IoT for measuring road network quality index
نویسندگان
چکیده
Abstract Egypt has been fighting the issue of ensuring road safety‚ reducing accidents‚ preserving lives citizens since its inception. For these reasons‚ precisely identifying condition‚ followed by effective and timely maintenance rehabilitation measures‚ leads to an increase in network's safety level lifespan. This paper presents a multi-input deep learning framework that combines BiLSTM Depthwise separable convolution work parallel for automatic recognition surface quality different anomalies. Furthermore, we performed investigation compare networks approaches against other traditional using real-time data sensed collected from Egyptian network. The proposed model achieved average accuracy 93.1%‚ which is superior compared evaluated approaches. Finally, utilized estimate index cities.
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ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2022
ISSN: ['0941-0643', '1433-3058']
DOI: https://doi.org/10.1007/s00521-022-07736-x