Towards optimal tuned machine learning techniques based vehicular traffic prediction for real roads scenarios-Reference-Cited by-同舟云学术

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Towards optimal tuned machine learning techniques based vehicular traffic prediction for real roads scenarios

Published:2024-08 Issue: Volume:161 Page:103508
ISSN:1570-8705
Container-title:Ad Hoc Networks
language:en
Short-container-title:Ad Hoc Networks

Author:

Qaddoura Raneem^ORCID,Younes Maram Bani^ORCID,Boukerche Azzedine

Publisher

Elsevier BV

Reference45 articles.

1. Intelligent traffic management system for smart cities;Khanna,2019

2. A performance evaluation of an efficient traffic congestion detection protocol (ECODE) for intelligent transportation systems;Younes;Ad Hoc Netw.,2015

3. Distributed classification of urban congestion using VANET;Al Mallah;IEEE Trans. Intell. Transp. Syst.,2017

4. Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems;Boukerche;Comput. Netw.,2020

5. Efficient traffic congestion estimation using multiple spatio-temporal properties;Yang;Neurocomputing,2017

Cited by 1 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 surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model;Accident Analysis & Prevention;2024-11

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