Explainability techniques applied to road traffic forecasting using Graph Neural Network models-Reference-Cited by-同舟云学术

Explainability techniques applied to road traffic forecasting using Graph Neural Network models

Published:2023-10 Issue: Volume:645 Page:119320
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

García-Sigüenza Javier,Llorens-Largo Faraon^ORCID,Tortosa Leandro,Vicent Jose F.^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference50 articles.

1. Combining Kohonen maps with arima time series models to forecast traffic flow;Van Der Voort;Transp. Res., Part C, Emerg. Technol.,1996

2. Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning;Lippi;IEEE Trans. Intell. Transp. Syst.,2013

3. Tcp-bast: a novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality;Zhang;Inf. Sci.,2022

4. Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches;Smith,1994

5. Speech recognition with deep recurrent neural networks;Graves,2013

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A survey on graph neural networks for intrusion detection systems: Methods, trends and challenges;Computers & Security;2024-06

2. Hierarchical bottleneck for heterogeneous graph representation;Information Sciences;2024-05

3. A Fundamental Model with Stable Interpretability for Traffic Forecasting;Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies;2023-11-13

4. Search for deep graph neural networks;Information Sciences;2023-11

5. Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning;Digital Transportation and Safety;2020