Spatial-temporal Dependencies Research Articles

Self-Attention Graph Convolution Residual Network for Traffic Data Completion

Complete and accurate traffic data is critical in urban traffic management, planning and operation. In fact, real-world traffic data contains missing values due to multiple factors, such as device outages and communication errors. For traffic data completion task, most of the existing methods are matrix/tensor completion methods, which usually enforce low rank constraint on traffic data matrix/tensor. But they neglect the graph structure of traffic data, resulting in low completion performance. Recently, graph convolutional networks have achieved remarkable results in traffic data forecasting due to their abilities of feature extraction and nonlinear fitting on arbitrarily graph-structured data. However, there are few studies based on graph neural networks for traffic data completion task. In this paper, we propose a traffic data completion model based on graph convolutional network model to impute missing values from the perspective of deep learning. This model utilizes graph convolution to model the local spatial dependency. As for global spatial dependency and temporal dependency, this model incorporates self-attention mechanism, which is applied in the spatial and temporal dimensions respectively. The experimental results on the two real-time datasets demonstrate that the proposed model outperforms the baseline methods significantly under arbitrarily missing scenarios.

Attention correlated appearance and motion feature followed temporal learning for activity recognition

<span lang="EN-US">Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.</span>

On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

In recent years, air pollution has attracted considerable attention worldwide. As an effective air protection method, the accurate prediction of air pollutants can help provide an early warning against harmful air pollutants and plan air pollution prevention and control strategies. Due to its high prediction capability, a novel method using a Takagi-Sugeno fuzzy model is presented to predict air pollutants in this study. This work investigates fuzzy model identification based on a larger dataset, and a hierarchical clustering-based identification method with model structure selection is proposed. The novelty of this work is as follows: First, a novel hierarchical clustering method has been employed in air quality forecasting. This method improves the widely used BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing a refinement phase for handling clusters with arbitrary shapes. Second, a cluster validity measure automatically determines the number of clusters. Then, the estimation of the model order selection and the model parameters is formulated as a sparse optimization problem, which can be solved using global optimization. Finally, the proposed Takagi-Sugeno model can learn the local trend pattern and spatial-temporal dependencies of multivariate air quality-related time series data. The presented method is demonstrated with air quality measurements in Shanghai, and the results show that the proposed method can achieve acceptable prediction performance.

Pre-encoding based temporal dependent rate–distortion optimization for HEVC

Rate–distortion optimization (RDO) is a crucial technique in block-based hybrid video encoders, which determines the coding option for a certain coding unit to achieve optimal rate–distortion (R–D) performance. However, the current RDO method, which is applied in High Efficiency Video Coding (HEVC), is far from being globally optimal as it ignores the interaction among coding options. In fact, the current coding option not only determines the R–D point of the coded unit but also influences the achievable R–D​ performance of the subsequent coding units due to the spatial–temporal dependencies among the block/frame optimizations. In this paper, we first investigate the R–D characteristics, including operational R–D curves and R–D dependencies, in HEVC through several simulation experiments. It is shown that under the hierarchical fixed QP setting in the HEVC encoder, the temporal R–D dependence dominates the coding process, and the distortion of the reference frame has a strong impact on that of coded frames while less influence on rate. We then introduce distortion effect factors to quantitatively measure the temporal R–D​ dependency. The distortion effect factor at frame level reflects the correlation degree between the current coding frame and the previous coded frames, and the one at block level reflects the influence degree that the distortion of a block to that of subsequent blocks which refer to it in temporal domain. Finally, a pre-encoding based temporal dependent RDO is proposed, which adaptively adjusts quantization parameters and Lagrange multipliers according to the distortion effect factors. Experimental results show that under configurations of low-delay B and P frames, the proposed method can achieve 5.4% and 5.5% bit rate savings on average, respectively, with around 7% encoding complexity increment, compared with the original HEVC encoder.

