Traffic volume prediction using intuitionistic fuzzy Grey-Markov model

Traffic congestion is a major problem in the last few decades. Though the Government is taking various measures like installing more traffic signals, constructing fly-overs, one-ways, etc., the problem could not be addressed effectively on the heavily congested areas. The increasing number of vehicl...

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Bibliographic Details
Published in:Neural computing & applications 2021-10, Vol.33 (19), p.12905-12920
Main Authors: Govindan, Kuppuswami, Ramalingam, Sujatha, Broumi, Said
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Fuzzy sets
Image Processing and Computer Vision
Markov chains
Original Article
Probability and Statistics in Computer Science
Roads
Traffic congestion
Traffic flow
Traffic models
Traffic signals
Traffic volume
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Summary:Traffic congestion is a major problem in the last few decades. Though the Government is taking various measures like installing more traffic signals, constructing fly-overs, one-ways, etc., the problem could not be addressed effectively on the heavily congested areas. The increasing number of vehicles, the dynamic population of traffic, the narrowness of roads and the absence of alternative roads or other routes are the major obstacles to reduce traffic congestion. Researchers of various streams have been working on predicting traffic volume to solve this problem. In this paper, a new method named Intuitionistic Fuzzy Grey-Markov Model(IFGMM) is introduced, which is an extension of Grey-Markov to Intuitionistic fuzzy set. The model is distinctive when compared with other models as it has a high level of accuracy performance. The forecasting result of this model is almost in proximity with the actual result of the traffic volume. In addition, the relative errors shown in this method are lesser than that of other models, due to its prediction accuracy is higher. Since the data collected in this method are taken from a heavily congested road from a metropolitan city, the proposed approach will be suitable for any congested area of the world.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-05940-9