A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction

Li, Guanyao; Njoo, Gunarto Sindoro; Zhong, Shuhan; Chan, Sixian; Hung, Chih‐Chieh; Peng, Wen-Chih

doi:10.1109/icde53745.2022.00058

Cited by 16 publications

(2 citation statements)

References 38 publications

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“…Another study by Li et al [76] proposed a data-driven spatial-temporal graph neural network, called STGNN-DJD, to solve the bike demand and supply prediction problem by integrating two spatial-temporal graphs referred to as the flowconvoluted graph and the pattern correlation graph. These graphs were used to represent the flow relationships between stations at various time slots and the dynamic demand-supply patterns between stations, respectively.…”

Section: B Demand Predictionmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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Section: B Demand Predictionmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…A lot of insights related to public traffic could be provided to governments and companies via traffic flow forecasting. However, traffic flow forecasting is more challenging than other time series forecasting tasks, as the future flow at one traffic road node depends on both the historical flows in time dimension and other nodes close in spatial dimension [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

QuoGNN: Quotient Graph Neural Network for Urban Flow Forecasting

Gou

Han

Zhang

2022

2022 IEEE International Conference on Big Data (Big Data)

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Urban traffic flow prediction plays a crucial role in smart city management. Since the flow volume of one road (treated as a node in a traffic network) in future time units (after time t) depends on the historical volume (before and including time t) of this road (node) itself and its neighboring roads (nodes), the traffic flow prediction problem has recently been studied by utilizing a spatial-temporal adjacency matrix (AM) of traffic nodes constructed from the historical traffic and node connections. The construction of AM is often based on statistical traffic information before t, instead of using the volume at individual time unit level. In addition, the spatial and temporal relations between traffic nodes are manually fused in AM, rather than in a trainable fusion. In order to conquer these issues, we propose a trainable context enhanced similarity graph, which fuses the unit-level similarity of traffic time series and multiple inter-node contextual relations through a learnable embedding model. In addition, a Quotient Neural Network is proposed to perceive the explicit relation among short-memory flow values and facilitate the forecasting. Based on the two modules, we propose a novel Quotient Graph Neural Network (QuoGNN). Experiments on four real-world benchmark datasets demonstrate the superior performance of our proposed model over the state-of-the-art baselines on multiple evaluation metrics. The implementation of the model and datasets are available 1 .Index Terms-traffic flow forecasting; graph neural network; urban computing * Equal Contribution• Corresponding Author 1 https://github.com/gpxlcj/QuoGNN

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