Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks

Yao, Xin; Gao, Yong; Zhu, Daoyun; Manley, Ed; Wang, Jiaoe; Liu, Yu

doi:10.1109/tits.2020.3003310

Cited by 57 publications

(18 citation statements)

References 61 publications

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“…Recently, the GCN model has received widespread attention, extending convolutional operations to non-Euclidean domains. It has been gradually applied in image classification and traffic road networks and has demonstrated that the spatial structure captured by GCNs improves the forecasting accuracy [26].…”

Section: Data Modelling 231 Spatial Dependence Modeling By Gcnmentioning

confidence: 99%

A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

Jiang

Luo

et al. 2022

Remote Sensing

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Landslide displacement prediction is crucial for the early warning of slope failure but remains a challenging task due to its spatiotemporal complexity. Although temporal dependency has been well studied and discussed, spatial dependence is relatively less explored due to its significant variations of the spatial structure of landslides. In this study, a novel graph convolutional incorporating GRU network (GC-GRU-N) is proposed and applied to landslide displacement forecasts. The model conducts attribute-augmented graph convolution (GC) operations on GNSS displacement data with weighted adjacency matrices and an attribute-augmented unit to combine features, including the displacements, the distance, and other external influence factors to capture spatial dependence. The output of multi-weight graph convolution is then applied to the gated recurrent unit (GRU) network to learn temporal dependencies. The related optimal hyper-parameters are determined by comparison experiments. When applied to two typical landslide sites in the Three Gorge Reservoir (TGR), China, GC-GRU-N outperformed the comparative models in both cases. The ablation experiment results also show that the attribute augmentation, which considers external factors of landslide displacement, can further improve the model’s prediction performance. We conclude that the GC-GRU-N model can provide robust landslide displacement forecasting with high efficiency.

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Section: Data Modelling 231 Spatial Dependence Modeling By Gcnmentioning

confidence: 99%

A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

Jiang

Luo

et al. 2022

Remote Sensing

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“…For instance, using a probabilistic model, Dai et al [35] reports an average MAPE of 13.26% for subway short-term passenger inflow in Zhengzhou City, China. Similarly, using convolutional neural networks, Yao et al [36] shows that its bestperforming model presents a MAPE of 24.3% for taxi flows in Beijing, China.…”

Section: Resultsmentioning

confidence: 99%

Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile

2022

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Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway. In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate negative externalities, such as congestion and pollution.

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“…We acknowledge there are other more sophisticated models based on deep learning techniques as explained in a recent survey [ 8 ]. Examples of works that model mobility using deep learning are Deep Gravity [ 56 ], SI-GCN (Spatial Interaction Graph Convolutional Network) [ 57 ] and GMEL (Geocontextual Multitask Embedding Learner) [ 58 ]. However, given the quantity of data needed to accurately train these models, we decided not to use them in this study where we have only an egocentric network for UK movements.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Modeling international mobility using roaming cell phone traces during COVID-19 pandemic

et al. 2022

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Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.

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Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks

Cited by 57 publications

References 61 publications

A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations

Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile

Modeling international mobility using roaming cell phone traces during COVID-19 pandemic

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