Surface Analysis
How to quantify street surface conditions from sensor data.
Overview
E-scooter telemetry is analyzed to estimate surface quality and map the results to OpenStreetMap road segments. The pipeline produces georeferenced, confidence-scored proposals for review. Once added to OpenStreetMap, these smoothness tags can support wheelchair-routing engines that use OSM accessibility data.
Classification Methodology
Surface quality is assessed using the Z-axis acceleration standard deviation, which strongly correlates with the roughness experienced by wheelchair users. The workflow below anchors that raw signal to OSM's established smoothness scale before any threshold is trusted.
| 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 |
Surface Classification Timeline
The visualization below depicts how these classes unfold along a ride. Each point is color-coded by smoothness class, with a confidence overlay indicating the certainty of wheelchair accessibility for each segment.
Single Ride Capability
Even a single high-quality ride can provide valuable wheelchair accessibility data. What counts as high quality is defined by a small set of requirements covering sampling coverage, riding speed, and sensor integrity.
| Requirement | Description |
|---|---|
| Proposal Generation | More than 30% of the segment must be sampled to surface a candidate for review or map display |
| Optimal Speed | Ride speed within the 2–6 km/h assessment range |
| Data Quality | Stable GPS accuracy and complete sensor readings |
| Apply Filters | At least 70% coverage and 70% point agreement |
Large Dataset Aggregation
A multi-ride workflow can aggregate rides on the same street segments to reduce the influence of anomalies, building on the same classification and confidence scoring that single rides already use. The parameters below define how such an aggregation weighs and combines rides.
| Metric | Description |
|---|---|
| Spatial Clustering | Group rides within a 10 m tolerance (allows for GPS error) |
| Temporal Weighting | Recent rides receive higher weight |
| Consensus Scoring | Proposed minimum of 3+ rides for high-confidence proposals |
| Coverage Calculation | Proposed minimum of more than 30% segment coverage |
| Agreement Threshold | Proposed 70% agreement threshold; 75% proposed for automatic handling |
| Indicator | Description |
|---|---|
| Ride Count | More rides increase confidence in results |
| Coverage Percentage | Proportion of the segment that was sampled |
| Temporal Spread | Data collected from different times and days |
| Speed Consistency | Measurements taken under similar conditions |
Validation Results
Validation assesses how well single-ride sensor-derived data matches real-world conditions, using streets that already carry OSM smoothness tags as the reference. Multi-ride aggregation is a natural extension of the same approach and is described separately below.
Single Ride Performance
For single rides, the measurements showed 78% agreement with existing OSM tags, effective for segments longer than 50 m, and a false positive rate below 15% for high-confidence proposals. Agreement was measured against segments that already carry smoothness tags in OSM.
Multi-Ride Enhancement
Multi-ride aggregation can improve consistency, expand coverage systematically across the city, and support monitoring of surface quality changes over time. As more rides accumulate on the same segments, individual anomalies matter less and longer-term trends become visible.
Practical Application
The methodology tells contributors where collected data helps most: use it to improve local accessibility mapping in the areas where it is most needed. The guidance differs by audience, but the underlying data and thresholds are the same.
For Contributors
Individual contributors can start with small rides to fill gaps, focusing on areas lacking surface quality data. Each ride contributes georeferenced measurements that can be aggregated with other rides to strengthen accessibility proposals.
For Cities
Cities benefit from systematic mapping, monitoring improvements, and prioritizing interventions where accessibility barriers are most critical. Aggregating e-scooter and manual data can help urban planners identify high-impact areas and track changes in surface quality over time.