As ride-hailing services have experienced significant growth, most research has concentrated on the dispatching mode, where drivers must accept the platform’s assigned trip requests. However, the broadcasting mode, in which drivers can freely choose their preferred orders from those broadcast by the platform, has received less attention. One crucial but challenging task in such a system is the determination of the matching radius, which usually varies across space, time, and real-time supply/demand characteristics. This study develops a Deep Learning-based Matching Radius Decision (DL-MRD) model that predicts key system performance metrics for a range of matching radii, which enables the ride-hailing platform to select an optimal matching radius that maximizes overall system performance according to real-time supply and demand information. To simultaneously maximize multiple system performance metrics for matching radius determination, we devise a novel multi-task learning algorithm named Weighted Exponential Smoothing Multi-task (WESM) learning strategy that enhances convergence speed of each task (corresponding to the optimization of one metric) and delivers more accurate overall predictions. We evaluate our methods in a simulation environment designed for broadcasting-mode-based ride-hailing service. Our findings reveal that dynamically adjusting matching radii based on our proposed approach significantly improves system performance.

中文翻译：

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按需乘车服务的动态匹配半径决策模型：一种深度多任务学习方法


随着网约车服务的显著增长，大多数研究都集中在派车模式上，即司机必须接受平台分配的行程请求。然而，司机可以从平台广播的订单中自由选择自己喜欢的订单的广播模式受到的关注较少。在这种系统中，一项关键但具有挑战性的任务是确定匹配半径，该半径通常随空间、时间和实时供需特性而变化。本研究开发了一种基于深度学习的匹配半径决策 （DL-MRD） 模型，该模型可预测一系列匹配半径的关键系统性能指标，使网约车平台能够根据实时供需信息选择最佳匹配半径，从而最大限度地提高整体系统性能。为了同时最大化多个系统性能指标以进行匹配半径确定，我们设计了一种新颖的多任务学习算法，称为加权指数平滑多任务 （WESM） 学习策略，该算法可以提高每个任务的收敛速度（对应于一个指标的优化）并提供更准确的整体预测。我们在为基于广播模式的叫车服务设计的模拟环境中评估我们的方法。我们的研究结果表明，基于我们提出的方法动态调整匹配半径可以显著提高系统性能。