UberPOOL is a service allowing passengers to share their rides and split the cost of the trips with another Uber passenger headed in the same direction. The average time added to an uberPOOL trip is on average less than 5 minutes. Each uberPOOL rider can bring one additional passenger along. More information here.
Lyft’s similar service is Lyft Line.
Driving for Uber and Lyft since August 2013, I’ve given over 4,000 rides to passengers in Boston. I definitely agree with the commonly held estimate that half of Uber rides are UberPOOL and that the percentage has been steadily increasing month by month.
January 2, 2017
The uneasy relationship between taxi companies and ride-hailing startups got a little more tense today thanks to research from the Massachusetts Institute of Technology.
It’s no secret that carpooling can help reduce traffic. But what if everyone in New York turned on the carpool option in ride-hailing apps like Uber and Lyft? Researchers at MIT found that just 3,000 four-person cars could serve 98 percent of the demand currently being met by almost 14,000 taxis.
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) fed data from 3 million New York City taxi rides into an algorithm that imagined those rides as carpool requests via a ride-hailing app. The computer model routed and re-routed all those rides and found it would only take 3,000 cars to meet most of New York’s demand.
What’s more, this boost in ride efficiency comes at a minimal cost to convenience. In the MIT researchers’ models, no more than 5 minutes would be added to your trip in order to wait for a car along your desired route and pick up and drop off other passengers.
The team also looked at different vehicle options. They found that 3,000 two-person cars could serve 94 percent of demand and just 2,000 ten-person vans could handle 95 percent of demand. Their findings were published Monday in the Proceedings of the National Academy of Sciences.
The takeaway: if we were willing to add a few minutes to our trips and rub elbows with other passengers on the regular, we could take thousands of cars off crowded streets.
In case you’re wondering, the research was funded by grants from the Office of Naval Research and an MIT-Singapore partnership to explore the future of urban mobility. Not by Uber or Lyft.
It should also be mentioned that thousands of driving jobs would be eliminated if this computer model became reality. Even worse for professional drivers, the algorithm developed by the MIT researchers would work best when used with a fleet of autonomous vehicles that can easily be re-routed on the fly.
The MIT algorithm presumes ride-hailing apps that can handle a little more carpool complexity than they currently do, such as being able to modify a route or add ride requests continuously.
If the idea of carpools replacing taxis sounds silly to you, consider that in many cities half of the Uber rides taken are via the UberPool option. We could soon be getting much more serious about all those times we’ve been told to share the road.
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment
Edited by Michael F. Goodchild, University of California, Santa Barbara, CA, and approved November 22, 2016 (received for review July 20, 2016)
Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min.
Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.
1Present address: Delft Center for Systems and Control, Delft Technical University, 2628 CD, Delft, Netherlands.
- 2To whom correspondence should be addressed. Email: J.AlonsoMora@tudelft.nl.
Author contributions: J.A.-M., S.S., and D.R. designed research; J.A.-M., S.S., E.F., and D.R. performed research; J.A.-M. and A.W. contributed new reagents/analytic tools; J.A.-M., S.S., A.W., E.F., and D.R. analyzed data; and J.A.-M., S.S., A.W., E.F., and D.R. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1611675114/-/DCSupplemental.
Freely available online through the PNAS open access option.
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