By: Larry Wang
Connected vehicles open a new world of opportunities to improve the driving experience: Think usage-based insurance, car sharing services, preventive maintenance, and shared auto loans. The key to unlocking this future is understanding the data behind it.
We provide analysts and engineers the geospatial tools to do just that. Here are five ways to drive insight from connected vehicle data with Mapbox.
Run millions of data points through a combination of your own proprietary data processing algorithms and Mapbox APIs to contextualize and enrich your location data. Then create a fast interactive visualization for web or mobile to make your insights actionable, no matter the amount and complexity of data.
1. High-density, high-frame rate data visualization with Mapbox GL
With Mapbox GL, you can visualize billions of data points at 60 fps, creating a fast interactive visualization for web or mobile to make your insight actionable — no matter the amount or complexity of the data.
Customize any aspect of the map with Studio, including embedding your own data, fonts, imagery, video, and 3D. In the example above, Uber used a custom map style and our vector maps — compatible with all platforms — to visualize hundreds of thousands of taxi pickups and drop-offs in Manhattan over the course of the day. Explore the map to see this in action.
2. Real-time drive time analysis
With over 225 million miles of real-time telemetry data process daily, we’re able to provide accurate ETAs and real-world traffic conditions in over 40 countries. Meaning that you have precise drive times, allowing you to identify the best locations for infrastructure, planning for autonomous fleet deployment, analysis of commuting patterns, allocation of car-sharing fleets and more.
Explore actual travel times for a passenger vehicle in London in the example above. Here we’re combining the Mapbox Matrix API and the visualization capabilities of our Mapbox GL JS for the web to represent ETAs as isochrones. Read the blog post or check out the live demo for more.
3. Clean up traces with map matching
Map matching snaps a vehicle GPS trace to the road network allowing you to create clean visualizations, understand travel frequencies on real-world streets, detect speeding, and more.
Our Map Matching API accepts any GPS trace and returns a cleaned up, enriched route. This is interesting for two reasons. First, you get a rectified route snapped to real-world streets. This is nice for visualization but becomes really useful for distance measurements or when you plan to aggregate many trips to measure travel frequency on a particular street. Second, we enrich the GPS trace with street name information and real-time speeds along the way giving you valuable geographic and traffic context.
In the animation above, you can see how the connected car platform Vinli is using Mapbox map matching, showing off the snap-to-road effect nicely.
4. Add location context with Geocoding and Enterprise Boundaries
Similar to map matching, geocoding provides geospatial context for location data. Where map matching snaps to a road, geocoding snaps to an address. For any coordinate, the Mapbox Geocoding API returns an address with the neighborhood, postcode, city, state, country. Alternatively, for an address, the API will return a coordinate. In addition, Mapbox Enterprise Boundaries provide a global set of administrative, postal, and statistical polygons to join your data for analysis and visualization.
5. Mapbox as part of your BI toolchain
For data engineers and visualization exports, our documentation and Studio are excellent entry points to explore Mapbox. Even better, we now come integrated into a number of leading business intelligence platforms. You may be already using one of them. Check out the links below to get started:
I’d love to hear how you want to visualize your vehicle data. Drop me a note at larry@mapbox.com to talk more about your use case.
Driving insights from connected cars was originally published in Points of interest on Medium, where people are continuing the conversation by highlighting and responding to this story.