机器学习,智能交通预测和减少拥堵

A. Lakshna, K. Ramesh, B. Prabha, D. Sheema, K. Vijayakumar
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引用次数: 2

摘要

智能交通拥堵减少对于减少高度拥堵地区的交通是有用的。为了防止交通拥堵,物联网是通过一个叫做传感器的小设备来实现的,这种技术被称为智能交通。在路边的街道哨所附近放置一个小型装置来检测车辆数量。智能交通通过收集各种电子设备的各种信号,如WiFi、蓝牙、ZigBee,如智能手机、智能手表、智能手环、平板电脑。每辆车的MAC地址作为输入信息被收集并存储在云平台中。对收集到的数据集进行分析计算,并在机器学习预测算法下执行,以获得更好的准确率结果,避免交通拥堵。逻辑回归算法在交通中给出了91%的准确率水平。它给出了到达目的地的最短路线,没有任何障碍。结果减少了行驶时间,噪音污染,二氧化碳排放,准时到达目的地,节省了燃料。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning Smart Traffic Prediction and Congestion Reduction
Smart traffic congestion reduction is useful for reducing the traffic in a highly congested area. To prevent heavy traffic Internet of things is implemented through a small device called a sensor, this technology is called smart traffic. A small device is placed near the roadside street post to detect the vehicle count. Smart traffic works by collecting the various signals like WiFi, Bluetooth, ZigBee from various electronic gadgets like a smartphone, smartwatch, smart band, tablet. The MAC address from each vehicle is collected as input information and stored in a cloud platform. Analyze and calculate the collected data set and performed it under machine learning prediction algorithms to get a better accuracy result to avoid traffic congestion. The logistic regression algorithm gives a 91% of accuracy level in traffic. It gives the shortest route to reach the destination without any hurdles. Results are reduced the traveling time, noise pollution, carbon dioxide emission, reach the destination on correct time and also save the fuel.
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