The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of ride-sharing platforms and increased research on using autonomous vehicle technologies for routing and matching. The future of urban mobility for passengers and goods relies on leveraging new methods that minimize operational costs and environmental footprints of transportation systems. This article considers combining passenger transportation with goods delivery to improve vehicle-based transportation. We propose FlexPool: a distributed model-free deep reinforcement learning algorithm that jointly serves passengers & goods workloads by learning optimal dispatch policies from its interaction with the environment. The proposed algorithm pools passengers for a ride-sharing service and delivers goods using a multi-hop transit method. These flexibilities decrease the fleet’s operational cost and environmental footprint while maintaining service levels for passengers and goods. The dispatching algorithm based on deep reinforcement learning is integrated with an efficient matching algorithm for passengers and goods. Through simulations on a realistic multi-agent urban mobility platform, we demonstrate that FlexPool outperforms other model-free settings in serving the demands from passengers & goods. FlexPool achieves 30% higher fleet utilization and 35% higher fuel efficiency in comparison to (i) model-free approaches where vehicles transport a combination of passengers & goods without the use of multi-hop transit, and (ii) model-free approaches where vehicles exclusively transport either passengers or goods.

中文翻译：

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FlexPool：联合旅客和货物运输的分布式无模型深度强化学习算法

在线商品交付的增长导致城市汽车流量从最后一英里交付急剧增加。另一方面，随着乘车共享平台的成功以及对使用自动驾驶技术进行路线选择和匹配的研究日益增多，乘车共享一直在上升。旅客和货物的城市机动性的未来依赖于利用新方法来最大程度地降低运营成本和运输系统的环境足迹。本文考虑将客运与货物运输相结合，以改善基于车辆的运输。我们提出FlexPool：一种无需模型的分布式深度强化学习算法，该算法通过从与环境的交互中学习最佳调度策略来共同为旅客和货物工作量提供服务。所提出的算法将乘客分担共享乘车服务，并使用多跳运输方法运送货物。这些灵活性降低了机队的运营成本和环境足迹，同时保持了乘客和货物的服务水平。基于深度强化学习的调度算法与针对旅客和货物的高效匹配算法相集成。通过在现实的多代理城市移动性平台上进行的仿真，我们证明FlexPool在满足乘客和货物需求方面优于其他无模型设置。与（i）无模型方法相比，FlexPool的车队利用率提高了30％，燃油效率提高了35％，在这种情况下，车辆不使用多跳运输方式将乘客和货物组合起来进行运输，