“Ride-sharing services have enormous potential for positive societal impact with respect to congestion, pollution and energy consumption,” says Rus. Unlike previous studies, which were limited to static routes with two or three riders per vehicle, the new model was able to adjust to changing conditions on the fly, altering the routes when new passengers appeared, and selecting vehicles of different sizes when needed. “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,” said Daniela Rus, a professor of computer science at CSAIL who led the study. On the flipside, such algorithms could be a boost to the US economy as a whole, which loses an estimated $121 billion annually (about 1 percent of the US GDP) as a result of the 5.5 billion hours people send sitting in traffic (to say nothing of the 2.9 billion gallons of fuel that is also wasted). These estimates do not even consider the cost of other potential negative externalities such as the vehicular emissions (greenhouse gas emissions and particulate matter) (2), travel-time uncertainty (3), and a higher propensity for accidents (4). A new study published in the Proceedings of the National Academy of Sciences shows that 3,000 four-passenger cars could serve 94 percent of demand in New York City, while 2,000 ten-person vehicles could fulfill 95 percent of demand—and with an average wait time of just 2.7 minutes and a trip delay of about 3.5 minutes. We also extend the method to proactively rebalance the vehicle fleet by moving idle vehicles to areas of high demand. New York City has about 14,000 taxi medallion licenses, but the new research suggests the city’s transportation infrastructure could function just as efficiently with a fleet one-quarter of this size. “What’s more, the system is particularly suited to autonomous cars, since it can continuously reroute vehicles based on real-time requests.” 
 
 This is bad news for NYC’s already embattled taxi drivers, who have spent the last few years fighting Uber’s rapid expansion in the city. The system works by creating a graph of all of the requests and all of the vehicles. Normally, ride-sharing systems require that different users be on the same route in order to match them for carpooling and need all the requests to be finalized before creating a carpool route. 1C); solving an ILP to compute the best assignment of vehicles to trips (Fig. Results We assess the performance of a MoD fleet controller using the proposed algorithm, against real data from an arbitrarily chosen representative week, from 0000 hours Sunday, May 5, 2013, to 2359 hours, Saturday May 11, 2013, from the publicly available dataset of taxi trips in Manhattan, New York City (23). CSAIL’s algorithm, by contrast, rematches users to different vehicles in real-time to optimize the system while also analyzing where high occupancy vehicles can be of the most benefit based on metrics like cost, time and the inconvenience to the passenger. When the team adjusted the model for different vehicle types, it found that 3000 two-person vehicles could serve 94 percent of NYC’s demand and 2000 ten-person vehicles could cover 95 percent of NYC’s demand. To achieve 98% service rate, a fleet of just 2,000 vehicles with a capacity of 10 was required, which represents a reduction of the fleet size to 15% of the active taxi fleet in New York City. We analyze service rate (percentage of requests serviced) (A), average in car delay δ − ω (B), average waiting time ω (C), average distance traveled by each vehicle during a single day (D), percentage of shared rides (number of passengers who shared a ride divided by the total number of picked-up passengers) (E), and average computational time for a 30-s iteration of the method (F), in a 24 core 2.5 GHz machine, including computation of the RV-graph, computation of the RTV-graph, ILP assignment, rebalancing, and data writing (higher levels of parallelization would drastically reduce this computational time). Even if some fraction of the rides are shared, there will be fewer vehicles on the roads. If a maximum delay of 5 min or more (comparable to the time spent retrieving a car from parking) is allowed, higher-capacity vehicles (i) increase the service rate significantly, (ii) reduce the waiting time, and (iii) reduce the distance traveled by each vehicle.
– It's no wonder New York City traffic is so awful, with 13,600 taxis navigating the streets. But just 3,000 sedans could accomplish about as much, according to a new study. Using a computer formula, MIT researchers discovered that just 3,000 four-person vehicles could provide for 98% of the ride demands of Manhattan residents if everyone opted to share rides, while 2,000 10-passenger vans could account for 95%, reports CNNMoney. The model, particularly suited to self-driving cars since they would be able to automatically re-route themselves when necessary, used data from 3 million taxi rides taken over a week in Manhattan in March 2013 to pose real-time requests, then grouped passengers together based on destinations and sent empty vehicles to await requests in high-traffic areas, which made the system 20% faster, per Gizmodo. The result also meant less congestion and pollution. The downside was that passengers theoretically waited 2.8 minutes for a vehicle, and spent an extra 3.5 minutes traveling because of additional stops. If adopted, such a system would also put thousands of taxi drivers out of work, but "drivers will be able to make the same amount of money working shorter shifts, while the customers will get the same level of service, cities will have fewer cars on roads, the commute experience will be better for everyone ... and the air is cleaner," the study author says. Motherboard reports it could also save some $121 billion lost annually "as a result of the 5.5 billion hours people spend sitting in traffic (to say nothing of the 2.9 billion gallons of fuel that is also wasted)." (A smart traffic light might also improve traffic.)