Today is the third day of New York's new congestion relief zone. And if you're curious to see how it's already impacting traffic conditions, here is a website run by Joshua Moshes and Benjamin Moshes, under the supervision of Brown University Professor Emily Oster.

The site collects Google Maps traffic data every 15 minutes for 19 routes leading into Manhattan (some of which are directly affected by the new relief zone and some of which are not). It then calculates average traffic times for each day of the week, both before and after the congestion charge.

Here is the Holland Tunnel on Sunday (which was day number one):

And here is the Holland Tunnel on Monday (which was the first weekday):

Already, we are seeing a meaningful reduction in average traffic times. Maybe demand is more elastic than I suggested yesterday. But obviously we're only looking at two days. So I'll check back in later once we have more data points. In the meantime, if you'd like to follow along, you now have a website.

Since we're on the topic of large-scale data collection, I thought some of you may be interested in Uber Movement's new "Speeds" product.

First launched in 2017, Uber Movement aggregates anonymized data from their ride-sharing business to create data sets and tools that can help cities make better transportation decisions.

Below is a (hex cluster) map of Toronto showing average travel times from downtown. I dropped the pin at Toronto City Hall. What is shown is the average for all days of the week during the month of January 2018.

Uber Movement's new Speeds product looks at how specific streets are performing relative to their "free-flow speed." Uber defines this as "the average speed of traffic in the absence of congestion or other adverse conditions." (The 85th percentile of all speed values.)

As of right now, Speeds is only available in 5 cities: New York City, Seattle, Cincinnati, Nairobi, and London. Here is a snapshot of London during the same time period as above, January 2018:

In comparison to what we were talking about yesterday, I have few concerns with the fact that my Uber rides around town have likely contributed to these mappings. With these use cases, the value really only emerges once you aggregate the data.