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Mining frequent patterns with generalized linear model for traffic density analysis

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Abstract

Call Detail Record (CDR) is the detailed record of all the telephonic calls that pass through a telephone exchange or any other telecommunications equipment. It contains temporal and spatial data, and can also convey other information that would be helpful to the user. Large numbers of vehicles on roads creates substantial traffic, which makes it very difficult to maintain safety and control traffic especially in the urban areas. Several works were carried out in the past to estimate the traffic density. However, they were inappropriate and quite expensive, owing to the dynamics of the traffic flow. This paper proposes the use of CDR data to find the high traffic density zones (HTDZs). For prediction purpose, we mine the frequent patterns from CDR data to find the co-occurrence of the position associated with a mobile user. In addition, Recurrent neural Networks (RNN) using LSTM (Long Short-term memory) are used for the time series prediction. The proposed system helps the whole public not only the registered users by decreasing the accident rates. Statistical performance evaluation integrated with time series causality is done for the proposed system. The proposed system is evaluated over standard data sets and an accuracy of 96% is achieved and a root mean square value was obtained as 3.84 during prediction.

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Acknowledgements

The authors are grateful to all of those with whom I have had the pleasure to work during this research paper.

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  1. Cochin University of Science and Technology, Kochi, Kerala, India

    Suja Chandrasekharan Nair, Sudheep Elayidom & Sasi Gopalan

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  1. Suja Chandrasekharan Nair
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  2. Sudheep Elayidom
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  3. Sasi Gopalan
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Nair, S.C., Elayidom, S. & Gopalan, S. Mining frequent patterns with generalized linear model for traffic density analysis. Multimed Tools Appl 82, 18327–18352 (2023). https://doi.org/10.1007/s11042-022-13802-2

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  • DOI: https://doi.org/10.1007/s11042-022-13802-2

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