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Rebalance Modern Bike Sharing System: Spatio-Temporal Data Prediction and Path Planning for Multiple Carriers

机译：重新平衡现代自行车共享系统：多个载波的时空数据预测和路径规划

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Modern bike sharing system, in which bikes can be parked freely, extends the flexibility of traditional bike sharing system and thus has greatly facilitated urban transportation. However, the balance of such system is often broken by the user behaviors. And how to manage a large number of bikes which parked randomly in a city is a difficult problem. To tackle this problem, we propose a two-step solution. First, we deal with the bike trajectory data and design the Spatial-Temporal Bike Flow Prediction (ST-BFP) model, which is a convolutional network based on residual framework with history external factors to predict the bike flows. Second, to make the system return to balance state as soon as possible, we propose an Improved Local Search Algorithm (ILSA) for path planning with multiple carriers based on forecast result, which schedules multiple carriers in real time to complete the rebalance task collaboratively. Finally, we validate our model and algorithm via real-data based experiment. Experimental results demonstrate that our method can balance the entire system efficiently.
机译：现代自行车分享系统，可自行车停放，延长了传统自行车共享系统的灵活性，从而促进了城市交通。但是，这种系统的平衡通常由用户行为打破。以及如何管理在城市随机停放的大量自行车是一个难题。为了解决这个问题，我们提出了一项两步的解决方案。首先，我们处理自行车轨迹数据，并设计空间 - 时空自行车流量预测（ST-BFP）模型，该模型是基于历史外部因素的剩余框架的卷积网络，以预测自行车流动。其次，为了使系统尽快返回余额状态，我们提出了一种改进的本地搜索算法（ILSA），用于基于预测结果的多个载波的路径规划，该路径规划是实时调度多个载波，以完成重新平衡任务。最后，我们通过基于实际数据的实验验证了我们的模型和算法。实验结果表明，我们的方法可以有效地平衡整个系统。

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