Identifying key grid cells for crowd flow predictions based on CNN-based models with the Grad-CAM kit-Reference-Cited by-同舟云学术

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Identifying key grid cells for crowd flow predictions based on CNN-based models with the Grad-CAM kit

Published:2022-10-08 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Chiu Sheng-Min^ORCID,Liou Yow-Shin,Chen Yi-Chung^ORCID,Lee Chiang,Shang Rong-Kang,Chang Tzu-Yin

Funder

Ministry of Science and Technology, Taiwan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03988-1.pdf

Reference47 articles.

1. Fiore S, Elia D, Pires C E, Mestre D G, Cappiello C, Vitali M, Andrade N, Braz T, Lezzi D, Moraes R et al (2019) An integrated big and fast data analytics platform for smart urban transportation management. IEEE Access 7:117652–117677

2. Sarkar B, Biswas A (2021) Pythagorean fuzzy ahp-topsis integrated approach for transportation management through a new distance measure. Soft Comput 25(5):4073–4089

3. Marsch L A (2021) Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology 46(1):191–196

4. Hayes S C, Merwin R M, McHugh L, Sandoz E K, A-Tjak Jacqueline GL, Ruiz F J, Barnes-Holmes D, Bricker J B, Ciarrochi J, Dixon M R et al (2021) Report of the acbs task force on the strategies and tactics of contextual behavioral science research. J Contex Behav Sci 20:172–183

5. Williams B M (2001) Multivariate vehicular traffic flow prediction: evaluation of arimax modeling. Transp Res Rec 1776(1):194–200

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