Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach

Ke, Jintao; Feng, Siyuan; Zhu, Zheng; Yang, Hai; Ye, Jieping

doi:10.1016/j.trc.2021.103063

Cited by 72 publications

(23 citation statements)

References 42 publications

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“…Encoder Convolutional neural network is an efficient model widely used on image and proven to be effective on some natural language processing tasks such as part-of-speech tagging, chunking, named entity recognition and semantic role in the model. Since CBOW has the shortage of ignoring the information of word orders and is easy to be influenced by the quality of keywords extraction (Ke et al, 2021;Riba et al, 2021).…”

Section: Figure 2: Encoder Of Cbowmentioning

confidence: 99%

Learning of Knowledge Graphs with Entity Descriptions

Conde¹

2022

IJMRA

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The representing learning makes specialty of knowledge graph and it indicates the difference between different entities. The knowledge graph representing with the low-dimensional space. In fact, most of the method usually concern of description of entity which is hard for existing strategies to take benefit of. Here, we recommend a new representing learning method with knowledge graphs that uses entity description. We evaluate our method on two tasks like knowledge graph and entity classification. Experimental effects on actual-world datasets show that our version plays higher than different baseline fashions, especially under the zero-short setting, which indicate that our technique for novel the entity description.

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Section: Figure 2: Encoder Of Cbowmentioning

confidence: 99%

Learning of Knowledge Graphs with Entity Descriptions

Conde¹

2022

IJMRA

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“…The experimental results show that the model integrating multifactors could predict taxi demand more accurately. Ke et al [17] proposed a novel deep multi-task multi-graph learning approach to predict the demand for online car-hailing under different service modes. Independent multi graph revolutionary (MGC) networks for different service modes were established by using the regulated cross task (RCT) learning and the multi-linear relationship (MLR) learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Different time series and space sequences have different weights on the demand and supply distributions of online car-hailing. On the other hand, although many deep learning methods have been applied in the field of demand forecasting for online car-hailing (e.g., [15][16][17][18]), the method of LSTM + Attention has not been explored in the context of short-term demand forecasting for online car-hailing. An attention mechanism enables recognition the importance of the time series and space sequences of order data.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

Yan

et al. 2021

Electronics

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The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car-hailing. In order to accurately predict the short-term demand of online car-hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal features, spatial features, and weather features. Significantly, an attention mechanism is used to distinguish the time series and space sequences of order data. The order data in Haikou city was collected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short-term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support advanced scheduling and dynamic pricing for online car-hailing.

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“…Similarly, Wang et al [5] proposed a convolutional recurrent network to co-predict travel demands for ride-hailing and bike sharing based on the same spatial grid. In [6], a multi-graph learning-based approach was introduced to predict the zone-based ride-hailing demand for different service modes, i.e., solo-rides and shared-rides. Overall, these models all aggregate the demand data of different modes to the same zone partition.…”

Section: Multimodal Demand Predictionmentioning

confidence: 99%

“…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

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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.

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Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach

Cited by 72 publications

References 42 publications

Learning of Knowledge Graphs with Entity Descriptions

Learning of Knowledge Graphs with Entity Descriptions

Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

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

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