Price-aware real-time ride-sharing at scale: an auction-based approach
Price-aware real-time ride-sharing at scale: an auction-based approach
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DOI：
10.1145/2996913.2996974
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
M. Asghari;Dingxiong Deng;C. Shahabi;Ugur Demiryurek;Yaguang Li
中科院分区：
文献类型：
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作者：
M. Asghari;Dingxiong Deng;C. Shahabi;Ugur Demiryurek;Yaguang Li
实时共乘能够实现乘客和司机之间的即时匹配（甚至在途中），由于其环境和社会效益，它是一个重要的问题。随着许多乘车共享平台的出现（例如，Uber和Lyft），设计一个可扩展的框架，根据乘客和司机的各种约束条件进行匹配，同时最大限度地提高平台的整体利润，成为一种与众不同的商业策略。这种框架的关键挑战是满足系统中的两种类型的用户，例如，减少了乘客和驾驶员的行驶距离。然而，大多数现有的方法只关注最小化驾驶员的总行驶距离，这并不总是等同于更短的行程。因此，我们提出了一个公平的定价模型，同时满足乘客和司机的约束和愿望（制定为他们的配置文件）。特别是，我们引入了一个分布式的基于拍卖的框架，每个司机的移动的应用程序自动出价的每一个附近的请求，考虑到许多因素，如司机和乘客的个人资料，他们的行程，定价模型，以及当前的乘客在车辆中的数量。随后，服务器确定出价最高的人并将骑手分配给该司机。我们表明，这个框架是可扩展的和高效的，每秒处理数百个任务，在数千个驱动程序的存在。我们比较我们的框架与国家的最先进的方法在工业界和学术界通过实验纽约市的出租车数据集。我们的结果表明，我们的框架可以同时匹配更多的车手的司机（即，更高的服务率）。此外，我们的框架工作安排更短的行程骑手（即，更好的服务质量）。最后，由于更高的服务率和更短的行程，我们的框架增加了乘车共享平台的整体利润。
Real-time ride-sharing, which enables on-the-fly matching between riders and drivers (even en-route), is an important problem due to its environmental and societal benefits. With the emergence of many ride-sharing platforms (e.g., Uber and Lyft), the design of a scalable framework to match riders and drivers based on their various constraints while maximizing the overall profit of the platform becomes a distinguishing business strategy. A key challenge of such framework is to satisfy both types of the users in the system, e.g., reducing both riders' and drivers' travel distances. However, the majority of the existing approaches focus only on minimizing the total travel distance of drivers which is not always equivalent to shorter trips for riders. Hence, we propose a fair pricing model that simultaneously satisfies both the riders' and drivers' constraints and desires (formulated as their profiles). In particular, we introduce a distributed auction-based framework where each driver's mobile app automatically bids on every nearby request taking into account many factors such as both the driver's and the riders' profiles, their itineraries, the pricing model, and the current number of riders in the vehicle. Subsequently, the server determines the highest bidder and assigns the rider to that driver. We show that this framework is scalable and efficient, processing hundreds of tasks per second in the presence of thousands of drivers. We compare our framework with the state-of-the-art approaches in both industry and academia through experiments on New York City's taxi dataset. Our results show that our framework can simultaneously match more riders to drivers (i.e., higher service rate) by engaging the drivers more effectively. Moreover, our frame-work schedules shorter trips for riders (i.e., better service quality). Finally, as a consequence of higher service rate and shorter trips, our framework increases the overall profit of the ride-sharing platforms.