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Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems

机译：使用关于流动的图形结构信息增强自行车共享系统中的短期需求预测

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Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the "tidal flows" of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities. Despite recent advances in machine learning approaches (e.g. deep neural networks) and in short-term traffic demand predictions, relatively few studies have examined this issue using a feature engineering approach to inform model selection. This research extracts novel time-lagged variables describing graph structures and flow interactions from real-world bike usage datasets, including graph node Out-strength, In-strength, Out-degree, In-degree and PageRank. These are used as inputs to different machine learning algorithms to predict short-term bike demand. The results of the experiments indicate the graph-based attributes to be more important in demand prediction than more commonly used meteorological information. The results from the different machine learning approaches (XGBoost, MLP, LSTM) improve when time-lagged graph information is included. Deep neural networks were found to be better able to handle the sequences of the time-lagged graph variables than other approaches, resulting in more accurate forecasting. Thus incorporating graph-based features can improve understanding and modelling of demand patterns in urban areas, supporting bike-sharing schemes and promoting sustainable transport. The proposed approach can be extended into many existing models using spatial data and can be readily transferred to other applications for predicting dynamics in mass transit systems. A number of limitations and areas of further work are discussed.
机译：短期需求预测对于管理运输基础设施来说是重要的，特别是在中断时期或围绕新发展。许多自行车分享计划面临管理服务提供的挑战，并由于旅行和使用的“潮流”而导致的自行车舰队重新平衡。对于他们来说，在精细的时空粒度具有精确的旅行需求预测至关重要。尽管最近的机器学习方法（例如，深度神经网络）和短期交通需求预测，但使用特征工程方法可以向模型选择进行相对较少的研究检查了这个问题。本研究提取了描述图形结构和来自现实世界自行车使用数据集的图形结构和流量交互的新型时间滞后变量，包括图形节点超出强度，强度，up-degraid，度和PageRank。这些被用作不同机器学习算法的输入，以预测短期自行车需求。实验结果表明，基于图形的属性在需求预测中更重要，而不是更常用的气象信息。当包括时间滞后图信息时，来自不同机器学习方法（XGBoost，MLP，LSTM）的结果。发现深神经网络更好地能够处理时间滞后的图变量的序列，而不是其他方法，导致更准确的预测。因此，纳入基于图形的特征​​可以改善城市地区需求模式的理解和建模，支持自行车共享计划和促进可持续运输。所提出的方法可以使用空间数据扩展到许多现有模型，并且可以容易地传送到其他应用程序以预测传质系统中的动态。讨论了许多限制和进一步工作的领域。

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