A Scalable Approach For Data-Driven Taxi Ride-Sharing Simulation

As urban population grows, cities face many challenges related to transportation, resource consumption, and the environment. Ride sharing has been proposed as an effective approach to reduce traffic congestion, gasoline consumption, and pollution. Despite great promise, researchers and policy makers lack adequate tools to assess tradeoffs and benefits of various ride-sharing strategies. Existing approaches either make unrealistic modeling assumptions or do not scale to the sizes of existing data sets. In this paper, we propose a real-time, data-driven simulation framework that supports the efficient analysis of taxi ride sharing. By modeling taxis and trips as distinct entities, our framework is able to simulate a rich set of realistic scenarios. At the same time, by providing a comprehensive set of parameters, we are able to study the taxi ride-sharing problem from different angles, considering different stakeholders' interests and constraints. To address the computational complexity of the model, we describe a new optimization algorithm that is linear in the number of trips and makes use of an efficient indexing scheme, which combined with parallelization, makes our approach scalable. We evaluate our framework and algorithm using real data - 360 million trips taken by 13,000 taxis in New York City during 2011 and 2012. The results demonstrate that our framework is effective and can provide insights into strategies for implementing city-wide ride-sharing solutions. We describe the findings of the study as well as a performance analysis of the model.