Real-time ride-sharing framework with dynamic timeframe and anticipation-based migration

The emergence of large-scale online ride-sharing platforms has substantially reformed the way people travel. Ride-sharing plays a very positive role in alleviating traffic congestion, reducing carbon emissions, and improving travel efficiency by sharing transportation resources. However, it is challenging to design an effective real-time ride-sharing framework due to the complex dynamics of a real-life environment. Most proposed models have difficulties in following the density variation of commuters in different time periods. Moreover, the existing matching methods have limitations related to large-scale instances. Therefore, in this paper, we propose a real-time ride-sharing framework with a dynamic timeframe and anticipation-based migration. The problem is formally modeled and two concrete approaches are introduced to dynamically segment timeframes and migrate commuters to future timeframes based on historical data. To solve this problem, we propose a multi-strategy solution graph search heuristic that can easily deal with large-scale instances and provide high-quality solutions. We also conduct extensive experiments on real-world datasets to demonstrate the efficiency and effectiveness of the proposed framework.