Real-Time Traffic Flow Uncertainty Quantification Based on Nonparametric Probability Density Function Estimation-Reference-Cited by-同舟云学术

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Real-Time Traffic Flow Uncertainty Quantification Based on Nonparametric Probability Density Function Estimation

Published:2024-11 Issue:11 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Li Meiye¹,Guo Jianhua²^ORCID,Zhong Xiaobin¹

Affiliation:

1. Ph.D. Student, Intelligent Transportation System Research Center, Southeast Univ., Nanjing 211189, PR China.

2. Professor, Intelligent Transportation System Research Center, Southeast Univ., Nanjing 211189, PR China; Researcher, Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Ministry of Transport, Nanjing 211100, PR China (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8539

Reference75 articles.

1. ANALYSIS AND MODELING TIME HEADWAY DISTRIBUTIONS UNDER HEAVY TRAFFIC FLOW CONDITIONS IN THE URBAN HIGHWAYS: CASE OF ISFAHAN

2. Analysis of Time Headways on Urban Roads: Case Study from Riyadh

3. Generalized autoregressive conditional heteroskedasticity

4. Calculating interval forecasts;Chatfield C.;J. Bus. Econ. Stat.,1993

5. Chen Y. S. Yu J. K. Eshraghian and C. P. Lim. 2023. “Multi-objective spiking neural network for optimal wind power prediction interval.” In Proc. 2023 IEEE Int. Symp. on Circuits and Systems (ISCAS) 1–5. New York: IEEE. https://doi.org/10.1109/ISCAS46773.2023.10181537.

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