How Lyft discovered OpenStreetMap is the Freshest Map for Rideshare
By: Clare Corthell
Tags: Data Science, Mapping, Technology, Rideshare, Lyft

Mark Huberty & Clare Corthell, Lyft Mapping

Roads to the new terminal at LaGuardia Airport opened in July 2020, evidenced by aggregate Lyft traffic (left). OpenStreetMap data had been updated (right) shortly after opening, faster than many other maps.

Lyft Mapping study shows crucial OpenStreetMap road attributes are fresh and high quality in 30 North American cities, as compared to groundtruth.

Lyft moves people — from home to work, work to play, play to rest, through cities and beyond. Maps play a critical role, helping Lyft figure out where drivers and riders are, how best to connect them, and estimate how long it will take to get to the destination.

Lyft Mapping is built on top of OpenStreetMap. This global map database is used by millions of people around the world, for combatting climate change, tracking agricultural land use, disaster recovery, refugee response, academic research, and much more. After 16 years of growth, OSM is now commonly used by many companies to power applications like logistics platforms, social media, and gaming. OSM is now the biggest crowdsourced repository of human geospatial knowledge. But is this map suitable for supporting the rideshare experience? Is it the best option available? Can Lyft support the OSM community and contribute to making the map better? Though we had a strong intuition that OSM offered a complete road network, we didn’t know how well the map matched the real world — so we ran a study.

tl;dr

After three months, thousands of miles driven, and a lot of skilled work from our data curation team, we’re happy to report positive findings:

OpenStreetMap has a very high-quality road network in 30 large North American cities. The OpenStreetMap community can be credited with maintaining the map at a reliably fresh standard in these areas. This survey design is potentially valuable for any study of map quality relative to groundtruth.

In this post, we’ll review how we did this (see the paper), the findings, and takeaways for Lyft and the mapping community.

Survey Design: How accurate is the map?

Measuring the entire map — whether a road exists, is annotated correctly, is up-to-date — is simply hard. Lyft operates in over 300 markets in the United States and Canada, from dense, old urban areas like New York City, to sprawling suburban metropolises like Phoenix or Los Angeles. To get the ground truth required to assess map quality, we might send a surveyor to every intersection and record whether something is correct. The US Census Bureau does exactly this — in fact, it hires so many people every 10 years that it singlehandedly changes the unemployment rate. But this approach could be incredibly slow and expensive. We needed a survey design that balanced our desire for regional specificity with cost and logistical feasibility.

An example of our sampling plan in Greater New York City. Mapillary dispatched cars to each of the S2 polygons and collected imagery along all driveable roads in each.

Sampling + Remote Sensing = Feasible Study

We knew that we needed a sampling-based approach to make this tractable. But a pure random sample wouldn’t work; sending people to randomly-sampled intersections around a city would be incredibly time consuming. Instead, we looked to public health and remote sensing for a solution. Health researchers often face a similar problem of how to send a limited number of survey workers to homes for evaluation of disease prevalence or health outcomes. They solve this with cluster sampling:

Sample spatial units, such as a city block Sample households from that block

This two-stage process simplifies life for survey workers. A survey taker can go to one city block and visit multiple households at a time, maximizing the information they get from one trip. This sort of survey design isn’t as statistically efficient as a pure random sample, but what it lacks in purity it makes up for in logistical simplicity and cost effectiveness.