Multi-view dynamic graph convolution neural network for traffic flow prediction

The rapid urbanization and continuous improvement of road traffic equipment result in massive daily production of traffic data. These data contain the long-term evolution of traffic flow and dynamic changes in the traffic road network. Due to the complex topology of the traffic road network, traffic flow prediction is challenging as it contains complex, multi-periodic patterns, and is often affected by sudden events. In this paper, we propose a Multi-View Dynamic Graph Convolution Network (MVDGCN) that captures different levels of spatial–temporal dependencies to predict traffic flow. Firstly, we use the coupling graph convolution network to learn the relationship matrix among stations dynamically, capturing the spatial dependencies at different levels in the traffic network. Secondly, we establish three encoder–decoders, representing hourly, daily, and weekly views, to extract the evolution law of traffic flow from three different time periods. Finally, we use the dynamic fusion module to merge the spatial–temporal dependencies extracted from the multi-view encoder–decoders. We conducted experiments on two real datasets, NYCTaxi and NYCBike, and found that our proposed MVDGCN model outperformed the best baseline, improving the RMSE, MAE, PCC, and MAPE by 12.9%, 6.2%, 0.8%, and 6.5% respectively on the NYCBike dataset and 9.2%, 4.2%, 4.6%, and 3.0% respectively on the NYCTaxi dataset. These results show that the proposed MVDGCN model performs better than state-of-the-art algorithms.

Transformer Memory for Interactive Visual Navigation in Cluttered Environments

Substantial progress has been achieved in embodied visual navigation based on reinforcement learning (RL). These studies presume that the environment is stationary where all the obstacles are static. However, in real cluttered scenes, interactable objects (e.g. shoes and boxes) blocking the way of robots makes the environment non-stationary. Accordingly, the ego-centric visual agent will easily get stuck in the dilemma of finding the next waypoint as it struggles to decide whether to push the obstacles ahead. To handle the predicament, we formulate this interactive visual navigation as Partial Observed Markov Decision Process (POMDP). As the transformer encoder has demonstrated its superior ability to capture the spatial-temporal dependencies in natural language processing. We propose a transformer-based memory to empower the agents utilizing the historical interactive information. However, directly leveraging the transformer architecture in the RL settings is highly unstable. We further propose a surrogate objective to predict the next waypoint as the auxiliary task, which facilitates the representation learning and bootstraps the RL. We demonstrate our method in the iGibson environment and experimental results show a significant improvement over the interactive Gibson benchmark and the related recurrent RL policy both in the validation seen scenes and the test unseen scenes.

GraphSTGAN: Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things (IoT) devices for Maritime Monitor Services (MMS), maritime traffic controllers could not handle a massive amount of data in time. For unmanned MMS, one of the key technologies is situation understanding. However, the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult, and pose significant challenges to maritime situation understanding. In order to comprehend the situation accurately and thus offer unmanned MMS, it is crucial to model the complex dynamics of multi-agents using IoT big data. Nevertheless, previous methods typically rely on complex assumptions, are plagued by unstructured data, and disregard the interactions between multiple agents and the spatial-temporal correlations. A deep learning model, Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN), is proposed in this paper, which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions. Extensive experiments show the effectiveness and robustness of the proposed method.

Dynamic Spatial Correlation in Graph WaveNet for Road Traffic Prediction

Road traffic prediction is a crucial area currently investigated under the umbrella of the intelligent transportation system. Timely and accurate traffic prediction is a challenging problem because of the diverse nature of roads, abrupt changes in speed, and the existence of dependencies between road segments. A critical component of this research is addressing the dynamic spatial and non-linear temporal dependencies in the road network. The traffic conditions in a traffic road network change continuously, and for precise predictions, time-varying spatial correlation needs to be integrated into the model. This study intends to incorporate dynamic spatial dependencies in the Graph WaveNet model by applying attention mechanisms that can compute attention scores for the self-adaptive adjacency matrix in the time domain. We compared the computation cost of our proposed model of a graph attention network with multi-head attention with Graph WaveNet for up to 60 min. Our model gave the best result for 60-min prediction with an average percentage decrease of 3.4% and 4.76% in root-mean-square error on PEMS-BAY and METR-LA datasets, respectively. However, the model training time is increased because of the added computation of attention scores.

