consistent with return commuting. Weekend panels exhibit a more balanced and less temporally structured distribution between potential access and egress shares. The light blue line indicates access (first-mile) ISCA trips, the purple line indicates egress (last-mile) ISCA trips, and filled circles mark hourly observed values.

The temporal distribution of access and egress segments mirrors home-based commuting rhythms. For access trips, 20.7% occur during the morning peak and 23.9% during the evening peak. Egress trips account for 10.7% of morning-peak activity but 29.7% of evening-peak activity. Nearly half of all ISCA trips thus take place during peak hours, indicating that potential intermodal cycling is primarily embedded within commuting routines, with access trips supporting the morning rush and egress trips disproportionately shaping evening returns.

Spatial and temporal distances

These temporal variations frame the analysis of the spatial and temporal extent of ISCA transfers. The 16,335 potential intermodal segments are strongly concentrated around short cycling distances and durations. Access and egress legs display a nearly identical median length of around 1.4 km, corresponding to roughly 7 min of travel (Table 8). Median distance–time pairs correspond to cycling speeds of 11–13 km/h, consistent with typical observed cycling speeds. Access and egress segments share the same median distance (Fig. 12), but access trips exhibit a larger standard deviation, indicating greater variability and a non-negligible share of longer access segments. Egress trips appear more homogeneous, with fewer extreme values.

Fig. 12: Violin plots of access and egress distances travelled by intermodal riders using DBS.

The figure displays mirrored violin plots of routed Inside Station Catchment Areas (ISCA) trip distances (in metres, bottom axis and in kilometres, top axis) for access (first-mile, upper half, blue) and egress (last-mile, lower half, purple) segments, stratified by transit node type across four columns: Total (16,335 trips), Multimodal Hubs (10,985 trips), Metro Stations (4,683 trips), and Train Stations (667 trips). Violin widths are scaled relative to the total column (scale factors: Total: 1.00, Multimodal Hubs: 0.43, Metro Stations: 0.43, Train Stations: 0.06). Within each violin, horizontal lines indicate the first quartile (Q1), the median (Q2), and the third quartile (Q3). Across all node types, access and egress distance distributions are broadly similar, with access segments accounting for 51.5% and egress for 48.5% of total ISCA trips. Multimodal hubs concentrate the largest share of trips (67.2%), with a near-equal access–egress split (51.7–48.3%). Train stations show a higher egress share (59.4%). Metro stations show a slight access dominance (52.3–47.7%).

Table 8 Descriptive statistics of access and egress transfer trips in spatial distance and duration

We relied on a commonly used threshold to measure the distance considered ‘socially acceptable’ for transfer segments³⁰: The 85th percentile of the cumulative distribution of cycling distances¹⁰. In integrated bicycle use, this percentile is commonly interpreted as an acceptability benchmark for access to public transport^{71,72,73,74,75}. Here, the 85th percentile corresponds to 2.4 km or 12 min for FLM connections, delineating the empirical influence area of station neighbourhoods¹⁰. Given that the acceptable walking distance in the canton of Vaud is estimated at 1.0 km from MTMC 2015 analyses³⁹, station areas extended by DBS can be considered theoretically 5.8 times larger in surface area than those reachable on foot, corresponding to an absolute difference of 1500 ha (15 km²) per buffer. Considering origin–destination pairs, their potential combinations expand multiplicatively, and extended station catchments theoretically enable users to reach up to 33.2 times more locations. However, theoretical coverage is not sufficient to capture the capacity of DBS to expand station catchments and systemic accessibility. The next subsection, therefore, examines regional accessibility gains in terms of populations and destinations that become reachable through these services^76,77,78,79. This subsection quantifies territorial accessibility gains produced by integrating DBS with the public transport network. Using empirically derived spatial and temporal thresholds, it assesses the extent to which DBS enlarges station catchment areas beyond walking isochrones in terms of surface coverage, network accessibility, population and employment coverage, and destination accessibility.

