MODELLING SMART ROAD TRAFFIC CONGESTION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES
MODELLING SMART ROAD TRAFFIC CONGESTION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES
复制标题
DOI：
10.14311/nnw.2019.29.008
复制
发表时间：
2019-01-01
影响因子：
0.8
通讯作者：
Fatima, A.
中科院分区：
文献类型：
--
作者：
Ata, A.;Khan, M. A.;Fatima, A.
由于城市人口的急剧增长，要求充分利用现代技术，高效、可持续地设计交通系统。动态交通流是造成交通阻塞的一个重要问题。因此，为了解决这一问题，本文旨在提供一种利用人工神经网络（Artificial Neural Networks， ANN）来预测交通拥堵的机制，从而控制或最小化拥堵，使道路交通更加顺畅。建议使用人工反向传播神经网络（MSR2C-ABPNN）建模智能道路交通拥堵控制，以提高向市民提供服务的透明度、可用性和效率。本文采用反向传播算法对神经网络进行训练，实现了对拥塞的预测。该系统旨在提供一种解决方案，提高旅行者的舒适度，从而做出更智能、更好的交通决策，而神经网络是一种合理的方法来发现交通状况。与拟合方法相比，采用时间序列的MSR2C-ABPNN在MSE方面得到了令人满意的结果。
By the dramatic growth of the population in cities requires the traffic systems to be designed efficiently and sustainably by taking full advantage of modern-day technology. Dynamic traffic flow is a significant issue which brings about a block of traffic movement. Thus, for tackling this issue, this paper aims to provide a mechanism to predict the traffic congestion with the help of Artificial Neural Networks (ANN) which shall control or minimize the blockage and result in the smoothening of road traffic. Proposed Modeling Smart Road Traffic Congestion Control using Artificial Back Propagation Neural Networks (MSR2C-ABPNN) for road traffic increase transparency, availability and efficiency in services offered to the citizens. In this paper, the prediction of congestion is operationalized by using the algorithm of backpropagation to train the neural network. The proposed system aims to provide a solution that will increase the comfort level of travellers to make intelligent and better transportation decision, and the neural network is a plausible approach to find traffic situations. Proposed MSR2C-ABPNN with Time series gives attractive results concerning MSE as compared to the fitting approach.