MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction

Yin, Du; Jiang, Renhe; Deng, Jiewen; Li, Yongkang; Xie, Yi; Wang, Zhongyi; Zhou, Yifan; Song, Xuan; Shang, Jedi S.

doi:10.1007/s10707-022-00466-1

Cited by 26 publications

(6 citation statements)

References 34 publications

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“…It is worth mentioning that our proposed model integrates multiple graphs to cover the spatiotemporal complexity inherent in different inter-station connections, resulting in superior prediction accuracy and the best model fit. Moreover, we also compared the experimental results of models STGCN [33], DCRNN [33], GBDT [22], TSTFN [34], and MGSTCN [35] with the Hangzhou Metro dataset, which indicated that the proposed model has better predictive performance. As shown in Tables 6 and 7, deep learning methods perform better than traditional models in predicting metro passenger flow at different time granularities.…”

Section: Comparative Analysis Of Different Modelsmentioning

confidence: 99%

“…Moreover, we also compared the experimental results of models STGCN [33], DCRNN [33], GBDT [22], TSTFN [34], and MGSTCN [35] with the Hangzhou Metro dataset, which indicated that the proposed model has better predictive performance. We also assessed the performance of the proposed model at different time granularities, as illustrated in Figure 17.…”

Section: Comparative Analysis Of Different Modelsmentioning

confidence: 99%

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Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Li,

Liu,

2023

Applied Sciences

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Accurate prediction of metro passenger flow helps operating departments optimize scheduling plans, alleviate passenger flow pressure, and improve service quality. However, existing passenger flow prediction models tend to only consider the historical passenger flow of a single station while ignoring the spatial relationships between different stations and correlations between passenger flows, resulting in low prediction accuracy. Therefore, a multi-scale residual depthwise separable convolution network (MRDSCNN) is proposed for metro passenger flow prediction, which consists of three pivotal components, including residual depthwise separable convolution (RDSC), multi-scale depthwise separable convolution (MDSC), and attention bidirectional gated recurrent unit (AttBiGRU). The RDSC module is designed to capture local spatial and temporal correlations leveraging the diverse temporal patterns of passenger flows, and then the MDSC module is specialized in obtaining the inter-station correlations between the target station and other heterogeneous stations throughout the metro network. Subsequently, these correlations are fed into AttBiGRU to extract global interaction features and obtain passenger flow prediction results. Finally, the Hangzhou metro passenger inflow and outflow data are employed to assess the model performance, and the results show that the proposed model outperforms other models.

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Section: Comparative Analysis Of Different Modelsmentioning

confidence: 99%

Section: Comparative Analysis Of Different Modelsmentioning

confidence: 99%

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Li,

Liu,

2023

Applied Sciences

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“…The advancement of spatio-temporal graph neural networks has given rise to the field of traffic prediction, and it has been successfully applied to several traffic-related tasks, such as demand prediction [19], flow prediction [20,21] and driver maneuver anticipation [22]. Generally, various ST graph implementations of traffic prediction can be classified into three groups based on how to process time series: CNN-based, RNNbased and transformer-based approaches.…”

Section: Spatial-temporal Graph For Traffic Predictionmentioning

confidence: 99%

Traffic Flow Prediction on Graph with Node-level Curriculum Scheduler

Zhang

Wang

Fan

et al. 2023

Preprint

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Traffic flow prediction is a critical part of the intelligent transportation system , and many schemes have been proposed for accurate flow prediction based on traffic sensors. However, existing approaches mainly capture spatial-temporal (ST) dependencies from the traffic graph and train their models using randomly ordered data. This approach ignores the fact that the modeling difficulty of each sensor/node from the traffic graph can vary significantly due to its spatial dependencies and temporal trends, leading to unreliable and unstable predictions for each traffic sensor in real-world applications. These issues motivated us to design a more effective training process for sensors with different levels of difficulty. The ST modeling difficulty stems from two factors: (I) deviations between the normal component and abnormal component in the time sequences; and (II) deviations between the learned relation and the explicit relation of a predetermined graph in the spatial dependencies. In this case, we argue that a well-designed curriculum with an easy-to-difficult order can benefit the training of ST models. This approach draws inspiration from the human learning principle that models should first learn straightforward concepts before tackling more difficult ones. To implement this approach, this paper proposes a method to measure the ST difficulty at the node level of traffic graphs from both spatial and temporal aspects. Then, it conducts a curriculum during the ST model training procedure. Specifically, 1 based on the ST difficulty score, the model training starts from a subgraph whose nodes are considered ”easy” because of their relatively consistent spatial relationships and regular temporal patterns. After that, more difficult nodes are gradually added to the subgraph and participate in the following training stages. We have conducted extensive experiments on two public traffic datasets, which show the superiority of our proposed curriculum scheduler over multiple state-of-the-art ST models.

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“…Identifying the future state of a system via forecasting has been applied in a wide range of disciplines such as economics [27], energy and environmental studies [3,16,22,36], epidemiology [21,46] and transport [13,32,41,49,55], among others. Forecasting is typically undertaken with the use of statistical and machine learning models, which may be embedded in an end-to-end forecasting system.…”

Section: Related Workmentioning

confidence: 99%

Foresight Plus: Scalable Serverless Real-Time Spatio-Temporal Traffic Forecasting

Oakley,

Conlan,

Demirci

et al. 2023

Preprint

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Building a real-time, cost-effective, spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network management. Most forecasting research focuses on average prediction quality, with less attention paid to building practical pipelines and achieving timely and accurate forecasts when the network is under heavy load. Additionally, transport authorities need to leverage dynamic data sources (e.g., scheduled roadworks) and vehicle-level flow data, while also supporting ad-hoc inference workloads at low cost. The cloud-based system Foresight, developed in collaboration with Transport for the West Midlands (TfWM), is able to ingest, aggregate and process streamed traffic data, as well as dynamic urban events/flow data to produce regularly scheduled forecasts with high accuracy. In this work, we extend our system with several novel enhancements. First, we present an efficient method for extending the forecasting scale, enabling transport managers to predict traffic patterns further into the future than existing methods. In addition, we augment the existing inference architecture with a new, fully serverless design. This offers a more cost-effective inference solution, which seamlessly handles sporadic inference workloads over multiple forecasting models. We observe that Graph Neural Network (GNN) forecasting models are robust to extensions of the forecasting scale, achieving consistent (and sometimes even improved) performance up to 24 hours ahead. This is in contrast to the 1 hour forecasting horizons popularly considered in the literature. Further, our serverless inference solution is shown to be significantly more cost-effective than provisioned alternatives in appropriate use-cases. We identify the optimal memory configuration of serverless resources to achieve an attractive cost-to-performance ratio.

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MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction

Cited by 26 publications

References 34 publications

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Traffic Flow Prediction on Graph with Node-level Curriculum Scheduler

Foresight Plus: Scalable Serverless Real-Time Spatio-Temporal Traffic Forecasting

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