In recent years, ridesharing platforms have become a prominent mode of transportation for the residents of urban areas. As a fundamental problem, route recommendation for these platforms is vital for their sustenance. The works done in this direction have recommended routes with higher passenger demand. Despite the existing works, statistics have suggested that these services cause increased greenhouse emissions compared to private vehicles as they roam around in search of riders. This analysis provides finer details regarding the functionality of ridesharing systems and it reveals that in the face of their boom, they have not utilized the vehicle capacity efficiently. We propose to overcome the above limitations and recommend routes that will fetch multiple passengers simultaneously which will result in increased vehicle utilization and thereby decrease the effect of these systems on the environment. As route recommendation is NP-hard, we propose a k-hop-based sliding window approximation algorithm that reduces the search space from entire road network to a window. We further demonstrate that maximizing expected demand is submodular and greedy algorithms can be used to optimize our objective function within a window. We evaluate our proposed model on real-world datasets and experimental results demonstrate superior performance by our proposed model.

翻译：近年来，共享出行平台已成为城市居民的主要交通方式。作为基础性问题，路径推荐对这些平台的可持续发展至关重要。已有研究主要推荐乘客需求较高的路线。然而现有统计表明，由于车辆为寻找乘客而空驶，共享出行服务产生的温室气体排放量甚至超过私家车。本研究深入剖析了共享出行系统的运行机制，发现在行业高速发展的背景下，车辆运力并未得到有效利用。我们提出通过优化路线推荐，实现同时搭载多名乘客的服务模式，从而提升车辆利用率并降低系统对环境的影响。由于路径推荐属于NP-hard问题，我们提出基于k跳的滑动窗口近似算法，将搜索空间从整个路网缩小至局部窗口。进一步证明期望需求最大化具有次模性，可采用贪心算法在窗口内优化目标函数。基于真实数据集的实验结果表明，所提模型具有显著优越性能。