Abstract
How to translate e-scooter telemetry into wheelchair accessibility data.
The Core Concept
Each e-scooter ride generates telemetry data that may reveal surface quality, slope, and the existence of sidewalks and ramps. The Wheels4Wheels project explores whether analyzing accelerometer readings from regular rides can be used to estimate wheelchair accessibility at the street level and upload this information to OpenStreetMap. The following modules summarize the data sources, methodology, and workflow used throughout the project.
Finding the Data Source
E-scooters generate high-frequency telemetry data (30 Hz accelerometer readings, GPS coordinates, and speed), which can be repurposed to measure surface conditions. In particular, the Z-axis accelerometer data is processed to estimate surface roughness, slopes, and inclines – a quantitative signal for wheelchair accessibility.
The Process in Detail
A step-by-step breakdown of the methodology, showing how e-scooter telemetry is transformed into actionable accessibility data. Each of the four steps feeds the next, ending in proposals that are ready for review.
1. Data Collection
Telemetry data is collected from e-scooter rides. These measurements provide the foundation for assessing surface conditions. The table below summarizes the data types and their purposes, where only data relevant to accessibility analysis is extracted.
| Data Source | Description | What It’s Used For |
|---|---|---|
| 30Hz Accelerometer Data | X, Y, Z axes, sampled at 30 times per second | Sudden spikes in the Z-axis values indicate a bumpy surface |
| GPS Coordinates | Collected every second (latitude, longitude), with timestamps | Mapping location of surface measurements and proposals |
| Speed Readings | Raw telemetry in 1/10 km/h (converted by dividing by 10) | Ensuring speed is within the wheelchair assessment range and supporting data filtering |
2. Surface Analysis
Z-axis acceleration data is analyzed over 1-second intervals, smoothing out transient noise and producing a single value per second. This value correlates with the roughness of the surface, providing a standardized measure for wheelchair accessibility. The table below translates these standard deviation ranges into descriptive surface categories.
| Classification | Z-axis Standard Deviation (mg) | Description |
|---|---|---|
| Excellent | 0–60 | Smooth asphalt or concrete |
| Good | 60–110 | Minor imperfections |
| Intermediate | 110–180 | Noticeable bumps and cracks |
| Bad | 180–250 | Rough surface, significant obstacles |
| Very Bad | >250 | Extremely rough, impassable |
3. Wheelchair Calibration
To confirm that surface roughness measurements align with real-world conditions, the thresholds are systematically validated against existing OpenStreetMap tags. Streets that are already well mapped serve as the reference for calibrating the classification.
4. Confidence Scoring
Each measurement receives a wheelchair-focused confidence score between 0 and 100%. The score expresses how much trust a reading deserves before it becomes a tagging proposal, and it is computed from three metrics.
| Metric | Description |
|---|---|
| Classification Clarity | Shows how confidently a reading fits within calibrated thresholds |
| Speed Appropriateness | Checks whether measurements fall within the 2–6 km/h assessment range |
| Movement Continuity | Penalizes readings taken during stops to maintain data reliability |
Key Insight
Even a single ride can provide valuable data if its coverage and point agreement are high enough. The proposal generator can surface a way-level candidate when more than 30% of the segment is sampled. Applying that proposal with the current tooling requires at least 70% coverage and 70% point agreement.
Benefiting Parties
The Wheels4Wheels dataset is useful to more groups than wheelchair users. The following sections outline what each group can take from the project’s findings.
| Focus | Benefit |
|---|---|
| Improved Navigation | Get more accurate and up-to-date wheelchair accessibility information |
| Safety | Avoid dangerous roads and areas |
| Inclusion | Feeling of support from the community |
| Focus | Benefit |
|---|---|
| Accuracy | Contribute to more accurate and up-to-date wheelchair accessibility data |
| Inclusion | Contribute to a more inclusive and accessible world |
| Sustainability | Help keep accessibility data maintained over time |
| Focus | Benefit |
|---|---|
| Identify Problem Areas | Systematic mapping of accessibility barriers |
| Track Improvements | Monitor surface quality changes over time |
| Data-driven Planning | Prioritize infrastructure investments based on actual usage |
Output
The exploration wraps up into a set of concrete artifacts that let others pick up the work. Everything is designed to be handed over – documented methods, working scoring code, reviewable maps, and proposals formatted for direct OSM contribution.
Behind the deliverables sits one reproducible pipeline. The stages below show where each artifact comes from, from telemetry ingestion through classification and scoring to the proposals that are finally exported.
| Stage | Description |
|---|---|
| Input | Anonymized e-scooter telemetry in JSON format |
| Processing | Python pipeline and OpenStreetMap CLI for Wheels4Wheels surface quality proposals |
| Output | CSV, GeoJSON, and HTML maps ready for review and integration |
| Integration | Compatible with standard OpenStreetMap editing workflows |
| Visualizations | Interactive charts illustrating key analysis and metrics |
Anyone Can Help
Anyone can contribute to improving accessibility maps, from individuals reviewing a single ride to organizations processing city-scale datasets. The two paths differ mainly in volume, not in kind, and both rely on the same tooling and the same review standards.
Individual Contributors
Contributors can analyze their own ride data, where even a single high-quality ride improves local accessibility mapping. They can review proposed accessibility tags through CSV exports and interactive maps, and focus on targeted contributions to fill gaps in wheelchair-accessible routes.
Organizations
Organizations can process large volumes of telemetry data and reach city-wide coverage. The confidence-scoring system helps prioritize high-impact contributions, while outputs are formatted for OpenStreetMap and other accessibility-focused platforms.