Data-driven Collaborative Transportation

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Currently, cars are vastly underutilized. Firstly, most cars sit in parking spaces over ninety percent of the time. Secondly, even cars that are in use are underused due to their low occupancy relative to the number of seats available. This inefficient use of transportation and parking resources had to be tolerated to enjoy reliable, safe, and on-demand transportation. Today, however, Internet-connected devices allow us to move away from the car-ownership paradigm toward a shared transportation paradigm. Hence, more and more people are moving away from car-ownership to embrace public transportation, on-demand taxis (Uber, BlaBlaCar), and on-demand carsharing (Car2go). Carsharing refers to services like Car2Go, where multiple travelers use the same vehicle sequentially. Ridesharing refers to services such as UberPool and Blablacar, where multiple riders share the same vehicle concurrently on overlapping portions of their trips. Government policy-changes and electric vehicles are tackling the problems of pollution from vehicle emissions. Future autonomous vehicles aim to reduce the number of idle vehicles in parking lots by increasing their usage through carsharing. However, neither electric vehicles nor autonomous vehicles can reduce the number of cars traveling on roads and the resulting congestion delays. This Discovery program aims to reduce road congestion through ridesharing by using real-time data from Internet-connected devices (vehicles and smartphones) and real-time optimization to make ridesharing and routing decisions. In fact, the advent of autonomous vehicles will likely make ridesharing services even more accessible.