Title:  An Efficient Profit-Aware Large-Scale Vehicle Dispatch Framework for Ridesharing

for Vehicular Communications

Speaker: Mr. Benjamin Lartey

Date: Friday, March 31, 2023

On-demand ridesharing is a promising avenue to transform urban mobility by providing effective, low-cost transportation services to passengers in real-time, while increasing the profit made by transit companies. Most existing works suffer from the lack of balance between the overall system profit and the response time. The main focus is generally on optimizing

the profit and service rate (i.e., number of passengers served) to the neglect of response time. Therefore, in this work, we propose a computationally efficient (i.e., improved response time), dynamic vehicle dispatch framework while also optimizing the overall system’s profit. Our approach considers assigning vehicles to ride requests in a one-to-one manner. This assignment strategy improves the efficiency of the proposed algorithm. Extensive experiments conducted on the New York City taxicab open-source data demonstrates that the proposed framework is up to ten times faster than the state-of-the-art method and achieves comparable profit. Moreover, the proposed approach proved to be scalable and efficient when a large number of vehicles and requests are considered.