Expanded spatial coverage and accessibility gains

We assess the effective surface area reachable through the spatial extension of station catchments enabled by DBS, assuming the service operates across the entire study perimeter (132.6 km²). One-km walking isochrones cover 17.3% of the metropolitan area (22.8 km²). With DBS, coverage expands to 44.2% (58.1 km²), representing a 2.6-fold increase in catchment area. Street-network accessibility shows a similar pattern (Fig. 13A). In the walk-and-ride scenario, 54.5% of street segments fall within station catchment areas, representing 41.2% of total network length. With DBS, coverage rises to 84.4% of segments and 73.1% of total length, equivalent to 1.5 times more accessible segments and 1.8 times greater accessible network length.

Fig. 13: Comparative accessibility analysis of walking and cycling catchment areas around railway stations.

The figure presents a 2 × 2 matrix of network-constrained accessibility maps across the Lausanne metropolitan area, comparing walk-and-ride (top row) and bike-and-ride (bottom row) isochrones, and contrasting accessible road network segments (column A) against accessible buildings (column B). All maps are derived from 960 station entry isochrones computed using OpenTripPlanner (OTP). A Accessible road network under walk-and-ride conditions (1000-m isochrones): 22,382 segments totalling 1221 km are reachable from transit stations on foot, out of a full metropolitan sample of 41,088 segments (2964 km). Accessible road network under bike-and-ride conditions (2400-m isochrones): 434,668 segments totalling 2168 km are reachable by bicycle from transit stations. B Accessible buildings under walk-and-ride conditions (1000-m isochrones): 14,745 buildings covering 4.079 km² are reachable on foot, out of a full sample of 35,006 buildings (9.995 km²). Accessible buildings under bike-and-ride conditions (2400-m isochrones): 26,589 buildings covering 8.031 km² are reachable by bicycle. In all four panels, road segments and buildings are coloured according to the level of pedestrian or cycling accessibility, using a sequential colour scale from light yellow (low accessibility, value: 0) through orange and red to dark purple.

Combining isochrones with built environment data (Fig. 13B) shows that walking-only conditions place 42.1% of buildings, accounting for 40.8% of total gross floor area (GFA), within accessible zones. Including DBS raises these shares to 76.0% of buildings and 80.4% of GFA, corresponding to 1.8 times more accessible buildings and a twofold increase in accessible floor space. In terms of accessibility by population (BBTOT), walking isochrones already include 69.8% of metropolitan residents (n = 213,766). Extending access through DBS raises potential coverage to 93.0% (n = 284,853), a 1.3-fold increase. Population densities remain higher in walking isochrones (n = 5839 inhabitants/km²) than in DBS-accessible areas (n = 3989 inhabitants/km²), indicating that expanded catchments are on average 1.5 times less dense. Accessibility gains must also be interpreted through access to employment and activity locations, structuring daily mobility⁸⁰. For total employment (B08EMPT), extending station catchments through DBS increases accessible jobs by a factor of 1.3: Walking isochrones cover 73.9% (n = 171,947), whereas cycling isochrones reach 95.1% (n = 221,103). By sector, accessibility gains range from 1.25 to 1.77. Primary sector coverage (B08EMPTS1) increases from 41.5% (n = 308 jobs) to 65.0% (n = 483 jobs). Secondary sector coverage (B08EMPTS2) rises from 51.5% (n = 12,984 jobs) to 90.9% (n = 22,931 jobs). Tertiary sector coverage (B08EMPTS3) improves from 76.7% (n = 159,820 jobs) to 95.7% (n = 199,350 jobs). Beyond employment, we assess destination opportunities using POIs (osm_pois). Walking isochrones provide access to 74.7% of POIs (n = 1872), whereas integrating DBS raises this share to 94.8% (n = 2376), a 30-percentage-point improvement with similar relative gains across categories. Disaggregating by POI category (fclass) shows that DBS makes approximately 1.3 times more POIs of every type accessible. For ‘regional’ POIs, 100.0% (n = 24) are reached, compared with 83.3% (n = 20) under walking conditions. For ‘intermediate’ POIs, coverage increases from 72.2% (n = 179) to 95.2% (n = 236). For ‘local’ POIs, 94.7% (n = 2116) become accessible, compared with 74.9% (n = 1673) in the walk-and-ride case. This final subsection examines how ISCA and OSCA behaviours relate to their underlying determinants through global regression models. Whereas previous sections characterised the magnitude and territorial reach of potential DBS–rail integration, this analysis identifies the accessibility, built environment, land use, and sociodemographic factors associated with different dimensions of shared e-bike behaviour at the metropolitan scale.

