Predictive Analysis of Street Light Operations for Fault Detection In Urban Lighting Infrastructure

Abstract

Street lighting plays a pivotal role in urban infrastructure, ensuring safety and security for pedestrians and motorists during nighttime hours. However, traditional maintenance approaches often fall short due to reactive strategies and manual interventions. To address these challenges, predictive analysis of street light operations offers a promising solution by leveraging data analytics and machine learning techniques. Traditionally, street light maintenance relied on periodic inspections and manual interventions, resulting in inefficiencies and delays in addressing issues. Reactive maintenance strategies led to increased operational costs and compromised public safety due to unexpected outages and delays in repairs. There is a pressing need for proactive and data-driven approaches to street light management to enhance operational efficiency, reduce maintenance costs, and improve service reliability.