Utility-Aware Dynamic Ridesharing in Spatial Crowdsourcing

With smartphones and geo-locating devices widely used around the world, ridesharing, as a main application field of spatial crowdsourcing, has been fast expanding its widespread adoption and potentially brings great benefits to human society and the environment. However, ridesharing has so far not been as popular as expected. A recent survey shows that notable obstacles come from concerns about social comfort and price fairness when riding with strangers. To defeat these obstacles, in this paper, we study a novel problem of utility-aware ride matching (URM) for dynamic ridesharing, where drivers and riders are matched in batches by considering their social comfort and price revenue. While the URM problem is of practical usefulness, we prove that this problem is Nondeterministic Polynomial Hard (NP-hard). To tackle the problem optimally and find exact answers, we present a novel bipartite matching algorithm by integrating an effective Driver-Rider Graph (DR-Graph) index. To balance the effectiveness and efficiency, we propose two efficient algorithms to solve the URM problem with only a small loss of utility. Leveraging a bounded Driver-Rider-group Graph (DRg-Graph) and several useful pruning bounds for matching utility and travel cost, we develop an
$\epsilon$
-refining algorithm to find an
$\epsilon$
-approximation matching utility of the optimal answer. Extensive experiments on real datasets showed that our proposed algorithms achieved effective matching results of social-pricing based utilities and the efficient performance of quick matching under various parameter settings.