The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing -- setting prices to customer requests for taxis; and (b) matching -- assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this paper, we develop a novel framework that handles the pricing and matching problems together, while also considering the future impact of the pricing and matching decisions. In our experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly improve revenue (up to 17% and on average 6.4%) in a sustainable manner by reducing the number of vehicles (up to 14% and on average 10.6%) required to obtain a given fixed revenue and the overall distance travelled by vehicles (up to 11.1% and on average 3.7%). That is to say, we are able to provide an ideal win-win scenario for all stakeholders (customers, drivers, aggregator, environment) involved by obtaining higher revenue for customers, drivers, aggregator (ride pooling company) while being good for the environment (due to fewer number of vehicles on the road and lesser fuel consumed).

翻译：按需拼车的普及得益于其为乘客（更低价格）、出租车司机（更高收入）、环境（因车辆减少而降低碳足迹）以及优步等聚合平台（更高收益）带来的多重效益。为实现这些效益，需有效解决两个相互关联的关键挑战：（a）定价——为出租车请求设定价格；（b）匹配——将接受定价的乘客分配至出租车/车辆。传统上，这两项挑战均通过短视策略（仅考虑当前请求）单独研究，而未考虑当前匹配对处理未来请求的影响。本文提出了一种新颖框架，该框架协同处理定价与匹配问题，同时考虑定价与匹配决策对未来影响的延伸效应。基于真实出租车数据集的实验结果表明，本框架能够通过可持续方式显著提升收益（最高提升17%，平均提升6.4%），同时减少获取给定固定收益所需的车辆数量（最高减少14%，平均减少10.6%）及车辆总行驶里程（最高减少11.1%，平均减少3.7%）。换言之，我们为所有利益相关方（乘客、司机、聚合平台、环境）构建了理想的共赢场景：在提升乘客、司机及聚合平台（拼车公司）收益的同时，通过减少道路车辆数量与燃料消耗实现环境友好。