[论文解读] Dynamic Demand Prediction for Expanding Electric Vehicle Sharing Systems: A Graph Sequence Learning Approach.

Man Luo, Hongkai Wen+4 more|arXiv (Cornell University)|Mar 10, 2019

Transportation and Mobility Innovations参考文献 17被引用 6

一句话总结

本文提出一种图序列学习方法，用于在扩展的电动汽车（EV）共享系统中进行动态需求预测，通过图卷积网络实现局部时间编码和动态空间编码，以建模时间与空间依赖性。在真实上海EV共享数据上，该方法相较最先进方法性能最高提升三倍。

ABSTRACT

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe. During their fast expansion, one fundamental determinant for success is the capability of dynamically predicting the demand of stations as the entire system is evolving continuously. There are several challenges in this dynamic demand prediction problem. Firstly, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and support for decision making. Secondly, for the stations to be deployed, there is no historical record or additional mobility data available to help the prediction of their demand. Finally, the impact of deploying/closing stations to the remaining stations in the system can be very complex. To address these challenges, in this paper we propose a novel dynamic demand prediction approach based on graph sequence learning, which is able to model the dynamics during the system expansion and predict demand accordingly. We use a local temporal encoding process to handle the available historical data at individual stations, and a dynamic spatial encoding process to take correlations between stations into account with graph convolutional neural networks. The encoded features are fed to a multi-scale prediction network, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach on real-world data collected from a major EV sharing platform in Shanghai for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the rapidly expanding EV sharing system.

研究动机与目标

• 解决在持续扩展过程中，新站点缺乏历史数据的EV共享系统需求预测挑战。
• 实时建模站点部署与关闭对剩余站点造成的复杂时空影响。
• 实现对演化系统中长期预期需求与短期即时需求的精准预测。
• 克服现有静态或分阶段预测模型在动态系统变化期间失效的局限性。

提出的方法

• 采用局部时间编码过程，从单个站点的历史需求数据中提取时间模式。
• 基于图卷积网络设计动态空间编码机制，以建模站点间不断演化的空间相关性。
• 将时间与空间编码整合至多尺度预测网络，实现对不同时间范围需求的预测。
• 在新增或移除站点时动态更新图结构，确保系统演化过程中空间依赖性的建模。
• 利用图卷积网络捕捉非线性空间依赖性，即使新站点无先前出行或需求记录亦可实现。
• 在真实世界数据上端到端训练模型，联合优化长期与近未来需求预测。

实验结果

研究问题

• RQ1 在缺乏历史数据的前提下，如何准确预测EV共享系统中尚未部署的新站点的需求？
• RQ2 在动态系统中，站点的部署与关闭在多大程度上影响现有站点的需求模式？
• RQ3 统一模型能否有效预测扩展EV共享网络中的长期预期需求与短期即时需求？
• RQ4 所提出的图序列学习方法在系统扩展期间，相较现有最先进方法在预测性能上表现如何？

主要发现

• 所提模型在预测快速扩展的EV共享系统需求方面，相较最先进方法性能最高提升三倍。
• 在某大型EV共享平台上海真实数据集上为期一年的实验中，该模型显著优于现有方法。
• 动态空间编码的集成使模型在系统演化过程中（包括新站点部署）仍能准确建模空间依赖性。
• 多尺度预测网络成功高精度捕捉了需求中的长期趋势与短期波动。
• 局部时间编码过程能有效从单个站点有限的历史数据中提取有意义的时间模式。
• 该模型在无任何先前运营历史的站点上仍保持稳健性能，展现出其在主动需求预测方面的能力。