Deep Wide Spatial-Temporal Based Transformer Networks Modeling for the Next Destination According to the Taxi Driver Behavior Prediction

Abideen, Zain Ul; Sun, Heli; Yang, Zhou; Ahmad, Rana Zeeshan; Iftekhar, Adnan; Ali, Amir

doi:10.3390/app11010017

Cited by 25 publications

(5 citation statements)

References 32 publications

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“…Given that flow generation is a task for estimating the flows between a group of geographic places based on those locations’ data (such as population, POIs, land use, and distance to other sites), it is a separate task from next-place prediction. Finally, only one study presented a transformer-like architecture, the DWSTTN (Deep Wide Spatio Temporal Transformer Network) [ 33 ]. The DWSTTN uses historical pick-up and drop-off data from taxi companies in Porto and Manhattan to predict a taxi’s next destination.…”

Section: Related Workmentioning

confidence: 99%

Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling

Corrias,

Gjoreski,

Langheinrich

2023

Sensors

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The estimation of human mobility patterns is essential for many components of developed societies, including the planning and management of urbanization, pollution, and disease spread. One important type of mobility estimator is the next-place predictors, which use previous mobility observations to anticipate an individual’s subsequent location. So far, such predictors have not yet made use of the latest advancements in artificial intelligence methods, such as General Purpose Transformers (GPT) and Graph Convolutional Networks (GCNs), which have already achieved outstanding results in image analysis and natural language processing. This study explores the use of GPT- and GCN-based models for next-place prediction. We developed the models based on more general time series forecasting architectures and evaluated them using two sparse datasets (based on check-ins) and one dense dataset (based on continuous GPS data). The experiments showed that GPT-based models slightly outperformed the GCN-based models with a difference in accuracy of 1.0 to 3.2 percentage points (p.p.). Furthermore, Flashback-LSTM—a state-of-the-art model specifically designed for next-place prediction on sparse datasets—slightly outperformed the GPT-based and GCN-based models on the sparse datasets (1.0 to 3.5 p.p. difference in accuracy). However, all three approaches performed similarly on the dense dataset. Given that future use cases will likely involve dense datasets provided by GPS-enabled, always-connected devices (e.g., smartphones), the slight advantage of Flashback on the sparse datasets may become increasingly irrelevant. Given that the performance of the relatively unexplored GPT- and GCN-based solutions was on par with state-of-the-art mobility prediction models, we see a significant potential for them to soon surpass today’s state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling

Corrias,

Gjoreski,

Langheinrich

2023

Sensors

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“…STSAN [21] has three attention modules, and not only incorporates spatial-temporal factors but also captures patterns of location and user preference change. DWSTTN [22] used a transformer-based model to dynamically capture long-range dependencies. HGMAP [23] combined GCNs and multi-head attention.…”

Section: Neural Network Applications In Trajectory Predictionmentioning

confidence: 99%

A Multi-Level Attentive Context-Aware Trajectory Prediction Algorithm for Mobile Social Users

Xin

Zang

2023

Electronics

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The prediction of a user’s trajectory is a key problem in mobility prediction, which has been applied to a range of fields such as location-based service recommendations and traffic planning. The impact of users’ social contacts on mobility is not adequately considered in the current trajectory prediction research. Furthermore, the spatial–temporal dependence of long trajectories is difficult to characterize by conventional recurrent neural network models. A multi-level attentive context-aware trajectory prediction model (MACTP) for mobile social users is proposed in this research to address the above problems. Specifically, users’ social preferences are captured by friend-level attention, and different friends are allocated varying weights. The impact of other check-in points in the trajectory on the present check-in point is considered through check-in-level attention. Trajectory-level attention is used to obtain the representation of historical trajectories influenced by current trajectories, as well as the spatial–temporal dependencies of longer trajectories. Experimental results on two real-world datasets demonstrate that the proposed model significantly improves trajectory prediction performance.

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“…Transformer [9] entirely relies on the attention mechanism to model the global dependencies of the sequence and breaks through the limitation that RNN cannot be parallelized. Deep Wide Spatio-Temporal Transformer Network (DWSTTN) [36] uses two attention mechanisms to extract relevant information in time and space, respectively. Graph Convolutional Dual-Attentive Networks (GCDAN) [12] design a dual-attention mechanism within and between trajectories and use graph convolution to extract spatial features in the embedding layer.…”

Section: Related Work 21 Mobility Predictionmentioning

confidence: 99%

Human Mobility Prediction with Calibration for Noisy Trajectories

Miao

Lin

et al. 2022

Electronics

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Human mobility prediction is a key task in smart cities to help improve urban management effectiveness. However, it remains challenging due to widespread intractable noises in large-scale mobility data. Based on previous research and our statistical analysis of real large-scale data, we observe that there is heterogeneity in the quality of users’ trajectories, that is, the regularity and periodicity of one user’s trajectories can be quite different from another. Inspired by this, we propose a trajectory quality calibration framework for quantifying the quality of each trajectory and promoting high-quality training instances to calibrate the final prediction process. The main module of our approach is a calibration network that evaluates the quality of each user’s trajectories by learning their similarity between them. It is designed to be model-independent and can be trained in an unsupervised manner. Finally, the mobility prediction model is trained with the instance-weighting strategy, which integrates quantified quality scores into the parameter updating process of the model. Experiments conducted on two citywide mobility datasets demonstrate the effectiveness of our approach when dealing with massive noisy trajectories in the real world.

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Deep Wide Spatial-Temporal Based Transformer Networks Modeling for the Next Destination According to the Taxi Driver Behavior Prediction

Cited by 25 publications

References 32 publications

Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling

Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling

A Multi-Level Attentive Context-Aware Trajectory Prediction Algorithm for Mobile Social Users

Human Mobility Prediction with Calibration for Noisy Trajectories

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