Petite term traffic flow prediction using deep learning for augmented flow of vehicles

J. Indumathi, V. Kaliraj
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引用次数: 1

Abstract

An Intelligent Transport System (ITS) model that is contingent on the compulsion and expertise of the Traffic Prediction System in the contemporary urban context is proposed in this paper. Deep Learning (DL) is computationally becoming comfortable to train and set as many hyperparameters automatically as possible. The researchers and practitioners crave to set as many hyperparameters inevitably as possible in the DL. To be a great enabler, ITS has to find suitable solutions to issues like—alert on live time traffic information to interested parties along with facility to retrieve on demand the long-term statistical data, reduce the middling waiting time for commuters, offer protected, consistent, value-added services, control with vitality the signal timing based on the traffic flow etc., All these limitations call for instant attention. Among all the listed issues the problems like the sharp nonlinearities due to changeovers amid free flow, breakdown, retrieval and congestion. The contributions in this paper are as follows: (i) Adopt an ascendable approach to kindle the scarce information formed; (ii) Exploit the attention mechanism to exterminate the disadvantages of Long Short-Term Memory (LSTM) methods for traffic prediction; (iii) Suggest a new fusion smoothing model; (iv) Investigating, developing, and utilizing the Bayesian contextual bandits; (v) Recommend a Linear model based on LSTM, in combo with Bayesian contextual bandits. The travel speed prediction is done by LSTM. The results authenticate that the proposed model can adeptly achieve the goal of developing a system. The proposed model is definitely the best solution to overcome the issues.
基于深度学习的车辆增强流量小周期交通流预测
本文提出了一种基于交通预测系统的智能交通系统(ITS)模型。深度学习(DL)在计算上变得越来越容易自动训练和设置尽可能多的超参数。研究人员和从业者渴望在DL中不可避免地设置尽可能多的超参数。智能交通系统要想成为一个强大的推动者,就必须找到合适的解决方案,如实时交通信息提醒相关方,并能够按需检索长期统计数据,减少通勤者的中间等待时间,提供有保护的、一致的、增值的服务,以及基于交通流量的信号配时控制等问题。在列举的所有问题中,自由流、故障、检索和拥塞等切换引起的尖锐非线性问题。本文的贡献如下:(1)采用一种可上升的方法来激发形成的稀缺信息;(ii)利用注意机制,消除长短期记忆(LSTM)交通预测方法的弊端;(iii)提出一种新的融合平滑模型;调查、发展和利用贝叶斯上下文强盗;(v)推荐基于LSTM的线性模型,结合贝叶斯上下文强盗。车速预测采用LSTM算法。结果表明,该模型能够很好地实现系统开发的目标。所提出的模型无疑是克服这些问题的最佳解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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