Online platforms for matching supply and demand, as part of the sharing economy, are becoming increasingly important in practice and have seen a steep increase in academic interest. Especially in the taxi/travel industry, platforms such as Uber, Lyft, and Didi Chuxing have become major players. Some of these platforms, including Didi Chuxing, operate two matching systems: Inform, where multiple drivers receive ride details and the first to respond is selected; and Assign, where the platform assigns the driver nearest to the customer. The Inform system allows drivers to select their destinations, but the Assign system minimizes driver-customer distances. This research is the first to explore: (i) how a platform should allocate customer requests to the two systems and set the maximum matching radius (i.e., customer-driver distance), with the objective to minimize the overall average waiting times for customers; and (ii) how taxi drivers select a system, depending on their varying degrees of preference for certain destinations. Using approximate queuing analysis, we derive the optimal decisions for the platform and drivers. These are applied to real-world data from Didi Chuxing, revealing the following managerial insights. The optimal radius is 1-3 kilometers, and is lower during rush hour. For most considered settings, it is optimal to allocate relatively few rides to the Inform system. Most interestingly, if destination selection becomes more important to the average driver, then the platform should not always allocate more requests to the Inform system. Although this may seem counter-intuitive, allocating too many orders to that system would result in many drivers opting for it, leading to very high waiting times in the Assign system.

作为共享经济的一部分，用于匹配供需的在线平台在实践中变得越来越重要，并且学术兴趣急剧增加。特别是在出租车/旅行业中，Uber，Lyft和Didi Chuxing等平台已成为主要参与者。其中一些平台（包括滴滴出行）运行两个匹配的系统：通知，多个驾驶员接收到行驶详细信息，并选择第一个响应的驾驶员；和分配，平台在其中分配离客户最近的驱动程序。Inform系统允许驾驶员选择目的地，而Assign系统使驾驶员与客户之间的距离最小化。这项研究是首次探索：（i）平台应如何将客户请求分配给两个系统并设置最大匹配半径（即客户-驾驶员距离），目的是最大程度地减少客户的总体平均等待时间；（ii）出租车司机如何选择系统，取决于他们对某些目的地的不同偏好程度。使用近似排队分析，我们得出平台和驱动程序的最佳决策。这些被应用于滴滴出行的真实数据，揭示了以下管理见解。最佳半径为1-3公里，在高峰时段较低。对于大多数考虑的设置，最好将相对较少的游乐设施分配给Inform系统。最有趣的是，如果目的地选择对于普通驾驶员而言变得更加重要，那么平台不应总是向Inform系统分配更多请求。尽管这似乎违反直觉，