AI Powered Surveillance and Smart Analytics for Security Manpower Optimization in Rail Based Mass Rapid Transit Systems

Abstract

Rail-based Mass Rapid Transit Systems is a backbone of urban infrastructure where security failures can lead to panic among public, creating disaster scenarios. In India, metro rail organizations such as DMRC depends heavily on human-intensive security measures, primarily with CISF for overlooking physical frisking, baggage scanning, patrolling, overseeing surveillance and access control across the stations and allied infrastructures such as Train Depots, Operational Control Centres, etc. Traditional CCTV monitoring utilize about 9% of total deployed security strength, is becoming hard to manage due to rapid growth of metro network, increasing operational costs. This study evaluates the current manpower allocation for CCTV surveillance in DMRC and explores how security staff can be optimized with AI-based surveillance and smart analytics. This study adopts quantitative secondary data design to estimate the manpower required for CCTV surveillance across the operational and sanctioned phases of the DMRC. Findings reveals that adopting AI-powered surveillance could reduce the need for around 1560 dedicated CCTV monitoring staff across the sanctioned network of DMRC, leading to cumulative salary savings of approx. 15,455.64 million INR between 2026 to 2035. 
Further, AI-based systems can better spot unusual act, send instant alerts, monitor crowd levels and analyses passenger behavior, helping in efficient disaster preparedness in MRTS network. The study concludes that AI-powered surveillance is an important step in building cost-effective, modern and disaster resilient metro security systems. However, the research also points challenges for successful large scale rollout, including high implementation costs and establishing rules for privacy and ethical use of the technology which need to be managed.