Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model

Isaac O. Olayode; Lagouge K. Tartibu; Modestus O. Okwu

doi:10.1109/ICMIMT52186.2021.9476173

Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model

Isaac O. Olayode, Lagouge K. Tartibu, Modestus O. Okwu

Mechanical and Industrial Engineering Technology

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Citations (Scopus)

Abstract

Traffic congestion is a prevailing problem globally, which threatens the wider community, especially in developing countries. The Markov chain model (MCM) is a widely acknowledged and applied method used in traffic modelling, planning, and road traffic control systems. Classical techniques like MCM have been used to reduce vehicular flow and traffic congestions. Nowadays, artificial intelligence techniques have been recognized for solving traffic congestions and multivariate problems. The application of ANN in traffic flow prediction performance yielded positive results. The present study dwells on a comparison between the Markov Chain Model and artificial neural network model for predicting traffic flow of vehicles at signalized road intersections. Analysis of dataset collected at Mikros traffic monitoring (MTM) firm, with vehicular speed and distance as input variables and time as output, gave a good performance with root mean square error (RMSE) of 0.0025 and coefficient of determination (R2) of 0.96417. The ANN model was adjudged capable of modelling traffic flow at road intersections.

Original language	English
Title of host publication	Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	287-292
Number of pages	6
ISBN (Electronic)	9780738144627
DOIs	https://doi.org/10.1109/ICMIMT52186.2021.9476173
Publication status	Published - 13 May 2021
Event	12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021 - Cape Town, South Africa Duration: 13 May 2021 → 15 May 2021

Publication series

Name	Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021

Conference

Conference	12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021
Country/Territory	South Africa
City	Cape Town
Period	13/05/21 → 15/05/21

Keywords

Artificial Intelligence
Artificial Neural Network (ANN)
Markov chain model
Traffic congestion

ASJC Scopus subject areas

Industrial and Manufacturing Engineering
Mechanical Engineering
Mechanics of Materials
Metals and Alloys
Artificial Intelligence
Civil and Structural Engineering

UN SDGs

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

Access to Document

10.1109/ICMIMT52186.2021.9476173

Cite this

Olayode, I. O., Tartibu, L. K., & Okwu, M. O. (2021). Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model. In Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021 (pp. 287-292). Article 9476173 (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMIMT52186.2021.9476173

Olayode, Isaac O. ; Tartibu, Lagouge K. ; Okwu, Modestus O. / Traffic flow Prediction at Signalized Road Intersections : A case of Markov Chain and Artificial Neural Network Model. Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 287-292 (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021).

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title = "Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model",

abstract = "Traffic congestion is a prevailing problem globally, which threatens the wider community, especially in developing countries. The Markov chain model (MCM) is a widely acknowledged and applied method used in traffic modelling, planning, and road traffic control systems. Classical techniques like MCM have been used to reduce vehicular flow and traffic congestions. Nowadays, artificial intelligence techniques have been recognized for solving traffic congestions and multivariate problems. The application of ANN in traffic flow prediction performance yielded positive results. The present study dwells on a comparison between the Markov Chain Model and artificial neural network model for predicting traffic flow of vehicles at signalized road intersections. Analysis of dataset collected at Mikros traffic monitoring (MTM) firm, with vehicular speed and distance as input variables and time as output, gave a good performance with root mean square error (RMSE) of 0.0025 and coefficient of determination (R2) of 0.96417. The ANN model was adjudged capable of modelling traffic flow at road intersections.",

keywords = "Artificial Intelligence, Artificial Neural Network (ANN), Markov chain model, Traffic congestion",

author = "Olayode, {Isaac O.} and Tartibu, {Lagouge K.} and Okwu, {Modestus O.}",

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Olayode, IO, Tartibu, LK & Okwu, MO 2021, Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model. in Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021., 9476173, Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021, Institute of Electrical and Electronics Engineers Inc., pp. 287-292, 12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021, Cape Town, South Africa, 13/05/21. https://doi.org/10.1109/ICMIMT52186.2021.9476173

Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model. / Olayode, Isaac O.; Tartibu, Lagouge K.; Okwu, Modestus O.
Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 287-292 9476173 (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T2 - 12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021

AU - Olayode, Isaac O.

AU - Tartibu, Lagouge K.

AU - Okwu, Modestus O.

PY - 2021/5/13

Y1 - 2021/5/13

N2 - Traffic congestion is a prevailing problem globally, which threatens the wider community, especially in developing countries. The Markov chain model (MCM) is a widely acknowledged and applied method used in traffic modelling, planning, and road traffic control systems. Classical techniques like MCM have been used to reduce vehicular flow and traffic congestions. Nowadays, artificial intelligence techniques have been recognized for solving traffic congestions and multivariate problems. The application of ANN in traffic flow prediction performance yielded positive results. The present study dwells on a comparison between the Markov Chain Model and artificial neural network model for predicting traffic flow of vehicles at signalized road intersections. Analysis of dataset collected at Mikros traffic monitoring (MTM) firm, with vehicular speed and distance as input variables and time as output, gave a good performance with root mean square error (RMSE) of 0.0025 and coefficient of determination (R2) of 0.96417. The ANN model was adjudged capable of modelling traffic flow at road intersections.

AB - Traffic congestion is a prevailing problem globally, which threatens the wider community, especially in developing countries. The Markov chain model (MCM) is a widely acknowledged and applied method used in traffic modelling, planning, and road traffic control systems. Classical techniques like MCM have been used to reduce vehicular flow and traffic congestions. Nowadays, artificial intelligence techniques have been recognized for solving traffic congestions and multivariate problems. The application of ANN in traffic flow prediction performance yielded positive results. The present study dwells on a comparison between the Markov Chain Model and artificial neural network model for predicting traffic flow of vehicles at signalized road intersections. Analysis of dataset collected at Mikros traffic monitoring (MTM) firm, with vehicular speed and distance as input variables and time as output, gave a good performance with root mean square error (RMSE) of 0.0025 and coefficient of determination (R2) of 0.96417. The ANN model was adjudged capable of modelling traffic flow at road intersections.

KW - Artificial Intelligence

KW - Artificial Neural Network (ANN)

KW - Markov chain model

KW - Traffic congestion

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 May 2021 through 15 May 2021

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Olayode IO, Tartibu LK, Okwu MO. Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model. In Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 287-292. 9476173. (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021). doi: 10.1109/ICMIMT52186.2021.9476173

Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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