Deep spatio-temporal residual neural networks for road-network-based data modeling

Ren, Yibin; Cheng, Tao; Zhang, Yang

doi:10.1080/13658816.2019.1599895

Cited by 34 publications

(25 citation statements)

References 36 publications

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“…The proposed ST-ResNet approach outperformed many well-known methods [16]. Ren et al proposed a deep spatio-temporal residual neural network for road network-based data modelling [17]. The proposed DSTR-RNet model constructed locally-connected neural network layers (LCNR) to model road network topology and integrated residual learning to model the spatio-temporal dependency, which maintained the spatial precision and topology of the road network, as well as improved the prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

et al. 2020

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Centralized publishing of big location data can provide accurate and timely information to assist in traffic management and for facilitating people to decide travel time and route, mitigate traffic congestion, and reduce unnecessary waste. However, the spatio-temporal correlation, non-linearity, randomness, and uncertainty of big location data make it impossible to decide an optimal data publishing instance through traditional methods. This paper, accordingly, proposes a publishing interval predicting method for centralized publication of big location data based on the promising paradigm of deep learning. First, the adaptive adjusted sampling method is designed to address the challenge of finding a reasonable release time via a prediction mechanism. Second, the Maximal Overlap Discrete Wavelet Transform (MODWT) is introduced for the decomposition of time series in order to separate different features of big location data. Finally, different deep learning models are selected to construct the entire framework according to various time-domain features. Experimental analysis suggests that the proposed prediction scheme is not only feasible, but also improves the prediction accuracy in contrast to the traditional deep learning mechanisms.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

et al. 2020

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“…As more researchers realize the advantages of such methods, recent years have witnessed their wide adoption by multidisciplinary studies, such as criminology [81], public health [82,83], municipal planning [84], and so forth. Many cases [85][86][87] have proved that region-based methods can outperform grid-based methods.…”

Section: Map-based Region Partitioning For Content Disseminationmentioning

confidence: 99%

Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval

Xiang

et al. 2019

IJGI

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Geospatial information is gaining immense interest and importance as we enter the era of highly developed transportation and communication. Despite the proliferation of cellular network and WiFi, on some occasions, users still face barriers to accessing geospatial data. In this paper, we design and implement a distributed prototype system with a delay/disruption tolerant network (DTN), named Geo-DMP, for cooperatively and opportunistically sharing and exchanging named geospatial contents in a device-to-device fashion. First of all, we construct a lightweight “content agent” module to bridge the gap between the application layer and the underlying DTN protocol stack. Afterwards, to profile the mobility history of users in practical geospatial environments, we present a map segmentation scheme based on road network and administrative subdivision information. Subsequently, we associate the regional movement history information with the content retrieval process to devise a hierarchical and region-oriented DTN routing scheme for both requests and responses. Finally, we conduct extensive experiments with real-world trajectories and complete implementations on the emulation platform composed of virtual machines. The experiments corroborate that Geo-DMP has the capability of successfully retrieving geospatial contents for users for most of the time under mobile circumstances with episodic connectivity. Moreover, en-route caches can be efficiently exploited to provision contents from multiple sources with less network resource consumption and shorter user-perceived latencies.

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“…Hence, several LSTM-based models have been proposed to predict the passenger flow volumes in isolated stations or traffic lines, which obtained better accuracy than traditional prediction methods [24][25][26]. However, LSTM is not good at capturing spatial dependency at the citywide level [27]. Convolutional neural networks (CNNs) have been applied for the prediction of citywide crowd flow [28].…”

Section: Introductionmentioning

confidence: 99%

Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

Han

Wang

Ren

et al. 2019

IJGI

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Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes-inflow and outflow-in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors.

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Deep spatio-temporal residual neural networks for road-network-based data modeling

Cited by 34 publications

References 36 publications

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval

Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

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