Dynamic Timeframe and Anticipation-Based Migration: A Real-Time Framework for Ride-Sharing

Abstract

Efficient and reliable ride-sharing services have gained significant attention in recent years as a means to address traffic congestion and reduce environmental impact. This paper presents a novel real-time ride-sharing framework that incorporates dynamic timeframes and anticipation-based migration to enhance the overall experience for users. The proposed framework leverages advanced algorithms and intelligent systems to optimize the matching of riders and drivers, considering various factors such as proximity, destination compatibility, and anticipated travel patterns. By dynamically adjusting the timeframe for ride requests and introducing anticipation-based migration, the framework aims to minimize waiting times and maximize resource utilization. The effectiveness of the framework is evaluated through extensive simulations, demonstrating its ability to improve the efficiency, scalability, and reliability of ride-sharing systems. The results highlight the potential of the proposed approach to revolutionize the ride-sharing industry and contribute towards more sustainable urban transportation solutions.