Can you give us average fare per trip for a city?

No, and no compliant vendor can. That figure is computed from completed trips and only the platform holds it. It appears publicly only in occasional regulatory or investor disclosures, at national level and months late. What we build is a fare-estimate panel: fixed routes, sampled on a schedule, per vehicle class, timestamped. From that you can derive a per-kilometre rate for those routes. It is a real dataset and it is not the same quantity, so we label it accurately.

Other vendors sell an average fare. Are they wrong?

They are sampling estimates and averaging them, which is reasonable arithmetic with an inaccurate label. A sampled average differs from a trip average because real trips are not evenly distributed across routes, riders decline surge while a sampler does not, and promotions change what was actually paid. The gap between the two is not constant either, so you cannot correct for it. If the true average is what your analysis needs, this is a licensed-data question.

Why does the route panel have to stay fixed?

Because a series is only comparable if the thing being measured does not change. If the route set shifts between months, a move in the average could be the market or could be the panel, and nothing in the data tells you which. We fix origin and destination coordinates, record a route class on every observation, and agree the mix with you upfront so you can weight it yourself.

How do you handle surge?

Where a platform displays a multiplier or a surge notice, we capture it as displayed. Where surge is baked into the quote without disclosure — increasingly the norm — we set surge_disclosed to false and do not infer it. Inferring surge from a price jump would mean labelling ordinary demand pricing as surge, which is a conclusion rather than an observation.

Do you collect anything about drivers or riders?

No. No names, no ratings, no identities, no trip records. The unit of analysis is a route and a quote. This is the same boundary applied across every service we run, and it matters more here because ride-hailing data about individuals is sensitive in every jurisdiction we operate in.

What does a ride-hailing panel cost?

We quote individually. The drivers are cities times routes times vehicle classes times samples per day, and the sample count is the unusual multiplier because daypart shape is the whole point. A tight panel across a few cities is inexpensive; broad global coverage at high frequency is not, and we will tell you which your question actually needs. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

Platform · Ride-hailing platforms

Ride-Hailing Market Data

Scheduled fare research data for agreed routes and times, plus publicly shown service information such as vehicle categories and service areas, for pricing studies and market research.

Ride-hailing market data is built from scheduled fare research for agreed routes and times, plus publicly shown service information such as vehicle categories and service areas — for pricing studies and market research. Uber, Ola, Bolt, Careem and Grab do not publish average fare per trip or per kilometre — only the platform holds that. What is observable is the fare a rider is quoted for a stated route at a stated moment, and a panel of those quotes is a real and defensible dataset.

Ride-hailing is not a catalogue. What a platform shows a prospective rider is a quote for one route at one moment, so we design the collection around that.

Get a free sample See sample data

Free pilot on your own Ride-hailing platforms list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

ridehail_panel.jsonl LIVE FEED

{"platform":"platform-a","city":"Sao Paulo", "route_id":"SAO-AIRPORT-CENTRE-01", "route_class":"airport", "vehicle_class":"economy", "fare_quoted":74.50,"currency":"BRL", "distance_km_quoted":27.4,"duration_min_quoted":38, "fare_per_km_derived":2.72, "surge_active":false,"surge_disclosed":true, "observed_at":"2026-08-25T08:15Z","daypart":"morning_peak", "city_average_fare":"not_produced"} {"route_id":"SAO-AIRPORT-CENTRE-01", "fare_quoted":121.00,"observed_at":"2026-08-25T18:40Z", "surge_disclosed":false, "note":"same route, evening peak — surge NOT disclosed, so not inferred"} {"route_id":"SAO-SUBURB-NORTH-04", "quote_returned":false, "no_quote_reason":"no_vehicles_available", "caution":"availability state — not a high price"}

3 of 1,884,200 route-quote rows cities: 42fare-ESTIMATE panel · trip averages NOT produced · schema v1.2

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Ride-hailing platforms or its owners. Ride-hailing platforms and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Ride-hailing platforms at a glance

How we handle ride-hailing platforms specifically

Ride-hailing is not a catalogue. What a platform shows a prospective rider is a quote for one route at one moment, so we design the collection around that.

Published: A fare estimate for a specific route, as shown to a prospective rider
Not published: Average fare per trip or per kilometre
Our record: One quote per route, vehicle class and scheduled observation time
Derived: Fare per km for that single route, using the platform's quoted distance
Surge: Recorded only where the platform displays it at the time of a scheduled observation
Coverage design: A fixed route panel per city, held stable so the series is comparable
Frequency: Scheduled samples at agreed dayparts
Never: Rider or driver personal data, trip records, signed-in fares

Platform specifics

Why a fare panel, and not the average everybody asks for

These are the reasons a Ride-hailing platforms dataset needs its own handling rather than a shared retail schema.

The number buyers ask for does not exist publicly

Average fare per trip is computed from completed trips. It sits inside platform systems and appears only occasionally in regulatory or investor disclosures, usually at national level and with a delay.

