ELMOPP: an application of graph theory and machine learning to traffic light coordination

Fareed Sheriff

doi:10.1108/aci-07-2020-0035

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Highlights

1.1 Background Traffic lights used for road systems have been around since the 19th century with the installation of a gas-lit traffic light in London
The purpose of traffic lights was to control traffic to prevent jams and decrease the risk of accidents. This is a heavy task to conduct manually because it requires that humans decide the optimal or even just an adequate configuration of traffic light timings to minimize traffic congestion, which becomes increasingly more difficult to manage as the number of traffic lights increases [1]
2.1 Problem statement/task definition The algorithm this paper presents, Edge Load Management and Optimization through Pseudo-flow Prediction (ELMOPP), aims to solve the problems detailed in previous algorithms and systems – through machine learning with nested long short-term memory modules (NLSTM), the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic

Summary

Introduction

1.1 Background Traffic lights used for road systems have been around since the 19th century with the installation of a gas-lit traffic light in London. This traffic light was a single light that controlled horse-drawn carriage traffic and was prone to explosions. The purpose of traffic lights was to control traffic to prevent jams and decrease the risk of accidents This is a heavy task to conduct manually because it requires that humans decide the optimal or even just an adequate configuration of traffic light timings to minimize traffic congestion, which becomes increasingly more difficult to manage as the number of traffic lights increases [1]

Methods

Results

Conclusion