Rethinking GPS: Engineering Next-Gen Location at Uber

Location and navigation using global positioning systems (GPS) is deeply embedded in our daily lives, and is particularly crucial to Uber’s services. To orchestrate quick, efficient pickups, our GPS technologies need to know the locations of matched riders and drivers, as well as provide navigation guidance from a driver’s current location to where the rider needs to be picked up, and then, to the rider’s chosen destination. For this process to work seamlessly, the location estimates for riders and drivers need to be as precise as possible.

Since the (literal!) launch of GPS in 1973, we have advanced our understanding of the world, experienced exponential growth in the computational power available to us, and developed powerful algorithms to model uncertainty from fields like robotics. While our lives have become increasingly dependent on GPS, the fundamentals of how GPS works have not changed that much, which leads to significant performance limitations. In our opinion, it is time to rethink some of the starting assumptions that were true in 1973 regarding where and how we use GPS, as well as the computational power and additional information we can bring to bear to improve it.

While GPS works well under clear skies, its location estimates can be wildly inaccurate (with a margin of error of 50 meters or more) when we need it the most: in densely populated and highly built-up urban areas, where many of our users are located. To overcome this challenge, we developed a software upgrade to GPS for Android which substantially improves location accuracy in urban environments via a client-server architecture that utilizes 3D maps and performs sophisticated probabilistic computations on GPS data available through Android’s GNSS APIs.

In this article, we discuss why GPS can perform poorly in urban environments and outline how we fix it using advanced signal processing algorithms deployed at scale on our server infrastructure.

https://eng.uber.com/rethinking-gps/

Pandas and Python Real World Project (GPS data)

“To celebrate my sub-30 minute finish on the manitou springs incline, I figured I’d drop some python and pandas knowledge while analyzing the data using beautifulsoup, pandas, python dateutil, python googlemaps, python geopy and the standard library.

I try to look at things in a non-trivial way and reflective of actual problems you encounter analyzing real world data. The data file is here https://gist.github.com/jrjames83/4de9d124e5f43a61be9cb2a… come code along!”

https://www.youtube.com/playlist?list=PLImyDqSBQbdmicMPRW0Yo5QHbeOIwC765

A Quick Guide to Redis 3.2’s Geo Support

The Geo API has been around for a while, appearing in the Redis unstable branch about ten months ago and that was, in turn, based on work from 2014. There’s a bit of history in that development process, which being practical folk we’ll skip past and go straight to the stuff that makes your development day better.

At its simplest, the GEO API for Redis reduces longitude/latitude down into a geohash. Geohash is a technique developed in 2008 to represent locations with short string codes. The Geohash of a particular location, say Big Ben in London, would come out as “gcpuvpmm3f0” which is easier to pass around than “latitude 51.500 longitude -0.12455”. The longer the string, the more precise the geohash code.

That encoding into a string is good for humans and URLs but it isn’t particularly space efficient. The good news is geohashes can be encoded as binary and using 52 bits, a geohash gets down to 0.6 meter accuracy which is good enough for most uses. A 52-bit value which just happens to be able to be a small-enough integer to live in a Redis floating-point double safely and that’s what the Geo API works with behind the scenes.

https://www.compose.com/articles/a-quick-guide-to-redis-3-2s-geo-support/

Unwinding Uber’s Most Efficient Service

Uber posted an article detailing how they built their “highest query per second service using Go”. The article is fairly short and is required reading to understand the motivation for this post. I have been doing some geospatial work in Golang lately and I was hoping that Uber would present some insightful approaches to working with geo data in Go. What I found fell short of my expectations to say the least…

https://medium.com/@buckhx/unwinding-uber-s-most-efficient-service

Introducing the Geo API in Redis

“Since a few days, the Geo API has been introduced in Redis. At the time of writing, the work is quite complete but still considered in progress: everything you’ll read here is actually available in the unstable development branch and not yet released for production (plans are to release it with the next 3.2 version).

The Geo API consists of a set of new commands that add support for storing and querying pairs of longitude/latitude coordinates into Redis keys. GeoSet is the name of the data structure holding a set of (x,y) coordinates. Actually, there isn’t any new data structure under the hood: a GeoSet is simply a Redis SortedSet…”

http://cristian.regolo.cc/2015/07/07/introducing-the-geo-api-in-redis.html