Table 9 reports statistically significant standardised coefficients from the global GLM (Y1) and OLS (Y2 and Y3) models, estimated separately for ISCA and OSCA trips. Results reveal both common structural determinants and notable differences between the two groups, with several variables exhibiting contrasting signs or magnitudes, suggesting that factors associated with potential intermodal behaviour differ from those shaping unimodal DBS use.

Table 9 Significant standardised coefficients ($\widehat{\beta }$) from GLM (Y1) and OLS (Y2, Y3) models, ISCA vs OSCA (p < 0.05)

Determinants of usage intensity

For usage intensity (Y1), the most pronounced effects are observed among accessibility variables, followed by land use and sociodemographic factors, with systematic differences between ISCA and OSCA behaviours.

Among accessibility indicators, the presence of PBS bikesharing stations (X4_B) is one of the strongest positive predictors of ISCA intensity (${\widehat{\beta }}_{{\rm{ISCA}}}=0.30{5}^{* * * }$), while its effect remains negligible for OSCA (${\widehat{\beta }}_{{\rm{OSCA}}}=0.00{9}^{* }$). This suggests that shared mobility infrastructure plays a central role in supporting potential bike-and-ride activity.

In contrast, BRT and bus stop density (X4_A) exhibits opposite effects across the two groups (${\widehat{\beta }}_{{\rm{ISCA}}}=-0.14{0}^{* * * }$, ${\widehat{\beta }}_{{\rm{OSCA}}}=0.12{0}^{* * * }$). Areas well served by bus networks tend to generate autonomous DBS use, while reducing the likelihood of rail-oriented intermodal combinations.

Street network characteristics display consistent effects. Intersection density (X2_B) is positively associated with both ISCA and OSCA intensity, with a stronger effect for ISCA (${\widehat{\beta }}_{{\rm{ISCA}}}=0.23{0}^{* * * }$ vs 0.114^***), indicating that well-connected networks facilitate station access. Slope (X2_C) is negatively associated with both groups, more strongly for ISCA (−0.273^***), confirming that topography remains a constraint even for e-bikes. Cycling facilities (X3_A) are positively associated with both groups, reflecting the importance of local infrastructure.

Land use variables show more contrasted patterns. Residential areas (X8_A) are negatively associated with both ISCA and OSCA intensity, with a stronger effect for ISCA (−0.322^*** vs −0.130^***), suggesting that residential zones generate fewer rail-oriented cycling trips.

Farmland (X8_E) and green space (X8_F) exhibit opposite patterns between ISCA and OSCA: They are negatively associated with ISCA intensity (−0.169^*** and − 0.191^***), but positively associated with OSCA (0.029^*** and 0.038^***), indicating that periurban environments support autonomous cycling but are less conducive to intermodal use.

Employment in primary and secondary sectors (X6_A and X6_B) is negatively associated with both groups, while regional POIs (X7_C) are positively associated with both, reflecting the role of central destinations in structuring demand.

Sociodemographic and mobility-tool variables display the clearest divergence between ISCA and OSCA behaviours. Driving licence prevalence (X9_A) is positively associated with ISCA intensity (0.224^***) but negatively associated with OSCA (−0.093^***), suggesting that populations with access to multiple transport options are more likely to combine DBS with rail.

Age (X11_A) is strongly negatively associated with ISCA (−0.245^***) but only weakly with OSCA, indicating that younger populations are more likely to engage in intermodal cycling. Transit pass ownership (X10_A) is positively associated with ISCA only (0.133^***), reinforcing the link between public transport use and DBS integration.

Determinants of travel distance and duration

Results for median trip distance (Y2) and duration (Y3) are presented jointly, given the strong correlation between the two outcomes and their largely overlapping determinants.

Compared to usage intensity, fewer variables are significant, and effect sizes are smaller, indicating that once DBS trips occur, their spatial and temporal extent is less strongly structured by the built environment.

