ELMOPP: An Application of Graph Theory and Machine Learning to Traffic Light Coordination

TL;DR

This paper introduces ELMOPP, a traffic light coordination algorithm that uses machine learning and graph theory to predict future traffic flow, outperforming existing algorithms while relying only on camera footage.

Contribution

ELMOPP is a novel algorithm combining NLSTM and graph theory to predict traffic flow and optimize light control using only camera data, unlike prior methods requiring GPS or sensors.

Findings

ELMOPP statistically outperforms OAF and ITLC in throughput rate.

ELMOPP requires only camera footage, simplifying deployment.

Simulation results validate ELMOPP's effectiveness in traffic management.

Abstract

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.