  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.
