Prediction of Road Traffic using an ELM-based Neural Network

R. S. Ali Fathima, R. Sumathi
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Abstract

This study discusses about traffic prediction, which is possible in intelligent transportation systems. This involves making predictions based on data from the previous year and data from the most recent years, which eventually yields accuracy and mean square error. For those who need to check the current traffic situation, this prediction will be useful. The traffic statistics is based on a 1 hour time gap. From this prediction, live traffic numbers are examined. So, while the user is also driving, this will be simpler to examine. The core objective of this proposed system is to identify the future traffic based on the video analysis. The proposed system uses video analysis and ELM based neural network. The proposed system is also useful for central and state government for maintaining smooth traffic flow.
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基于elm的神经网络道路交通预测
本研究探讨了智能交通系统中交通预测的可行性。这包括根据前一年和最近几年的数据进行预测,最终得出准确性和均方误差。对于那些需要查看当前交通状况的人来说,这个预测将是有用的。流量统计是基于1小时的时间间隔。根据这一预测,检查实时流量数字。因此,当用户也在开车时,这将更容易检查。该系统的核心目标是基于视频分析来识别未来的交通。该系统采用视频分析和基于ELM的神经网络。拟议中的系统对中央和邦政府维持顺畅的交通流量也很有用。
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