What a vendor selling it is actually doing

A vendor offering a city "average fare" is averaging sampled quotes and labelling the result an average. That introduces three biases:

Sampled routes are not distributed the way real trips are.
Surge affects real trips unevenly across the day.
Promotions and rider tiers do not appear in public quotes.

A route panel is a design decision, not a scrape

The value is in the panel design: fixed routes, a stated route mix, deliberate daypart sampling, and vehicle class as a dimension. Fare per km uses the platform's own quoted distance.

Surge, availability and coverage are separate signals

Price, availability and coverage are distinct and should not be merged into one number.

Surge

Where a platform displays a surge multiplier or surge notice at the time of a scheduled observation, we record it as displayed. Undisclosed surge is flagged, not inferred from price jumps. This is research data, not a live surge feed.

Availability

A "no cars available" state is recorded separately from a high price, with a reason code.

Coverage

Tracking whether quotes are returned on panel routes over time, alongside publicly listed service areas, shows platform entry and exit early.

Scope

What we collect on ride-hailing platforms, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Quoted fares (and fare range where shown) for agreed panel routes
Platform-quoted distance and duration
Route class and daypart of each scheduled observation
Displayed surge, with a surge_disclosed flag
Quote-returned state and no-quote reason
Vehicle categories as the platform labels them
Publicly shown service areas and route coverage over time

❌ What we do not, and why

City average fares (not published; see above)
Trip counts or revenue (not published)
Rider or driver personal data
Inferred surge
Fares that require a signed-in account
Live, on-demand fare quotes for arbitrary pickup and drop-off points

Core ride-hailing fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
platform / city / country	Service and location
route_id / route_class	Stable route identifier and class (e.g. airport, centre, suburb)
origin_lat_lng / dest_lat_lng	Fixed, agreed panel coordinates
vehicle_class	Tier name as the platform labels it
fare_quoted / currency	Displayed estimate
fare_low / fare_high	Quoted range where offered
distance_km_quoted / duration_min_quoted	Platform-supplied values
fare_per_km_derived	Per-km rate for that route only
surge_active / surge_multiplier / surge_disclosed	Surge as displayed at observation time
quote_returned / no_quote_reason	Availability state
observed_at / daypart	Scheduled observation timestamp and derived daypart

Use cases

What teams do with ride-hailing data

City-level fare benchmarking on a stated basis

Fixed route panels sampled across agreed dayparts give comparable city series, with a route mix you can weight.

Competitive entry and withdrawal detection

Changes in route coverage and publicly listed service areas show platform launches or exits early.

Vehicle-class positioning

Economy and premium comparisons on the same routes show where platforms compete.

Daypart and surge behaviour

The intraday shape of fares across scheduled observation times, with surge measured only where disclosed.

The 24-hour sample — run on your routes, not ours

Send a list of routes and time windows. We run collection against the real platforms and return output with platform fields populated, plus a coverage and QA note. The sample is free, with no card and no trial clock.

Real extraction on your routes
Turnaround target: 24 hours
Coverage and QA note included
You keep the data regardless
A named engineer

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

Engagement

Three ways to engage us

Managed service (most common)

We own collection, QA and scheduled delivery to your warehouse or S3, with a dedicated engineer.

API access

The scheduled panel data we have already collected, exposed through an authenticated REST endpoint with sandbox keys and a versioned schema. This serves stored research data; it does not return live fare quotes for arbitrary routes and is not intended for showing fares inside a consumer app.

One-time or project extraction

A fixed scope and a single delivery with a QA report. It can convert to a managed service later.

Pricing

Quoted individually: a free pilot first, then a fixed monthly figure with no per-request metering or overage billing.

Request a quote

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

REST endpoints on the same pipeline, with sandbox keys for testing.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A fixed-scope extraction for a single project or research question.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Pricing is quoted individually, based on category scope, number of markets, cadence and ongoing identity maintenance. After a scoping call and free pilot, we quote a fixed monthly figure, with no per-request metering.

Book a 20-min scoping call Request a quote Talk to an engineer

Ride-hailing data is usually collected alongside its competitors

Fare panels are most useful when several platforms are collected on one schema and one schedule.

That is what ecommerce data scraping covers, and a Ride-hailing platforms-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Ride-hailing data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

No. Only the platform holds that figure. We offer a scheduled fare-estimate panel instead and label it as such.

Their figures come from sampled estimates; the problem is the label. The gaps between sampled quotes and real trips cannot be corrected for.

If routes change, month-over-month movement cannot be interpreted.

Surge displayed at a scheduled observation is recorded as shown. Undisclosed surge is marked false, not inferred.

No. We provide scheduled fare research data for agreed routes and times, not a real-time fare API or instant estimates for arbitrary trips.

No personal data and no trip records.

It is quoted individually after a scoping call and a free pilot, then set as a fixed monthly figure.

See real ride-hailing data before you commit to anything

Send your route list for a free pilot with no obligation. If a field you need is not available, the sample will show that.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

▶

1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

▶

2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

▶

1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner