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首页> 外文期刊>Journal of ambient intelligence and humanized computing >Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance
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Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance

机译：多代理深神经网络与LQF-MWM算法进行交通管制和应急车辆引导

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摘要

Authorities in modern cities are facing daily challenges related to traffic control. Due to the problem complexity caused by the urbanization growth, investing in developing traffic signal control systems (TSCS) to guarantee better mobility has taken more attention by these authorities. In the existing literature, the majority of TSCS offers only a real-time control for a detected traffic problem without considering early prediction and estimation of its occurrence. Furthermore, traffic problems related to the arrival and guidance of emergency vehicles are rarely considered. Based on these gaps, we rely on concepts and mechanisms from both, the Artificial and the convolution neural networks (ANN and CNN), coupled with the longest queue first maximal weight matching algorithm (LQF-MWM), to develop PANNAL, a predictive and reactive TSCS. PANNAL is a Multi-Agent based System, where each individual agent has ANN, CNN, and LQF-MWM to adapt signal sequences and durations and favor the crossing of emergency vehicles. Agents have a heterarchical architecture considered for coordination. We leant on VISSIM, a state-of-the-art traffic simulation software for simulation and evaluation. We adopted algorithms, scenarios, key performance indicators, and evaluation results from the recent literature for benchmarking. These algorithms are pre-emptive and have a high performance and competitive results in traffic control of disturbed traffic condition.
机译：现代城市的当局正面临与交通管制有关的日常挑战。由于城市化增长引起的问题，投资开发交通信号控制系统（TSC）以保证更好的流动性，这些当局更加关注。在现有文献中，大多数TSCs仅为检测到的交通问题提供实时控制，而不考虑其发生的早期预测和估计。此外，很少考虑与紧急车辆到达和指导相关的交通问题。基于这些差距，我们依赖于来自人工和卷积神经网络（ANN和CNN）的概念和机制，与最长的队列第一最大重量匹配算法（LQF-MWM）耦合，以开发琶音，预测和反应性TSC。 Pannal是一种基于多种子体的系统，每个代理商都有ANN，CNN和LQF-MWM，适应信号序列和持续时间，并有利于紧急车辆的交叉。代理商有一个考虑协调的杂结构。我们在Vissim上倾斜，是用于仿真和评估的最先进的流量仿真软件。我们采用了最近的基准测试的算法，场景，关键绩效指标和评估结果。这些算法是先发制人的，并且在干扰交通状况的交通控制中具有高性能和竞争结果。

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