The most consistent predictor across both outcomes is income (X12), which is negatively associated with ISCA trip distance and duration (${\widehat{\beta }}_{{\rm{ISCA}}}=-0.19{7}^{* * * }$ for Y2 and −0.215^*** for Y3), and more weakly for OSCA. Higher-income areas are therefore associated with shorter intermodal trips.

Age (X11_A) is positively associated with both distance and duration for ISCA, suggesting that older users tend to perform slightly longer trips, while effects remain weaker for OSCA.

Local POI density (X7_A) is positively associated with both distance and duration, indicating that destination-rich environments generate longer or more complex trips.

Overall, these results suggest that while usage intensity is strongly shaped by accessibility and land use conditions, trip distance and duration are more weakly structured and primarily associated with individual socioeconomic context and local destination density.

Temporal modulation of determinants

While the global regression models capture the dominant and globally consistent associations between contextual factors and DBS behaviour, estimating the models separately for each temporal slice provides additional insights into how these relationships vary across periods (Supplementary Tables 1–3). These variations should be interpreted as indicative patterns rather than strong evidence of localised heterogeneity. The two strongest predictors of ISCA intensity both display a clear temporal gradient consistent with commuting patterns. PBS station presence (X4_B) shows its highest association during the morning and evening peaks (${\widehat{\beta }}_{{\tau }_{1}}=0.42{6}^{* * * }$, ${\widehat{\beta }}_{{\tau }_{2}}=0.40{8}^{* * * }$), with lower values observed during weekends (${\widehat{\beta }}_{{\tau }_{5}}=0.26{3}^{* * * }$). Transit pass ownership (X10_A) follows a similar pattern, decreasing from ${\widehat{\beta }}_{{\tau }_{1}}=0.26{7}^{* * * }$ to ${\widehat{\beta }}_{{\tau }_{5}}=0.08{1}^{* * * }$. These results suggest that the integration of DBS with rail is more strongly associated with commuting periods than with off-peak or leisure contexts. The negative association between bus stop density (X4_A) and ISCA intensity is also more pronounced during the morning peak (${\widehat{\beta }}_{{\tau }_{1}}=-0.40{7}^{* * * }$) and weakens across the day, becoming relatively small on weekends (${\widehat{\beta }}_{{\tau }_{5}}=-0.06{8}^{* * * }$). For OSCA trips, the association becomes positive from τ₂ onward (${\widehat{\beta }}_{{\tau }_{2}}=0.11{2}^{* * * }$), suggesting that proximity to bus networks is more closely related to independent cycling activity outside intermodal contexts. Commercial and office land use (X8_C) exhibits a change in association across periods for ISCA trips. It is positively associated during the evening peak (${\widehat{\beta }}_{{\tau }_{2}}=0.08{4}^{* * * }$) but becomes negative during off-peak and weekend periods (${\widehat{\beta }}_{{\tau }_{4}}=-0.06{1}^{* * * }$ and ${\widehat{\beta }}_{{\tau }_{5}}=-0.07{6}^{* * * }$). This pattern is consistent with employment areas acting as destinations in evening egress trips, while playing a more limited role outside commuting periods. Income (X12) also shows variation across periods for ISCA intensity. It is positively associated during the morning peak (${\widehat{\beta }}_{{\tau }_{1}}=0.25{2}^{* * * }$) and becomes negative during inter-peak and evening periods (${\widehat{\beta }}_{{\tau }_{3}}=-0.11{9}^{* * * }$). For OSCA trips, income remains positively associated throughout the day. This contrast may reflect differences in mobility patterns across income groups depending on time of day, particularly in relation to commuting structures.

Discussion

This study examined the potential integration of DBS with public transport in the Lausanne metropolitan area using a full year of operator-provided trip data. By combining network-based routing and a spatiotemporal identification framework, we identified behavioural, accessibility, and environmental patterns shaping potential intermodal practices. The findings demonstrate that DBS contributes meaningfully to FLM connectivity and can substantially extend the effective catchment areas of regional rail systems. While the analysis focuses on potential rather than confirmed transfers, the observed magnitudes, spatial structures, and determinants provide a robust basis for assessing the role of shared micromobility in multimodal sustainability. Taken together, the contribution of this paper lies in the articulation between identification, measurement, and explanation of potential intermodal practices.

