262 research outputs found
STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction
Human mobility forecasting in a city is of utmost importance to
transportation and public safety, but with the process of urbanization and the
generation of big data, intensive computing and determination of mobility
pattern have become challenging. This study focuses on how to improve the
accuracy and efficiency of predicting citywide human mobility via a simpler
solution. A spatio-temporal mobility event prediction framework based on a
single fully-convolutional residual network (STAR) is proposed. STAR is a
highly simple, general and effective method for learning a single tensor
representing the mobility event. Residual learning is utilized for training the
deep network to derive the detailed result for scenarios of citywide
prediction. Extensive benchmark evaluation results on real-world data
demonstrate that STAR outperforms state-of-the-art approaches in single- and
multi-step prediction while utilizing fewer parameters and achieving higher
efficiency.Comment: Accepted by MDM 201
Deep spatio-temporal residual neural networks for road-network-based data modeling
Recently, researchers have introduced deep learning methods such as convolutional neural networks (CNN) to model spatio-temporal data and achieved better results than those with conventional methods. However, these CNN-based models employ a grid map to represent spatial data, which is unsuitable for road-network-based data. To address this problem, we propose a deep spatio-temporal residual neural network for road-network-based data modeling (DSTR-RNet). The proposed model constructs locally-connected neural network layers (LCNR) to model road network topology and integrates residual learning to model the spatio-temporal dependency. We test the DSTR-RNet by predicting the traffic flow of Didi cab service, in an 8-km2 region with 2,616 road segments in Chengdu, China. The results demonstrate that the DSTR-RNet maintains the spatial precision and topology of the road network as well as improves the prediction accuracy. We discuss the prediction errors and compare the prediction results to those of grid-based CNN models. We also explore the sensitivity of the model to its parameters; this will aid the application of this model to network-based data modeling
A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes
The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns
Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting
Spatio-temporal sequence forecasting is one of the fundamental tasks in
spatio-temporal data mining. It facilitates many real world applications such
as precipitation nowcasting, citywide crowd flow prediction and air pollution
forecasting. Recently, a few Seq2Seq based approaches have been proposed, but
one of the drawbacks of Seq2Seq models is that, small errors can accumulate
quickly along the generated sequence at the inference stage due to the
different distributions of training and inference phase. That is because
Seq2Seq models minimise single step errors only during training, however the
entire sequence has to be generated during the inference phase which generates
a discrepancy between training and inference. In this work, we propose a novel
curriculum learning based strategy named Temporal Progressive Growing Sampling
to effectively bridge the gap between training and inference for
spatio-temporal sequence forecasting, by transforming the training process from
a fully-supervised manner which utilises all available previous ground-truth
values to a less-supervised manner which replaces some of the ground-truth
context with generated predictions. To do that we sample the target sequence
from midway outputs from intermediate models trained with bigger timescales
through a carefully designed decaying strategy. Experimental results
demonstrate that our proposed method better models long term dependencies and
outperforms baseline approaches on two competitive datasets.Comment: ECAI 2020 Accepted, preprin
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand
vehicle sharing services. However, predicting passenger demand over multiple
time horizons is generally challenging due to the nonlinear and dynamic
spatial-temporal dependencies. In this work, we propose to model multi-step
citywide passenger demand prediction based on a graph and use a hierarchical
graph convolutional structure to capture both spatial and temporal correlations
simultaneously. Our model consists of three parts: 1) a long-term encoder to
encode historical passenger demands; 2) a short-term encoder to derive the
next-step prediction for generating multi-step prediction; 3) an
attention-based output module to model the dynamic temporal and channel-wise
information. Experiments on three real-world datasets show that our model
consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page
Modeling Dynamic Spatio-Temporal Correlations for Urban Traffic Flows Prediction
Prediction of traffic crowd movement is one of the most important component in many applications' domains ranging from urban management to transportation schedule. The key challenge of citywide crowd flows prediction is how to model spatial and dynamic temporal correlation. However, in recent years several studies have been done, but they lack the ability to effectively and simultaneously model spatial and temporal dependencies among traffic crowd flows. To address this issue, in this article a novel spatio-temporal deep hybrid neural network proposed termed STD-Net to forecast citywide crowd traffic flows. More specifically, STD-Net contains four major branches, i.e., closeness, period volume, weekly volume, and external branches, respectively. We design a residual neural network unit for each property to depict the spatio-temporal features of traffic flows. For various branches, STD-Net provides distinct weights and then combines the outputs of four branches together. Extensive experiments on two large-scale datasets from New York bike and Beijing taxi have demonstrated that STD-Net achieves competitive performances the existing state-of-the-art prediction baselines
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