The latest project out of MIT's Senseable City Lab examines the "sensing power of taxis" in various cities around the world. Looking at traffic data, they determined how many circulating taxis you would need to equip with sensors if you wanted to capture comprehensive street data across a particular city. This might be useful if you wanted to measure things like air quality, weather, traffic patterns, road quality, and so on.
What they found is that the sensing power of taxis starts out unexpectedly high. It would only take 10 taxis to cover 1/3 of Manhattan's streets in a single day. However, because taxis tend to have convergent routes, they also discovered rapid diminishing returns. It would take 30 taxis (or 0.3% of all taxi trips) to cover half of Manhattan in a day, and over 1,000 taxis to cover 85% of it. A similar phenomenon was observed in the other cities that they studied: Singapore, Chicago, San Francisco, Vienna, and Shanghai.
However, if you look at the percentage of trips needed to scan half of the streets in a city, Manhattan has the lowest rate at 0.3%. Vienna is the highest at 9%. But I'm not sure if this is a function of the utilization rate of their taxis or if it has something to do with urban form. Singapore has a similarly low rate (0.44%), but its street grid looks nothing like that of New York's.
Here's a short video explaining the project:
https://youtu.be/Vs3q3jQaM9Q
Here is an interesting study by the MIT Senseable City Lab, which looks at: “the minimum number of vehicles needed to serve all the trips in New York without delaying passengers’ pick up times.” If you can’t see the embedded video below, click here.
[youtube https://www.youtube.com/watch?v=nFo64kBGF6o&w=560&h=315]
This is interesting because it begins to quantify the amount of waste running through the system today and the possible efficiencies brought about by autonomous vehicles. In this model, the current taxi fleet in NYC could be reduced by 40%.
For more on the study, go here.
Below is a short video that was created by the MIT Senseable City Lab, World Economic Forum and TomTom for a study on how people move in 100 cities around the world. They call it the Global Mobility Index.
It shows congestion levels (using real-time traffic data from TomTom), commute times, and an estimate for the percentage of trips that could be shared if people were willing to wait up to 5 minutes.
In the case of Toronto, they estimate that 99% of trips could be shared and that it would increase average speeds by ~7.9 km/h and reduce overall traffic levels by ~44.09%.
Their solution to solving traffic congestion is a cocktail that involves car-sharing, bike-sharing, and public transit. It’s about developing a “mobility portfolio.” Seems sensible.
I found myself wanting more information and data after watching the video. Still, it was interesting to see what the authors describe as the “pulse of our cities.”