Optimal Demand-Aware Ride-Sharing Routing

We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, in which the driver of a vehicle determines a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing economical and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need of (i) meeting travel delay requirements of customers, and (ii) making online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that the demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. We show that the problem is NP-Complete in the weak sense. We exploit the two-stage structure to design an optimal solution with pseudo-polynomial time complexity, which makes it amenable for practical implementation. We carry out extensive simulations based on real-world travel demand traces of Manhattan. The results show that using our demand-aware solution instead of the conventional greedy-routing scheme increases the driver's revenue by 10%. The results further show that as compared to the case without ride-sharing, our ride-sharing solution reduces the customers' payment by 9% and the total vehicle travel time (indicator of greenhouse gas emission) by 17%. The driver can also get 26% extra revenues per slot by participating in ride-sharing.