A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal

Epileptic seizures can affect the patient’s physical function and cause irreversible damage to their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic medical treatment. Hybrid deep learning models, which combine convolutional neural network and recurrent neural network, have better epileptic seizure detection performance as they could simultaneously extract spatial and temporal features. However, the existing hybrid deep learning models still have the following two weaknesses. Firstly, they directly input the raw electroencephalogram signals, where the epilepsy seizure information is limited. Secondly, some characteristic information is extracted in the feature map, distracting the attention of deep learning model. To address these issues, this paper proposes a difference attention ResNet-LSTM network (DARLNet). The proposed model uses a residual neural network (ResNet) and a long short-term memory network (LSTM) to capture spatial correlations and temporal dependencies, respectively. Besides, a difference layer is developed to automatically mine additional epileptic seizure information. Moreover, the channel attention module is introduced to make the model focus on seizure-relevant information. Several groups of experiments are conducted to evaluate the performance of DARLNet based on the Bonn Electroencephalogram dataset, which verifies the superiority of DARLNet on the two-category and five-category epileptic seizure detection tasks.

DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting

Capturing complex and dynamic spatial-temporal dependencies of traffic data is of great importance for accurate and real-time traffic forecasting in intelligent transportation systems. The spatial-temporal dependency between traffic locations is often dynamic, which means the correlation between the traffic status of different locations changes jointly over their spatial distance and the time slice they are in. Most of the existing Graph Convolutional Network-based methods usually capture spatial and temporal dependencies separately and then combine them in a parallel or serial mechanism to capture the spatial-temporal features. They always utilize the predefined static graph structure to capture both local correlations and global dependencies in the same time slice. These methods are incapable of directly learning dynamic spatial-temporal dependency across time slices. Meanwhile, it is challenging to learn the spatiotemporal correlation knowledge among traffic locations only by using neural networks. To address these issues, we propose a novel Dynamic Spatial-Temporal Adjacent Graph Convolutional Network (DSTAGCN), which connects the latest time slice with each past time slice to construct the spatial-temporal graph. DSTAGCN can directly learn the global spatial dependency across time and simultaneously capture the spatial-temporal dependencies through graph convolution. Since fuzzy theory make it possible to represent uncertain relationships, a simplified fuzzy neural network that integrates fuzzy systems and neural networks is designed to help generating the graph adjacency matrix representing the dynamic adjacency correlations. Experiments on public datasets show that our method outperforms baselines with fast convergence.

Learning Spatial-Temporal Dynamics for Short-Term Passenger Flow Prediction in Urban Rail Transit

Accurate short-term passenger flow prediction in urban rail transit (URT) plays an important role in ensuring the stable operation of the URT systems. Because of the complex dynamic spatial-temporal dependencies and potential semantic correlations of the URT network, accurate and effective short-term passenger flow prediction is challenging. To solve these problems, a novel model called the dynamic spatial-temporal graph convolutional network (DSTGCN) was proposed. Firstly, spatial semantic graphs (SSGs) were established to encode the spatial dependencies and semantic correlations of the URT network. Meanwhile, the dynamic graph convolutional network (DGCN) with the spatial attention mechanism was used to learn the dynamic spatial correlations of the nodes in the SSGs. Then, the long short-term memory (LSTM) network was integrated into the DGCN to learn the dynamic changes of passenger flow and capture local temporal dependencies. Moreover, the temporal attention mechanism was introduced after LSTM to capture global dynamic temporal correlations by adjusting the weights of different sequence information. Finally, the full connection layers were used to output the prediction results. Several experiments were conducted on Nanning Metro Line 1 real datasets to evaluate the model. The experimental results showed that the DSTGCN can effectively capture the dynamic spatial-temporal dependencies and semantic associations of the passenger flow. Besides, the prediction performances of the DSTGCN were better than those of existing baseline models, and it can provide technical support for improving the intelligent planning and operation decisions of URT systems.

Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines.

Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

In Intelligent Transportation Systems (ITS), controlling the traffic flow of a region in a city is the major challenge. Particularly, allocation of the traffic-free route to the taxi drivers during peak hours is one of the challenges to control the traffic flow. So, in this paper, the route between the taxi driver and pickup location or hotspot with the spatial-temporal dependencies is optimized. Initially, the hotspots in a region are clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to find the hot spots at the peak hours in an urban area. Then, the optimal route is allocated to the taxi driver to pick up the customer in the hotspot. Before allocating the optimal route, each route between the taxi driver and the hot spot is mapped to the number of taxi drivers. Among the map function, the optimal map is selected using the rain optimization algorithm (ROA). If more than one map function is obtained as the optimal solution, the map between the route and the taxi driver who has done the least number of trips in the day is chosen as the final solution This optimal route selection leads to control of the traffic flow at peak hours. Evaluation of the approach depicts that the proposed traffic flow control scheme reduces traveling time, waiting time, fuel consumption, and emission.

STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction, in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight. Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling. However, this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps. Furthermore, it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph (e.g., deriving from the geodesic distance or approximate connectivity), and may not reflect the actual interaction between nodes. To overcome those limitations, our paper proposes a spatial-temporal graph synchronous aggregation (STGSA) model to extract the localized and long-term spatial-temporal dependencies simultaneously. Specifically, a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process. In each STGSA block, we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes, and the potential temporal dependence is further fine-tuned by an adaptive weighting operation. Meanwhile, we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a data-driven manner. Then, inspired by the multi-head attention mechanism which can jointly emphasize information from different representation subspaces, we construct a multi-stream module based on the STGSA blocks to capture global information. It projects the embedding input repeatedly with multiple different channels. Finally, the predicted values are generated by stacking several multi-stream modules. Extensive experiments are constructed on six real-world datasets, and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

A Novel Spatial-Temporal Multi-Scale Alignment Graph Neural Network Security Model for Vehicles Prediction

Traffic flow forecasting is indispensable in today’s society and regarded as a key problem for Intelligent Transportation Systems (ITS), as emergency delays in vehicles can cause serious traffic security accidents. However, the complex dynamic spatial-temporal dependency and correlation between different locations on the road make it a challenging task for security in transportation. To date, most existing forecasting frames make use of graph convolution to model the dynamic spatial-temporal correlation of vehicle transportation data, ignoring semantic similarity between nodes and thus, resulting in accuracy degradation. In addition, traffic data does not strictly follow periodicity and hard to be captured. To solve the aforementioned challenging issues, we propose in this article CRFAST-GCN, a multi-branch spatial-temporal attention graph convolution network. First, we capture the multi-scale (e.g., hour, day, and week) long- short-term dependencies through three identical branches, then introduce conditional random field (CRF) enhanced graph convolution network to capture the semantic similarity globally, so then we exploit the attention mechanism to captures the periodicity. For model evaluation using two real-world datasets, performance analysis shows that the proposed CRFAST-GCN successfully handles the complex spatial-temporal dynamics effectively and achieves improvement over the baselines at 50% (maximum), outperforming other advanced existing methods.

ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting

Traffic forecasting aims to capture complex spatial-temporal dependencies and non-linear dynamics, which plays an indispensable role in intelligent transportation systems and other domains like neuroscience, climate, etc. Most recent works rely on graph convolutional networks (GCN) to model the dependencies and the dynamics. However, the over-smoothing issue of GCN would produce indistinguishable features among nodes, leading to poor expressivity and weak capability of modeling complex dependencies and dynamics. To address this issue, we present a novel clustering spatial-temporal (ClusterST) unit, which incorporates unsupervised learning into GCN for extracting discriminative features. Specifically, we first exploit a neural network to learn a dynamic clustering, i.e., learning to partition the neighbors of each node into clusters at each time step. Two probabilistic losses are proposed to improve the separability of clusters. Then, the extracted features of different clusters can be distinguished. Based on the dynamically formed clusters, a vanilla GCN is applied to aggregate features within each cluster. By purely exploiting such a ClusterST unit, large improvements over the state-of-the-art are achieved. Furthermore, ClusterST units with a different number of clusters can be regarded as basic components to construct an inception-like ClusterST network for going deeper. We evaluate the framework on two real-world large-scale traffic datasets and observe an average improvement of 18.19% and 7.62% over state-of-the-art baselines, respectively. The code and models will be publicly available.

