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doi:10.1111/tgis.v24.3

Cited by 2 publications

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Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

et al. 2020

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Key Points Question Did human mobility patterns change during stay-at-home orders and were the mobility changes associated with the coronavirus disease 2019 (COVID-19) curve? Findings This cross-sectional study using anonymous location data from more than 45 million mobile phones found that median travel distance decreased and stay-at-home time increased across the nation, although there was geographic variation. State-specific empirical doubling time of total COVID-19 cases increased (ie, the spread reduced) significantly after stay-at-home orders were put in place. Meaning These findings suggest that stay-at-home social distancing mandates were associated with the reduced spread of COVID-19 when they were followed.

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Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

et al. 2020

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Long-term traffic flow prediction based on multi-scale perceptual change

Lu,

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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Traffic flow prediction is very important to urban traffic management and public safety, and is also a key link in the development of intelligent transportation system. However, the long-term traffic flow data has complex time characteristics, which brings severe challenges to the traffic flow forecasting research. Deep learning methods are mainly used in time series prediction tasks such as traffic flow prediction. Recently, Transformer-based approaches have been able to improve traffic flow forecasting results, but there are still limitations such as high computational costs and difficulty capturing overall trends in traffic flow data. We propose a multi-scale sensing neural network (MSPFormer) based on Transformer, which uses multi-layer perceptrons to achieve more efficient feature extraction in step prediction, while designing a progressive decomposition architecture to preserve historical information authenticity as much as possible. Detailed experimental studies have shown that our approach can improve the accuracy of traffic flow prediction in Transformer architecture by 4% to 24% without consuming additional computing resources.

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Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 2 publications

References 10 publications

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

Long-term traffic flow prediction based on multi-scale perceptual change

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