Comparative Analysis of Anpr and TIRTL Systems Using Artificial Neural Networks for Traffic Speed Management

Kumar, Boddu Sudhir; Kumar, Pala Gireesh; Kumar, Kontham Pavan

doi:10.2478/sjce-2025-0004

Abstract

Transportation networks are struggling with increased traffic due to mixed flows and the unregulated growth of private vehicles. Over-speeding and congestion are critical issues for urban planners. Effective speed management and enforcement are essential to mitigate excessive speed, which is a major cause of traffic accidents. This study aims to develop efficient traffic speed management measures by evaluating the performance of Automatic Number Plate Recognition (ANPR) and The Infra-Red Traffic Logger (TIRTL) in data collection and the detection of excessive speeding. The results show ANPR detected only 51% of the vehicle classes, while TIRTL detected 96% of them. A maximum speed reduction of 20 km/h was observed in the vehicles, with an average reduction of 8 km/h. The ANN model developed can help urban planners devise new speed management techniques by accurately estimating their effectiveness in an urban setting.

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Comparative Analysis of Anpr and TIRTL Systems Using Artificial Neural Networks for Traffic Speed Management

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