DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2021-08-06 - Article/Dans un journal avec peer-review - Anglais - 11 page(s)

Patel Monal, Valderrama Carlos , YADAV ARVIND, "Metaheuristic enabled deep convolutional neural network for traffic flow prediction: Impact of improved lion algorithm" in International Journal of Intelligent Transportation Systems Research, 25, 5, 10.1080/15472450.2021.1974857

  • Edition : Springer (Germany)
  • Codes CREF : Sciences de l'ingénieur (DI2000), Techniques d'imagerie et traitement d'images (DI2770), Technologies de l'information et de la communication (TIC) (DI4730), Semi-conducteurs (DI2512), Electronique et électrotechnique (DI2411), Instrumentation médicale (DI2760), Conception assistée par ordinateur (DI1247), Electronique générale (DI2510), Electricité (DI1230)
  • Unités de recherche UMONS : Electronique et Microélectronique (F109)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
  • Centres UMONS : Centre de Recherche en Technologie de l’Information (CRTI)
Texte intégral :

Abstract(s) :

(Anglais) Traffic flow prediction is a basic aspect to be considered in transportation management and modeling. Attaining precise information on near and current traffic flows has an extensive range of appliances and it further aids in managing the congestion. Numerous conventional models failed at offering precise prediction results due to “shallow in architecture and hand engineered in features”. Moreover, the raw traffic flow information contains noise that might lead to the worst prediction results. Therefore, this paper intends to design an enhanced prediction model on traffic flow using Optimized Deep Convolutional Neural Network (DCNN). The input features or the technical indicators subjected to the optimized CNN are Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Indicator (RSI) and Rate of Change (ROC), respectively. Moreover, for precise prediction, the weights of DCNN are optimally tuned using a new Improved Lion Algorithm (LA) termed as Lion with New Territorial Takeover Update (LN-TU) model. In the end, the betterment of implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis.

Identifiants :
  • DOI : 10.1080/15472450.2021.1974857