Mainstreaming Photo- and Video-Based Documentation as Method for Establishing a Level of Service Framework for the Mumbai Suburban Railway System

Abstract

The city of Mumbai has grown at an unprecedented rate, increasing the burden of mobility on its core public transport system, the Mumbai suburban railway network. The system is likely failing from “over-optimization,” with stations not designed to cater to the needs of a rapidly growing city, which has led to a steady surge in fatalities over the years, primarily in the metropolitan region beyond the city limits. Besides fatalities, research indicates that crowding has led to extreme fear and insecurity, especially in women and young commuters, with inappropriate behavior by fellow passengers causing them extreme discomfort. There is a need to decongest the Mumbai suburban rail network across the system and to gain a better measure of the extent of crowding in and around transit facilities. Concepts such as level of service (LOS) from the vantage point of crowding science can be used to address this need. However, there are two critical challenges. First, concepts developed in the Global North are inadequate to deal with the kind of commuter densities and complexities typical of cities like Mumbai. Secondly, conventional data gathering methods have proved to be time consuming, costly, and too inflexible to capture dynamic commuter behavior critical to the science of crowd management. This paper aims to address these two challenges by articulating a set of “inquiries” that can inform a localized framework and share learnings from the application of basic video and image processing. Thus, it proposes dynamic data capture methods that inform and enable a scientific planning process.