Impact of refined 2D/3D urban morphology on hourly air temperature across different spatial scales in a snow climate city

Urban morphology profoundly affects the air temperature, and a better understanding of their relationships is crucial for improving outdoor comfort and urban planning. However, how their complicated associations change across different times and spaces is not clear. This study compared the influence of the refined urban 2D/3D morphology on hourly air temperature in a snow climate city by using high spatial resolution GF-2 images, correlation analysis and multiple regression models at different spatial scales. Results indicated that: (1) the air temperature differences among the sites were much larger during nighttime (4.14 °C) as compared to daytime (2.22 °C). The spatial pattern of the hourly air temperature had similar distributions except at the hottest time of day. (2) The relationships between air temperature and urban morphology had an obvious spatial-temporal dependency. Overall, their connections became stronger as the spatial scale increased and tended to become stale at the 500 m scale; the effect of urban characteristics on average nighttime, lowest, and daily air temperature was larger than daytime air temperature, and there was almost no correlation between the highest air temperature and urban morphology. (3) The effects of 2D and 3D urban morphology on air temperature vary depending on the selection of spatial scales and the choice of different indicator of air temperature. Typically, the building height and volume had the strongest warming effect. The 3D metrics performed better in predicting air temperature than 2D metrics at the 500 m scale, while the influence of 2D features was stronger at the 1000 m scale. Regardless of the spatial analysis scale, 3D features had stronger correlations with average daily, daytime, and nighttime air temperature. Based on our results, 3D urban morphology indications, such as building height and volume, should be emphasized at the 500 m scale for urban planning. Arranging buildings and vegetation in a reasonable way could generate a better thermal comfort environment for city dwellers.

Faulty feeder detection for single phase-to-ground faults in distribution networks based on patch-to-patch CNN and feeder-to-feeder LSTM

Faulty feeder detection has always been recognized as an essential issue in guaranteeing the stable and reliable operation of distribution networks. Existing methods fail to fully exploit the spatial correlation and temporal dependencies in the zero-sequence currents, which are exhibited as waveforms on the spatial domain and are displayed as time-series data on the time domain. In this paper, a novel detection method based on a hybrid model of convolutional neural network (CNN) and long short-term memory (LSTM) neural network is proposed, and the spatial–temporal information can be comprehensively explored. Deep spatial features are extracted by using CNN, and they are further processed by using LSTM network. Besides, to maintain the temporal dependencies of the extracted spatial features, the current waveforms are recognized on the patch scale by the proposed patch-to-patch CNN (PToP CNN). Moreover, a feeder-to-feeder LSTM (FToF LSTM) network is established to learn and compare spatial–temporal correlations between feeders. The joint PToP-CNN and FToF-LSTM can achieve collaborative superiority on mining the fault features in the zero-sequence currents. To verify the prospects in real installations, the hybrid model is implemented in an embedded device, called NVIDIA Jetson AGX Xavier, and the real-time detection demonstrates the efficiency of the proposed method.

Improving short-term bike sharing demand forecast through an irregular convolutional neural network

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial–temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a “matrix-format” city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to leverage the hidden linkage among “semantic neighbors”. The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that “thinking beyond spatial neighbors” can further improve short-term travel demand prediction of urban bike sharing systems.

Airport flight ground service time prediction with missing data using graph convolutional neural network imputation and bidirectional sliding mechanism

Accurate flight ground service time prediction plays a key role in the efficient operation of airports, which can help managers make better resource scheduling optimization decisions, reduce flight delays and improve airport operation capabilities. However, since flight ground service data is typically acquired by sensor equipment or manually recorded, data missing and abnormalities caused by sensor failure or manual recording errors are an unavoidable occurrence, making the prediction problem challenging. Although missing values can be imputed, the traditional methods usually fill in missing values and then make predictions separately, which leads to a two-step process and increases the prediction error. Besides, the existing one-step methods still suffer some limitations and shortcomings: failure to integrate bidirectional spatial–temporal dependencies leads to unsatisfying model prediction accuracy and poor robustness under high data missing rates, which cannot be directly used in the study of flight ground service time prediction with missing values. To solve this problem, we presented a sliding bidirectional graph convention network model framework that capable of dealing predicting with missing values. First, we proposed a new adjacency matrix construction method to capture more comprehensive spatial–temporal dependencies and developed a graph convention network based missing value imputation unity (GCNM) to inference and filling missing features. Then, multiple GCNM units are stacked to develop a bidirectional module BDGCNM to realize the filling and prediction into one step. Finally, we introduced a variable sliding window mechanism to improve the model’s robustness. Extensive experiments on real airport data sets and PEMS traffic data sets show that our method is more accurate and robust than the current state-of-the-art.