Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network

Ye, Junchen; Sun, Leilei; Du, Bowen; Fu, Yanjie; Tong, Xinran; Xiong, Hui

doi:10.1145/3292500.3330887

Cited by 117 publications

(45 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Community public service information analysis and governance is a very complex project, and the information is often massive. However, in the process of information processing, there are often problems such as insufficient manpower, high operation cost, and being unable to respond quickly to the processing results [7]. However, the emergence and development of deep learning algorithms in recent years show their strong prediction ability.…”

Section: Related Workmentioning

confidence: 99%

Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model

Yajing

2021

Journal of Mathematics

View full text Add to dashboard Cite

The communities have significantly increased in number and the environment has become complex. There are problems such as poor information collection in community public service information governance, lack of relevant analysis standards and models, and unreliable prediction results. In order to forecast and manage the risk information of the community, this research analyzes the public information of the community through the collaborative deep learning model. First of all, the information characteristic factors are selected that affect social risks based on the correlation analysis theory. Secondly, the convolutional neural network is used in deep learning for simulation of the community risk prediction model. Finally, through the comparative analysis of the model prediction results, it can be concluded that the accuracy rate of the proposed prediction model reaches 92.5%. An effective collaborative deep learning model is used to govern community public service information.

show abstract

Section: Related Workmentioning

confidence: 99%

Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model

Yajing

2021

Journal of Mathematics

View full text Add to dashboard Cite

show abstract

“…And they missed the chance to utilize the correlations among different sources for performance improvement. Ye et al [29] focused on two transportation modes with a satisfactory level of region coverage which is hard to apply for station-sparse data analysis. Overall, most of the previous works did traffic prediction based on LSTM or RNN which had less capability to store the knowledge from one source which can be adapted to other sources.…”

Section: Travel Demand Predictionmentioning

confidence: 99%

“…And they were not able to explore the correlations among different transport modes which have great potential to improve the forecasting performance. Although Ye et al [29] co-predicted the demand of two transportation modes, their method requires the same level of region coverage for the sources, which lacks the applicability to predict the demand of station-sparse sources. In practice, since the development levels/stages of different cities and modes of transport are uneven, the sparseness of stations/regions for some modes may lead to less accurate and unsatisfactory demand forecasting.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network

Bai

Liu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

Accurate demand forecasting of different public transport modes (e.g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies significantly, which makes it hard to predict the demand of the modes with insufficient knowledge and sparse station distribution (i.e., station-sparse mode). Intuitively, different public transit modes may exhibit shared demand patterns temporally and spatially in a city. As such, we propose to enhance the demand prediction of stationsparse modes with the data from station-intensive mode and design a Memory-Augmented Multi-task Recurrent Network (MATURE) to derive the transferable demand patterns from each mode and boost the prediction of station-sparse modes through adapting the relevant patterns from the station-intensive mode. Specifically, MATURE comprises three components: 1) a memory-augmented recurrent network for strengthening the ability to capture the longshort term information and storing temporal knowledge of each transit mode; 2) a knowledge adaption module to adapt the relevant knowledge from a station-intensive source to station-sparse sources; 3) a multi-task learning framework to incorporate all the information and forecast the demand of multiple modes jointly. The experimental results on a real-world dataset covering four public transport modes demonstrate that our model can promote the demand forecasting performance for the station-sparse modes.

show abstract

“…For stationless modes, the operators usually define a number of service zones as the basic units of operations. To jointly model multimodal travel demand, recent works typically aggregate multimodal demand to a spatial grid [4,5] or other well-defined zone partitions [6]. Based on the same spatial structure, a similar model architecture can then be performed for different modes to learn shared spatiotemporal features [7].…”

Section: Introductionmentioning

confidence: 99%

Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach

Liang,

Huang,

Zhao

2021

Preprint

View full text Add to dashboard Cite

Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand prediction, ignoring the fact that the demands for different transportation modes can be correlated with each other. Despite some recent efforts, existing approaches to multimodal demand prediction are generally not flexible enough to account for multiplex networks with diverse spatial units and heterogeneous spatiotemporal correlations across different modes. To tackle these issues, this study proposes a multi-relational spatiotemporal graph neural network (ST-MRGNN) for multimodal demand prediction. Specifically, the spatial dependencies across modes are encoded with multiple intra-and inter-modal relation graphs. A multi-relational graph neural network (MRGNN) is introduced to capture cross-mode heterogeneous spatial dependencies, consisting of generalized graph convolution networks to learn the message passing mechanisms within relation graphs and an attention-based aggregation module to summarize different relations. We further integrate MRGNNs with temporal gated convolution layers to jointly model heterogeneous spatiotemporal correlations. Extensive experiments are conducted using real-world subway and ride-hailing datasets from New York City, and the results verify the improved performance of our proposed approach over existing methods across modes. The improvement is particularly large for demand-sparse locations. Further analysis of the attention mechanisms of ST-MRGNN also demonstrates its good interpretability for understanding cross-mode interactions.

show abstract

Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network

Cited by 117 publications

References 15 publications

Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model

Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model

Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network

Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach

Contact Info

Product

Resources

About