PREDICTION of VEHICULAR TRAFFIC FLOW USING LEVENBERG-MARQUARDT ARTIFICIAL NEURAL NETWORK MODEL: ITALY ROAD TRANSPORTATION SYSTEM

Isaac Oyeyemi Olayode; Alessandro Severino; Tiziana Campisi; Lagouge Kwanda Tartibu

doi:10.26552/COM.C.2022.2.E74-E86

PREDICTION of VEHICULAR TRAFFIC FLOW USING LEVENBERG-MARQUARDT ARTIFICIAL NEURAL NETWORK MODEL: ITALY ROAD TRANSPORTATION SYSTEM

Isaac Oyeyemi Olayode, Alessandro Severino, Tiziana Campisi, Lagouge Kwanda Tartibu

Mechanical and Industrial Engineering Technology

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

Abstract

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Levenberg-Marquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R²) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

Original language	English
Pages (from-to)	E74-E86
Journal	Communications - Scientific Letters of the University of Zilina
Volume	24
Issue number	2
DOIs	https://doi.org/10.26552/COM.C.2022.2.E74-E86
Publication status	Published - 2022

Keywords

Artificial intelligence
Italy transportation system
Levenberg-Marquardt artificial neural network model
Traffic congestion
Traffic flow

ASJC Scopus subject areas

Civil and Structural Engineering
Transportation
Mechanical Engineering
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.26552/COM.C.2022.2.E74-E86

Cite this

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title = "PREDICTION of VEHICULAR TRAFFIC FLOW USING LEVENBERG-MARQUARDT ARTIFICIAL NEURAL NETWORK MODEL: ITALY ROAD TRANSPORTATION SYSTEM",

abstract = "In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Levenberg-Marquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.",

keywords = "Artificial intelligence, Italy transportation system, Levenberg-Marquardt artificial neural network model, Traffic congestion, Traffic flow",

author = "Olayode, {Isaac Oyeyemi} and Alessandro Severino and Tiziana Campisi and Tartibu, {Lagouge Kwanda}",

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doi = "10.26552/COM.C.2022.2.E74-E86",

language = "English",

volume = "24",

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journal = "Communications - Scientific Letters of the University of Zilina",

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PREDICTION of VEHICULAR TRAFFIC FLOW USING LEVENBERG-MARQUARDT ARTIFICIAL NEURAL NETWORK MODEL: ITALY ROAD TRANSPORTATION SYSTEM. / Olayode, Isaac Oyeyemi; Severino, Alessandro; Campisi, Tiziana et al.
In: Communications - Scientific Letters of the University of Zilina, Vol. 24, No. 2, 2022, p. E74-E86.

Research output: Contribution to journal › Article › peer-review

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T1 - PREDICTION of VEHICULAR TRAFFIC FLOW USING LEVENBERG-MARQUARDT ARTIFICIAL NEURAL NETWORK MODEL

T2 - ITALY ROAD TRANSPORTATION SYSTEM

AU - Olayode, Isaac Oyeyemi

AU - Severino, Alessandro

AU - Campisi, Tiziana

AU - Tartibu, Lagouge Kwanda

PY - 2022

Y1 - 2022

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AB - In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Levenberg-Marquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

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PREDICTION of VEHICULAR TRAFFIC FLOW USING LEVENBERG-MARQUARDT ARTIFICIAL NEURAL NETWORK MODEL: ITALY ROAD TRANSPORTATION SYSTEM

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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