Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction

Wu, Mingming; Zhu, Chaochao; Chen, Lianliang

doi:10.1145/3395260.3395266

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Also, Kuang et al assured that using LSTM with MTL and 3D CNN to find spatio-temporal features and having pick up and drop off as related tasks with taking the weather, days, and transportation conditions into account could increase the accuracy of predicting the taxi demand for passengers, driver, or taxi demand applications [88]. Likewise, Wu et al illustrated that using both spatial and temporal graph attention networks (GTA) together to find the relationship among the road regions to capture the taxi pick up and drop off information and train them concurrently with MTL make improvement better than using only the spatial dependencies [89].…”

Section: ) Taxi Demand Predictionmentioning

confidence: 99%

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Survey on Multi-Task Learning in Smart Transportation

Alzahrani,

Wang,

Liao

et al. 2024

IEEE Access

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Artificial Intelligence (AI) has been widely adopted in numerous fields and enabled various smart systems because of its strong ability to perform tasks, including prediction, event detection, and status estimation, among others. As one of the typical smart systems empowered by AI and Internet of Things (IoT) technologies, the smart transportation system has made dramatic progress for traditional transportation in numerous aspects, including autonomous driving, smart traffic lights, navigation, and traffic forecasting, among others. Deep learning is an essential component to enable such smart systems. Typically, specific deep learning models, e.g., Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), can be trained on collected transportation data for a particular task. However, traditional deep learning techniques rely on data sufficiency to build an effective model. Additionally, each trained model can only work on one single task. This has limited the efficacy of deep learning techniques in numerous application scenarios. To this end, multi-task learning (MTL) has been studied to train a single model that can work for multiple tasks. This technique effectively allows the learning model to expand to specific tasks with only a limited amount of data affiliated. In the meantime, MTL significantly reduces the training time of each task. The success of MTL requires that there are potential relationships among different tasks. Many tasks in the smart transportation system are related. For instance, traffic speed and vehicle volume estimations for each road are highly correlated. Based on this, research on applying MTL in smart transportation systems has been studied recently. This paper reviews the recent efforts to use MTL in smart transportation systems and conducts an extensive survey to provide insights. In particular, we categorize the MTL applications in smart transportation systems into traffic forecasting, traffic sign recognition, vehicle recognition, travel time estimation, road safety estimation, taxi demand prediction, and autonomous driving. Ultimately, we discuss challenges and future research directions in applying MTL in smart transportation systems.INDEX TERMS Multi-task learning (MTL), smart transportation, deep learning.

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Section: ) Taxi Demand Predictionmentioning

confidence: 99%

“…2020 Wu et al [89] Improving the taxi demand prediction by using both spatial and temporal information of the road regions and combining the taxi pick up and drop off information based on MTL.…”

Section: ) Scalabilitymentioning

confidence: 99%

Survey on Multi-Task Learning in Smart Transportation

Alzahrani,

Wang,

Liao

et al. 2024

IEEE Access

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“…Wu et al [37] jointly trained the three subtasks in ECPE via a unified framework and had clause features shared to exploit the interaction between subtasks. To make full use of the implicit connection between emotion detection and emotion-cause pair extraction, Tang et al [38] tackled these two tasks in a joint framework.…”

Section: End-to-end Ecpementioning

confidence: 99%

“…MTNECP [37] is a feature-shared, multi-task model and improves cause extraction with the help of position-aware emotion information.…”

mentioning

confidence: 99%

A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair Extraction

Liu

et al. 2022

Electronics

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Recently, graph neural networks (GNN), due to their compelling representation learning ability, have been exploited to deal with emotion-cause pair extraction (ECPE). However, current GNN-based ECPE methods mostly concentrate on modeling the local dependency relation between homogeneous nodes at the semantic granularity of clauses or clause pairs, while they fail to take full advantage of the rich semantic information in the document. To solve this problem, we propose a novel hierarchical heterogeneous graph attention network to model global semantic relations among nodes. Especially, our method introduces all types of semantic elements involved in the ECPE, not just clauses or clause pairs. Specifically, we first model the dependency between clauses and words, in which word nodes are also exploited as an intermediary for the association between clause nodes. Secondly, a pair-level subgraph is constructed to explore the correlation between the pair nodes and their different neighboring nodes. Representation learning of clauses and clause pairs is achieved by two-level heterogeneous graph attention networks. Experiments on the benchmark datasets show that our proposed model achieves a significant improvement over 13 compared methods.

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“…The application research of GPS trajectory data has attracted the attention of academia and industry. The main research directions are location-based services (LBS) [2] and intelligent transportation (ITS) [3]. Location prediction is the core and underlying support of LBS.…”

Section: Introductionmentioning

confidence: 99%

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

Cao¹,

Jiang²,

Wang³

et al. 2020

Journal of Information Hiding and Privacy Protection

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Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods, the movement characteristics of trajectory data cannot be well expressed, which in turn affects the accuracy of the prediction results. First, a new trajectory data expression method by associating the movement behavior information is given. The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region. The movement behavior features based on pre-association may not always be the best for the prediction model. Therefore, through association analysis and importance analysis, the final association feature is selected from the pre-association features. The trajectory data is input into the LSTM networks after associated features and genetic algorithm (GA) is used to optimize the combination of the length of time window and the number of hidden layer nodes. The experimental results show that compared with the original trajectory data, the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.

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Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction

Cited by 7 publications

References 10 publications

Survey on Multi-Task Learning in Smart Transportation

Survey on Multi-Task Learning in Smart Transportation

A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair Extraction

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

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