We extract route schedules, dynamic pricing signals, seat availability, and stop coordinates from Megabus. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Journeys & Pricing objects from megabus.com. All fields typed and schema-versioned.
"journey_id": "MB-8492-LON-MAN", "origin_city": "London", "destination_city": "Manchester", "departure_time": "2024-10-14T08:30:00Z", "price": 14.99, "currency": "GBP", "available_seats": 42
| # | journey_id | origin_city | origin_stop | destination_city | destination_stop | departure_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from megabus.com. All fields typed and schema-versioned.
"route_id": "RT-104", "route_name": "London to Manchester", "origin_id": "LON-VIC", "destination_id": "MAN-SHU", "distance_km": 335, "average_duration": 270
| # | route_id | route_name | origin_id | destination_id | distance_km | average_duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Stops & Stations objects from megabus.com. All fields typed and schema-versioned.
"stop_id": "LON-VIC", "stop_name": "Victoria Coach Station", "city": "London", "latitude": 51.4933, "longitude": -0.1498, "wheelchair_accessible": true
| # | stop_id | stop_name | city | country | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities & Extras objects from megabus.com. All fields typed and schema-versioned.
"journey_id": "MB-8492-LON-MAN", "has_wifi": true, "has_power_outlets": true, "has_toilet": true, "luggage_allowance": "1 piece 20kg", "extra_luggage_price": 15.0
| # | journey_id | has_wifi | has_power_outlets | has_toilet | luggage_allowance | extra_luggage_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions & Discounts objects from megabus.com. All fields typed and schema-versioned.
"promo_id": "NUS-10", "journey_id": "MB-8492-LON-MAN", "discount_type": "percentage", "discount_value": 10, "student_discount_eligible": true, "valid_to": "2024-12-31T23:59:59Z"
| # | promo_id | journey_id | discount_type | discount_value | terms | valid_from |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Megabus scraper handles date-based searches, dynamic pricing matrices, and regional variations with IP spoofing and session management built in.
Extract departure times, arrival times, and journey durations across the entire Megabus network.
Capture base fares, booking fees, and seat reservation costs. Track price fluctuations as departure dates approach.
Monitor remaining seat counts and wheelchair space availability for every scheduled departure.
Extract exact geolocation data, station names, and street addresses for all Megabus boarding points.
Log onboard facilities including Wi-Fi availability, power outlets, and toilet access per vehicle type.
Scrape Megabus UK, North America, and European routes from a unified pipeline schema.
Run hourly or minute-level checks on high-demand routes to capture flash sales and yield management adjustments.
Extract standard luggage allowances and dynamic pricing for additional bags or oversized items.
Maintain time-series databases of route pricing to build predictive fare models.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or full network scraping requirements. We map the schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for megabus.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel aggregators face strict scraping counter-measures. Here is how we maintain data flow without triggering IP bans.
Megabus requires sequential API calls with valid session tokens to retrieve pricing. We maintain stateful Playwright contexts to emulate legitimate user search journeys.
We route requests through ISP-grade residential proxies matching the target region (UK or US) to bypass geo-blocking and rate-limiting rules.
Instead of parsing complex DOM structures, we intercept Megabus internal JSON API responses for cleaner, faster, and more reliable data extraction.
Our crawlers automatically generate date ranges and iterate through calendar grids to extract fares weeks or months in advance.
Dynamic pricing can return false zeroes. Our pipeline flags anomalous fare drops and triggers automatic retries before data reaches your warehouse.
OTA platforms integrate Megabus schedules and pricing into multi-modal journey planners alongside rail and flight data.
Rival coach operators monitor Megabus yield management strategies to adjust their own dynamic pricing algorithms.
Urban planners and transport consultants track intercity mobility patterns and route frequencies.
Data science teams build machine learning models to forecast ticket price fluctuations based on historical booking curves.
Discount aggregators track promotional fares and NUS discount eligibility for university routes.
Operators analyse active fleet deployment and timetable density across different geographic corridors.
"Megabus pricing changes continuously based on load factors and departure proximity. You need high-frequency extraction to capture the true yield curve."
Building a reliable scraper for travel operators requires complex session management, residential proxies, and calendar traversal logic. DataFlirt abstracts this infrastructure so your engineering team can focus on fare analysis and route optimisation rather than maintaining broken web scrapers.
Everything supported by our megabus.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright manages cookie sessions and API interception for dynamic fare retrieval.
We maintain pools of residential ISP proxies across UK and US regions. Rotation happens per-request with sticky sessions for search flows.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About megabus.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline supports both regional variants, handling the different domain structures, currency outputs, and route networks natively.
We can run pipelines at daily, hourly, or sub-hourly cadences depending on your requirements and the specific routes targeted.
We extract the full journey itinerary, including all intermediate stops, arrival times, and departure times for each segment.
Yes. We capture the remaining seat count and specific wheelchair space availability as reported by the Megabus booking engine.
We utilise geographically matched residential proxies, intelligent request throttling, and session persistence to mimic legitimate user traffic and avoid IP bans.
We monitor pipelines 24/7. Since we primarily target their internal APIs rather than DOM elements, our extraction is highly resilient. If an endpoint changes, our engineers update the pipeline within our SLA window.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily snapshot of UK routes or high-frequency price tracking across North America, we build and manage the infrastructure. Tell us your requirements.
