Real-time traffic condition uncertainty quantification using adaptive grey prediction interval model-Reference-Cited by-同舟云学术

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Real-time traffic condition uncertainty quantification using adaptive grey prediction interval model

Published:2024-08-26 Issue: Volume: Page:1-26
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Song Zhanguo¹,Wang Xinran²,Huang Wei¹³,Li Meiye¹,Zhong Xiaobin¹,Guo Jianhua¹³^ORCID

Affiliation:

1. Intelligent Transportation System Research Center, Southeast University, Nanjing, People’s Republic of China

2. College of Arts & Science, New York University, New York, NY, USA

3. Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Ministry of Transport, Nanjing, People’s Republic of China

Funder

Postgraduate Research & Practice Innovation Program of Jiangsu Province

China Scholarship Council

Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Ministry of Transport, PRC through open project

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2024.2394522

Reference69 articles.

1. Short-term prediction of traffic parameters-performance comparison of a data-driven and less-data-required approaches

2. Short-term freeway traffic parameter prediction: Application of grey system theory models

3. Resampling Methods for Estimating Travel Time Uncertainty: Application of the Gap Bootstrap

4. Generalized autoregressive conditional heteroskedasticity

5. Time series interval forecast using GM(1,1) and NGBM(1, 1) models

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