The analysis indicates that approximately one in four DBS trips in the Lausanne metropolitan area occurs within the spatial and temporal influence of a rail station. This magnitude aligns closely with international evidence, where 17–25% of shared micromobility trips are typically associated with transit stations^{32,63,81,82,83}. This convergence suggests that, even at an early deployment stage, DBS already fulfils a non-negligible connection function in Lausanne.

Behavioural differences between ISCA and OSCA trips further support this interpretation. ISCA trips are shorter, temporally concentrated around commuting peaks, and more frequently associated with major rail hubs, consistent with prior work on bike-and-ride and shared micromobility integration^{74,84,85,86,87}. The asymmetry between access and egress legs mirrors classical commuting patterns and reinforces the interpretation that DBS primarily provides station-oriented mobility functions^88,89.

Although a minority of users accounts for a disproportionate share of ISCA activity, the spatial distribution of trips suggests that potential intermodality is not confined to a niche group but reflects a broader, system-level interaction between DBS and the rail network^90,91. The observed distances are consistent with widely reported acceptability thresholds for cycling-based transfers and echo the principles underlying compact, transit-oriented, and 15-Minute proximity-centred urban systems^13,92,93.

A major contribution of this study lies in quantifying how DBS expands the territorial reach of public transport. By combining empirically derived spatial and temporal thresholds with network-constrained routing, we show that DBS–rail integration extends station catchment areas well beyond walking isochrones. Accessibility gains are observed across multiple spatial dimensions, including surface coverage, street-network reach, population, employment, and destination access, reflecting Lausanne’s polycentric urban structure^94,95,96,97.

Beyond residential coverage, these gains also concern access to activity locations that structure daily mobility, such as employment centres and key destinations. This reinforces the idea that the value of intermodal accessibility lies not only in demographic reach but also in the ability to connect households to jobs and services across the metropolitan region.

The regression results highlight a set of structurally consistent determinants that differentiate ISCA from OSCA behaviour, with the clearest contrasts emerging in accessibility and sociodemographic dimensions. Overall, the findings align with existing evidence on bike-and-ride practices, while revealing patterns specific to the DBS–rail interface.

Accessibility-related variables play a central role. Proximity to stations remains a key driver of DBS usage, confirming well-established distance-decay effects. More distinctively, PBS station presence emerges as the strongest positive predictor of ISCA intensity, suggesting a reinforcing effect between shared mobility services and rail nodes. This supports the interpretation of these locations as emerging ‘mobility hubs’, where shared and public transport modes co-locate and mutually reinforce accessibility⁹⁸.

In contrast, bus stop density exhibits opposite associations for ISCA and OSCA trips, supporting the interpretation of a substitution effect between bus-based feeder trips and cycling in intermodal contexts, particularly during commuting periods. DBS appears to be associated with lower bus-based feeder use, particularly during the morning peak, while reinforcing rail-oriented access. This pattern is consistent with a ‘coopetition’ dynamic in which cycling may substitute for bus-based feeder trips, primarily during commuting hours⁹⁹.

Network structure also shapes DBS use. Intersection density is positively associated with both intermodal and unimodal activity, reflecting the importance of fine-grained street connectivity for cycling. By contrast, slope retains a negative influence despite electric assistance, indicating that topography continues to act as a structural constraint. In the Swiss context, e-bikes are often considered particularly suited to hilly environments¹⁰⁰, yet our results confirm that while electric assistance mitigates slope-related barriers, it does not eliminate them. In this regard, Rérat¹⁰¹ shows that e-bikes ‘flatten’ the topography in Switzerland.

Land use patterns further differentiate DBS behaviours. Residential and periurban areas are negatively associated with ISCA activity, while supporting more autonomous cycling, suggesting that intermodal use remains concentrated in denser and transit-oriented environments. Employment-related effects are more nuanced and appear to depend on local spatial structure, with compact urban centres generating shorter and non-intermodal trips.

