Carpooling Apps could reduce Traffic 75%

Transportation studies put the annual cost of congestion at $160 billion, which includes seven billion hours of time lost to sitting in traffic and an extra three billion gallons of fuel burned.

One way to improve traffic is through ride-sharing – and a new MIT study suggests that using carpooling options from companies like Uber and Lyft could reduce the number of vehicles on the road 75 percent without significantly impacting travel time.

Led by Professor Daniela Rus of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers developed an algorithm that found that 3,000 four-passenger cars could serve 98 percent 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 percent. 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 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.