Data Driven Simulation Framework for Taxi Ride Sharing

Abstract

In the modern era vehicles are increasing exponentially with respect to its population. The urban cities are facing many challenges in transportation and energy consumption. The foremost approach will be taxi ride-sharing which effectively reduces traffic congestion, gasoline consumption, and pollution. Our proposed method will simulate a real-time data-driven framework for analysing the taxi ride-sharing in various scenarios. In this approaches the taxies and trips are modelled as separate entities for simulating a rich set of realistic scenarios. A new optimization algorithm is described to address the computational complexity and scalability is achieved by an efficient indexing scheme combined with parallelization. The framework is evaluated using a real-time streaming information obtained from the user.