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

Li, Can; Bai, Lei; Liu, Wei; Yao, Lina; Waller, S. Travis

doi:10.1145/3340531.3411965

Cited by 18 publications

(8 citation statements)

References 27 publications

(48 reference statements)

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“…For example, one study developed a knowledge adaptation module that boosted the prediction of transportation modes with fewer stations (e.g., ferries) by adapting the demand pattern from station-intensive modes (e.g., buses). The model results show that MTL improves the demand forecasting performance for modes with fewer stations [10]. Another study we found looked into demand prediction for the subway and TNCs [40].…”

Section: Multi-task Learningmentioning

confidence: 87%

“…Recently, multi-task learning (MTL) has garnered significant attention in the AI domain, as it enables different tasks to share information, thereby enhancing the prediction accuracy. Some transportation demand forecasting research has also adopted this technique [10][11][12][13][14]. However, the majority of these studies allow information sharing between tasks without controlling for "negative transfer", which is common and could reduce the effectiveness of multi-task learning.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs

Guo,

Chen,

Zhang

2024

Sustainability

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Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models.

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Section: Multi-task Learningmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs

Guo,

Chen,

Zhang

2024

Sustainability

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“…MasterGNN [12] utilizes a heterogeneous recurrent GNN to capture the spatio-temporal correlation between air quality and weather monitor stations. MATURE [18] and KA2M2 [19] propose to predict transportation mode with multi-task methods.…”

Section: Related Workmentioning

confidence: 99%

PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction

Zhang,

Zhao,

Liu

et al. 2023

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

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In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple spatio-temporal attributes simultaneously can alleviate regulatory pressure and foster smart city construction. However, current research can not handle the spatio-temporal multi-attribute prediction well due to the complex relationships between diverse attributes. The key challenge lies in how to address the common spatio-temporal patterns while tackling their distinctions. In this paper, we propose an effective solution for spatio-temporal multi-attribute prediction, PromptST. We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatiotemporal attributes. Then, we elaborate a spatio-temporal prompt tuning strategy to fit the specific attributes in a lightweight manner. Through the pretrain and prompt tuning phases, our PromptST is able to enhance the specific spatio-temoral characteristic capture by prompting the backbone model to fit the specific target attribute while maintaining the learned common knowledge. Extensive experiments on real-world datasets verify that our PromptST attains state-of-the-art performance. Furthermore, we also prove PromptST owns good transferability on unseen spatio-temporal attributes, which brings promising application potential in urban computing. The implementation code is available to ease reproducibility 1 .

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“…Ke et al (2021) focuses on the demand of ride-hailing systems to predict solo and shared service rides jointly by constructing multi-graph convolutional networks. Furthermore, Li et al (2020b) designs a recurrent network for demand prediction of the station-intensive travel mode and the station-sparse travel mode simultaneously to improve the forecasting accuracy for the station-sparse mode. A larger range of different transport modes are explored in Toman et al (2020), including taxis, bikes, subways, and vehicles operated by transit network companies (TNCs).…”

Section: Demand Forecasting Under Multiple Travel Modesmentioning

confidence: 99%