融合深度信念网络和 SVM 回归实现城市交通控制系统智能化

Alireza Soleimani, Yousef Farhang, Amin Babazadeh Sangar
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摘要

日益增长的城市交通和拥堵已导致空气污染加剧和时间浪费等重大问题,这凸显了对智能交通灯控制系统(TLC)的需求,以最大限度地减少车辆等待时间。本文介绍了一种新型交通灯控制系统,该系统利用物联网(IoT)进行数据收集,并采用随机森林算法进行预处理和特征提取。深度信念网络可预测未来的交通状况,而支持向量回归网络则可提高预测的准确性。此外,还利用强化学习优化了交通灯控制策略。我们通过两种不同的场景对所提出的方法进行了评估。第一种情况是与固定时间控制和双决斗深度神经网络(3DQN)方法进行比较。第二种情况是与 SVM、KNN 和 MAADAC 方法进行比较。仿真结果表明,所提出的方法明显优于这些替代方法,车辆平均等待时间分别大幅提高了 20%、32% 和 45%。使用深度信念网络,辅以支持向量回归,确保了预测交通模式的高精度。此外,基于强化学习的交通灯控制策略优化能有效适应不断变化的交通状况,提供卓越的交通流量管理。研究结果表明,所提出的系统可以大幅减少交通拥堵,改善城市交通流量。
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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Fusion of deep belief network and SVM regression for intelligence of urban traffic control system

Increasing urban traffic and congestion have led to significant issues such as rising air pollution and wasted time, highlighting the need for an intelligent traffic light control (TLC) system to minimize vehicle waiting times. This paper presents a novel TLC system that leverages the Internet of Things (IoT) for data collection and employs the random forest algorithm for preprocessing and feature extraction. A deep belief network predicts future traffic conditions, and a support vector regression network is integrated to enhance prediction accuracy. Additionally, the traffic light control strategy is optimized using reinforcement learning. The proposed method is evaluated through two different scenarios. The first scenario is compared with fixed-time control and the double dueling deep neural network (3DQN) methods. The second scenario compares it with the SVM, KNN, and MAADAC approaches. Simulation results demonstrate that the proposed method significantly outperforms these alternative approaches, showing substantial improvements in average vehicle waiting times by more than 20%, 32%, and 45%, respectively. Using a deep belief network, supplemented by support vector regression, ensures high precision in forecasting traffic patterns. Furthermore, the reinforcement learning-based optimization of the traffic light control strategy effectively adapts to changing traffic conditions, providing superior traffic flow management. The results indicate that the proposed system can substantially reduce traffic congestion and improve urban traffic flow.

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