Micromobility Data
Extraction Services

Micromobility operators don’t publish competitive data. GroupBWT extracts it — structured fleet positions, pricing, coverage zones, and device status across Bolt, Dott, Voi, Bird, and 10+ operators — delivered to your warehouse at the cadence your operations team uses.

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100+

software engineers

15+

years industry experience

$1 - 100 bln

working with clients having

Fortune 500

clients served

We are trusted by global market leaders

Fleet availability, pricing, coverage, and device history — across every operator active in your target cities. All data types normalize to a unified schema before delivery.

Fleet Availability and Locations

Vehicle counts by zone, updated from 15 minutes to daily. Scooters, e-bikes, and mopeds from Bolt, Dott, Voi, Bird, Whoosh, and 10+ operators.

Pricing and Tariff Structures

Unlock fees, per-minute rates, and promotional pricing per operator, city, and time window — ready for cross-provider analysis.

Device Status and Battery Levels

Availability status, charge level, and model data where the operator’s app exposes it — estimating effective fleet capacity beyond raw counts.

Coverage Areas and Service Zones

Coverage Areas and Service Zones
Geo-boundaries of active service zones per operator. Tracks where providers expand, contract, or overlap — the foundation of coverage gap analysis.

Historical Usage Patterns

Time-series records of device density, trip frequency, and zone activity — the pattern layer driving repositioning and demand forecasting.

Real-Time Trip Detection

Movement tracking from positional changes and power data. Detects trip starts, ends, and idle periods even when operators don’t publish trip counts.

Why External Mobility Data Matters for Business and Cities

Operator apps hold the competitive intelligence your team needs — fleet positioning, real-time pricing, coverage decisions — and it isn’t accessible through official channels. This is what micromobility data extraction services are built for.

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Operator APIs don't give you what you need

Public APIs either don't exist or surface only aggregate counts — not the vehicle-level, zone-by-zone data needed for competitive analysis. Open GBFS feeds are typically delayed by hours or days. GroupBWT extracts directly from operator apps — at scale and on schedule.

Pricing changes happen intra-day and invisibly

An operator raises per-minute pricing in a district while a competitor holds steady — and you find out in a quarterly report. With extraction in place, your team sees the shift within hours.

Demand gaps open and close in hours

In high-density districts during morning rush hour, available scooters can drop to single digits across an entire neighborhood. Those gaps represent unserved demand — only visible with high-frequency fleet data.

Market entry decisions rely on stale intelligence

Third-party mobility reports lag by weeks. When evaluating a new city or monitoring a competitor's expansion, you need operator-specific data by zone — not market-wide estimates. GroupBWT builds the extraction infrastructure that closes the gap — live operator data, your schema, your cadence.

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Benefits of Micromobility Data Extraction Services

Structured competitor data, delivered on schedule, removes the intelligence gap that forces reactive decisions.

Replace Third-Party Reports With Daily Operator Data

Fleet counts, pricing shifts, and coverage changes on a schedule your team sets — not when a vendor publishes their next report.

Spot Competitor Moves Before They Hit Your Ride Volume

When a rival adds vehicles or adjusts pricing, you see it within hours — before the shift reaches your ride metrics.

Act on Market Signals the Same Day

Scheduled pipelines remove the bottleneck between intelligence and action. Your team adjusts repositioning the same morning the signal arrives.

Data Your ML Models Can Learn From

External fleet data adds the market-side dimension internal trip logs can’t provide — improving demand forecast accuracy.

Start With One City, Scale Without Rebuilding

Validate quality in a single market before expanding. Each new city is a configuration addition.

Zero Transformation Between Extraction and Analysis

Data lands in your warehouse pre-normalized and schema-stable. Analysts query from row one.

The real-time mobile app extraction infrastructure is maintained continuously — operator app updates don’t interrupt your feed.

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Cities differ in operator mix and density; teams differ in delivery format, refresh cadence, and downstream tooling. Every engagement is scoped to your markets and your stack — not a generic template adjusted at the edges.

Tailored Coverage

Per-source collection logic mapped to your operator mix and target markets. Start in one city to validate quality, then extend the same pipeline to the rest.

Direct BI Integration

Output pre-shaped for Tableau, Power BI, Looker, or custom dashboards. Data lands in your warehouse on the schema your analysts already query.

Modular by Design

Add operators, cities, or data types through configuration. Each source is isolated, so one expansion can’t break another.

Cadence on Your Schedule

Daily, hourly, or 15-minute intervals — set per data type to match how your operations team works, not how a vendor batches reports.

Travel & Hospitality

Hotels, OTAs, and destination teams track scooter and e-bike availability around properties so guests don't wait for a ride that isn't there. Fleet density near resorts and airport hubs lands in the same dataset already running rate-parity and inventory feeds.

Automobile

Auto OEMs and rental fleet operators benchmark micromobility share against car rental, car-sharing, and ride-hail demand in the same urban catchments. One pipeline resolves rental pricing and scooter or moped operators on a unified schema.

Real Estate

Developers, REITs, and urban planners use micromobility coverage as a proxy for street-level demand at candidate sites. Operator-reported zones rarely match where vehicles actually appear; the feed shows availability hour by hour, district by district.

Retail

Retailers monitor scooter and e-bike flow around flagship stores to spot catchment shifts that foot-traffic counts miss. Density data pairs with competitive monitoring for parking demand, last-mile logistics, and store-format decisions in dense urban grids.

E-Commerce

Quick-commerce, grocery, and last-mile delivery operators study competitor micromobility fleets for routing, dispatch, and rider-recruitment signals. Where rivals concentrate vehicles tells the dispatch team where demand is likely tomorrow. Same setup as product-level e-commerce monitoring.

Telecommunications

Telcos with IoT and connectivity contracts for shared-fleet vehicles use mobility data to scope SIM volume, service zones, and renewal pricing per operator. Live fleet counts and device-status feeds give the commercial team the picture quarterly partner reports miss.

Advanced Technologies Behind Mobility Data Extraction

Engineering Layer

Generic / Off-the-Shelf Approach:

GroupBWT Engineering:

API access

Stops at the public surface — most operators expose no public API

Mobile app reverse engineering maps hidden endpoints and data schemas

Request volume

Single-IP scrapers trigger rate limits within minutes

Proxy and IP rotation distributes load across multiple IPs and geographic proxies

City coverage

Random sampling misses entire districts

Grid-based lat-lon iteration covers every zone systematically

Anti-bot updates

Pipeline breaks each time the operator changes auth, headers, or device fingerprints

Adaptive request emulation mimics legitimate headers, device IDs, and GPS coordinates

Delivery to analytics

Raw exports require a manual transformation layer before they reach the warehouse

Warehouse-native writes land structured rows in BigQuery, Snowflake, or Redshift

API access