Track spot freight rates, monitor carrier pricing, extract transit schedules and real-time delays, scrape rideshare surge pricing, and aggregate logistics network data — from ocean freight to last-mile delivery, across every mode of transport.
Logistics and transportation data scraping is the automated extraction of structured information from freight exchanges, carrier websites, load boards, transit agency feeds, rideshare platforms, shipping rate calculators, and real-time tracking portals. This data covers freight spot rates, transit schedules, vehicle locations, delivery time estimates, parking availability, rideshare pricing, and shipping capacity signals.
For shippers, freight brokers, 3PLs, and logistics tech companies, this data is operational intelligence. Freight rates change hourly. Carrier capacity shifts with demand. Transit schedules are updated without notice. Without automated data collection, you're always working off stale information. DataFlirt keeps your logistics data current — from the truckload spot market to urban transit feeds.
Whether you're building a freight rate comparison engine, optimising carrier selection for a supply chain team, or powering a mobility app with real-time transit data, we provide the structured data infrastructure that makes it possible.
Comprehensive extraction built for reliability, accuracy, and scale.
Extract spot rates, contract rates, and load board pricing for major freight lanes across road, rail, and ocean.
Scrape bus, rail, metro, and ferry schedules including real-time delays, service alerts, and route changes.
Track surge multipliers and fare estimates from Uber, Lyft, Ola, Rapido, and local rideshare platforms at high frequency.
Collect live vehicle locations and arrival predictions from GTFS-RT feeds and tracking portals.
Scrape parking availability, rates, and booking data from parking platforms and city parking portals.
Combine road, rail, air, and ferry data into unified journey datasets for trip planning and logistics optimisation.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "mode": "road_ftl", "origin": "Mumbai, IN", "destination": "Delhi, IN", "scraped_at": "2025-06-10T11:00:00Z", "carrier": "GATI Express", "rate": { "currency": "INR", "spot_rate": 42500, "fuel_surcharge": 3200, "all_in": 45700 }, "transit_days": 2, "capacity": "available", "platform": "loadshare.in" }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
We collect and parse both static GTFS schedule files and real-time GTFS-RT vehicle position and trip update feeds.
Freight rates can change hourly. Our infrastructure polls load boards and rate calculators at intervals as low as 15 minutes.
Modern freight platforms like Flexport and Freightos are React-heavy. Our Playwright fleet renders and extracts accurately.
Real-time vehicle tracking and transit delay data delivered via persistent WebSocket connections for live applications.
Road, rail, ocean, and air data normalised into a unified schema so you don't need separate parsers per mode.
Deliver directly to your TMS, WMS, or data warehouse — SAP, Oracle TMS, Snowflake, and custom systems supported.
From solo analysts to enterprise data teams — here's how organizations use this data.
In freight, a $50/lane rate difference across thousands of shipments compounding monthly is the difference between margin and loss. In transit, a 5-minute delay data gap is a missed connection for a commuter. DataFlirt provides the structured, real-time logistics data that lets shippers, brokers, and mobility platforms operate with the intelligence their competitors don't have.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
