Traffic light management is a broad subject with various papers published that put forth algorithms to efficiently manage traffic using traffic lights. Two such algorithms are the OAF (oldest arrival first) and ITLC (intelligent traffic light controller) algorithms. However, many traffic light algorithms do not consider future traffic flow and therefore cannot mitigate traffic in such a way as to reduce future traffic in the present. This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion. Furthermore, while ITLC and OAF require the use of GPS transponders; and GPS, speed sensors, and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors. ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation, and the collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second.

中文翻译：

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ELMOPP：图论和机器学习在交通灯协调中的应用

交通灯管理是一个广泛的主题，发表了各种论文，提出了使用交通灯有效管理交通的算法。两种这样的算法是 OAF（最早到达优先）和 ITLC（智能交通灯控制器）算法。然而，许多交通灯算法没有考虑未来的交通流量，因此无法以减少当前未来交通的方式缓解交通流量。本文介绍了通过伪流预测（ELMOPP）算法进行的边缘负载管理和优化，旨在解决之前算法中详述的问题；通过具有嵌套长短期记忆 (NLSTM) 模块和图论的机器学习，该算法尝试使用过去的数据和交通模式来预测不久的将来，以告知其实时决策并通过基于过去的流量预测未来的交通流量并使用这些预测来最大化当前的交通流量并减少未来的交通拥堵来更好地缓解交通拥堵。此外，虽然 ITLC 和 OAF 需要使用 GPS 转发器；和 GPS、速度传感器和无线电，ELMOPP 只使用交通灯摄像头镜头，与 GPS 和速度传感器相比，这几乎总是很容易获得。ELMOPP 针对 ITLC 和 OAF 交通管理算法进行了测试，该算法使用模仿 ITLC 论文中提出的模拟，即单交叉口模拟，