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MIT CSAIL, Cornell study finds rides-sharing theoretically could cut taxi traffic in NYC by 75%

A new modeling study by a team from MIT CSAIL (Computer Science and Artificial Intelligence Laboratory) and Cornell suggests that using ride-sharing from companies like Uber and Lyft theoretically could reduce the number of taxis on the road in New York City by 75% without significantly impacting travel time. A paper on their work will be published this week in Proceedings of the National Academy of Sciences (PNAS).

Led by Professor Daniela Rus of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), the researchers developed a dynamic ride-sharing algorithm that found that 3,000 four-passenger cars could serve 98% of taxi demand in New York City, with an average wait-time of only 2.7 minutes. The team also found that 95 percent of demand would be covered by just 2,000 ten-person vehicles, compared to the nearly 14,000 taxis that currently operate in New York City.

Using data from 3 million taxi rides, the new algorithm works in real-time to reroute cars based on incoming requests, and can also proactively send idle cars to areas with high demand—a step that speeds up service 20%, according to Rus.

Instead of transporting people one at a time, drivers could transport two to four people at once, results in fewer trips, in less time, to make the same amount of money. A system like this could allow drivers to work shorter shifts, while also creating less traffic, cleaner air and shorter, less stressful commutes.

To our knowledge, this is the first time that scientists have been able to experimentally quantify the trade-off between fleet size, capacity, waiting time, travel delay, and operational costs for a range of vehicles, from taxis to vans and shuttles. What’s more, the system is particularly suited to autonomous cars, since it can continuously reroute vehicles based on real-time requests.

—Daniela Rus

While the concept of carpooling has been around for decades, it’s only in the last two years that services like Uber and Lyft have leveraged smartphone data in a way that has made ride-sharing a cheap, convenient option. (In 2015 Lyft reported that half of its San Francisco trips were carpools.)

However, existing approaches are still limited in their complexity. For example, some ride-sharing systems require that user B be on the way for user A, and need to have all the requests submitted before they can create a route.

In contrast, the new system allows requests to be rematched to different vehicles. It can also analyze a range of different types of vehicles to determine, say, where or when a 10-person van would be of the greatest benefit.

The system works by first creating a graph of all of the requests and all of the vehicles. It then creates a second graph of all possible trip combinations, and uses a method called “integer linear programming” to compute the best assignment of vehicles to trips.

After cars are assigned, the algorithm can then rebalance the remaining idle vehicles by sending them to higher-demand areas.

A key challenge was to develop a real-time solution that considers the thousands of vehicles and requests at once. We can do this in our method because that first step enables us to understand and abstract the road network at a fine level of detail.

—Daniela Rus

The final product is what Rus calls an “anytime optimal algorithm,” which means that it gets better the more times you run it.


  • Alonso-Mora, Javier and Samaranayake, Samitha and Wallar, Alex and Frazzoli, Emilio and Rus, Daniela (2016) “On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment,” Proceedings of the National Academy of Sciences



Sharing 6 to 10 passenger taxis could be an excellent way to greatly reduce traffic density in large city cores.

Equitable charging may be a challenge but it should be relatively easy to fix charges based on distance & time travelled. Price should be a lot less than todays single passenger taxis.

Automated drive e-minibuses could quickly learn typical passenger routines and be dispatched on time.


Taxis can be $2 to start and $3 per mile, this is why you use one only if you have to. Uber/Lyft are less, they use dynamic routing. eTaxis will cost even less thus would be used more.


We've had a 100% electric e-taxis fleet for the last 12 months. Cost for customers is the same but you get new clean quite e-cars + uniformed clean polite drivers.


Harvey , what taxi company ? in Manhattan?
The only way taxis will be competitive is to go upscale, electric ,criminal Screened, Scoffer service.
Just imagine what hyperloop would do to this model.


The new ALL ELECTRIC taxis organisation (TEO) operates in Montréal, QC. The same group control Diamond and Hochelaga (ICEV) taxis fleets but are operated separately.

UBER is legally operating in the same area but has to pay the same or equivalent taxes as regular taxis.

Since a high percentage of total operation cost is associated with good or bad drivers, all weather ADVs would be beneficial to owners and users?


Just my opinion, but when you have to rely on carpools to get to work, that's what Adam Smith called "fast hurtling backward" in terms of living standards. This all came about because our major cities are too dense to support their inner city transit and rail and bus lack the convenience of cars. San Francisco is rapidly becoming a social hellhole and an expensive one. The California High Speed Rail has been foisted on residents as some sort of solution, but what does SF have to do with LA, Las Vegas, or California?

People will revolt and move to the suburbs.

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