使用深度学习的交通信号定时控制

Vishnu, Paavai Anand
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引用次数: 0

摘要

印度是人口第二多的国家,有13.7亿人口,所以避开交通是不可能的。但是通过适当的交通信号控制方法,我们可以控制在交通中花费的时间。我们的解决方案是控制交通信号的时间,并使用基于深度学习的计算机视觉方法(如物体检测)为包含更多车辆的车道分配更多的绿灯时间长度。2019年1月,全国购买和登记的新车超过150万辆(1,607,315辆)。其中74%的车辆为两轮车,超过80%的车辆为汽油驱动。印度有550万公里的公路网,而现在注册的车辆数量是印度的三倍。这些单一的统计数据应该揭示了为什么印度的道路每个月都变得越来越拥挤。2017年,全国共报告道路交通事故464910起,死亡147913人,受伤47975人。平均每天发生1274起事故,405人死亡。通过使用深度学习来控制交通信号,我们可以更有效地疏导交通,减少交通拥堵、交通违规、事故、燃料消耗、污染和交通时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traffic signal timing control using deep learning
India is the second most populated country with 1.37 billion people so that avoiding traffic is impossible. But with proper traffic signal control method, we can control the amount of time spent in traffic. Our solution for this problem is to control the traffic signal timing and allocate more time length of green light to lanes containing a greater number of vehicles using deep learning-based computer vision approaches such as object detection. In January 2019, more than a million and a half (1,607,315) new vehicles were bought and registered all across the country. In which 74% of the vehicles were two-wheelers and more than 80% of the total vehicles were petrol driven. India has 5.5 million kilometres of road network while now the number of vehicles registered is three times greater. These single statistics should reveal why Indian roads are getting more congested every month. In 2017, a total of 4,64,910 road accidents have been reported in which 1,47,913 deaths occurred and 4,70,975 people were injured. An average of 1274 accidents and 405 deaths every day. By using deep learning for controlling traffic signals, we can clear traffic more effectively and reduce traffic congestion, traffic violations, accidents, fuel consumption, pollution and time in traffic.
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