An Efficient Ride-Sharing Framework for Maximizing Shared Routes

Ride-sharing (RS) has great values in saving energy and alleviating traffic pressure. In this paper, we propose a new ride-sharing model, where each driver requires that the shared route percentage (SRP, the ratio of the shared route's distance to the driver's total traveled distance) exceeds her expected rate (e.g., 0.8) when sharing with a rider. We consider two variants of this problem. The first considers multiple drivers and multiple riders, and aims to compute a set of driver-rider pairs to maximize the overall SRP. We model this problem as the maximum weighted bigraph matching problem. We propose an effective exact algorithm, and an efficient approximate solution with error-bound guarantee. The second considers multiple drivers and a single rider and aims to find the top-k drivers for the rider with the largest SRP. We devise pruning techniques and propose a best-first algorithm to progressively selects drivers with high probability to be in the top-k results.

[1]  Zhifeng Bao,et al.  Crowdsourcing-based real-time urban traffic speed estimation: From trends to speeds , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[2]  David K. Smith Network Flows: Theory, Algorithms, and Applications , 1994 .

[3]  Guoliang Li,et al.  An Efficient Ride-Sharing Framework for Maximizing Shared Route , 2018, IEEE Transactions on Knowledge and Data Engineering.

[4]  Kian-Lee Tan,et al.  G-Tree: An Efficient and Scalable Index for Spatial Search on Road Networks , 2015, IEEE Transactions on Knowledge and Data Engineering.