Sociodemographic factors provide the most distinctive contrasts. Transit pass ownership is strongly associated with ISCA activity, confirming that intermodal cycling is closely tied to public transport use. Younger populations are more likely to engage in DBS–rail combinations, while areas with higher car ownership also show higher ISCA intensity, pointing toward multimodal rather than substitutive behaviour.

Finally, income emerges as a key determinant of trip distance and duration. Higher-income areas are associated with shorter intermodal trips, likely reflecting better proximity to high-quality transit infrastructure. This suggests that the accessibility benefits of DBS are unevenly distributed, with more efficient intermodal patterns concentrated in already well-served areas, echoing broader concerns regarding equity in shared micromobility systems^102,103.

Taken together, these results indicate that DBS–rail integration is not solely driven by infrastructure availability, but emerges from the interaction between network conditions, land use, and user characteristics.

Although this study is grounded in the Swiss context, the ISCA proxy was deliberately conceived as a transferable analytical framework. Its underlying logic—identifying DBS trips compatible with public transport access using network-based isochrones and spatiotemporal filters—does not depend on institutional specificities. Sensitivity analyses further indicate that the main determinants of DBS–rail integration remain stable across alternative spatial definitions of station catchment areas, supporting the transferability of the approach.

From a sustainable mobility perspective, these findings highlight the capacity of DBS to act as a flexible complement to rail systems by improving access conditions and extending station catchments. In metropolitan contexts where planning frameworks prioritise multimodality and climate mitigation, shared micromobility can contribute to enhancing the competitiveness of public transport, particularly for short- and medium-distance trips.

This study demonstrates that dockless bikesharing (DBS) can meaningfully strengthen first- and last-mile (FLM) connectivity in medium-sized European cities. By applying a spatiotemporal detection pipeline to high-resolution DBS data and by developing the ISCA proxy, we identify trips that are spatially and temporally compatible with public transport access. Overall, the results indicate that shared micromobility can extend the territorial reach of rail-based transport, improving feeder connections and supporting more integrated and sustainable mobility systems.

Beyond these empirical insights, the study contributes a transferable analytical framework for detecting and analysing bike-and-ride integration patterns. The approach combines multimodal routing with finely resolved walking isochrones, spatial clustering to mitigate GPS imprecision, and origin–destination and temporal filters to distinguish compatible transfers from misclassified or substitutive trips. A diagnostic comparison of global and spatially varying specifications further indicates that global GLM and OLS models provide a robust and parsimonious representation of the data. While some spatial heterogeneity is observed, it remains limited in magnitude and does not substantially alter the interpretation of the main relationships, which appear broadly consistent across the study area.

Several limitations must nonetheless be acknowledged. First, ISCA captures potential, rather than confirmed, intermodal behaviour and should be interpreted as an upper bound. Future work should combine operator data with surveys, smartphone traces, or multimodal ticketing data to validate transfer sequences and better capture behavioural diversity. Second, the analysis focuses on rail-based public transport; extending the framework to dense bus or BRT systems will require refined methods to distinguish genuine transfers from spatiotemporal proximity. Third, the study examines a recently deployed DBS service whose usage patterns are still evolving, calling for longitudinal analyses to assess how integration develops as systems mature. Finally, the associations between the built environment and DBS usage should be interpreted with caution, as transit infrastructure and PBS deployments may be partially endogenous to underlying demand and urban structure. In particular, their spatial concentration in dense urban cores may bias the observed relationships and limit causal interpretation.

An important question concerns the extent to which DBS trips substitute for short walk-and-ride or feeder bus trips, as opposed to expanding the effective catchment area of public transport. Some ISCA-classified trips may indeed replace existing access modes in station-adjacent areas, while others likely enable new connections in locations where public transport would otherwise remain less competitive. Disentangling substitution from expansion effects remains a key avenue for future research.

More broadly, these findings highlight the capacity of shared micromobility to act as a flexible complement to rail systems in supporting multimodal and low-carbon mobility transitions. By extending station accessibility and facilitating FLM connections, DBS can contribute to enhancing the competitiveness of public transport, particularly in urban contexts where improving access conditions remains a central challenge for achieving modal shift.