Mainstreaming Photo and Video-based Documentation as Method for Establishing a LOS Framework for the Mumbai Suburban Railway System

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. It is likely that the system is failing due to ‘over optimization’, and the fact that stations aren’t designed to cater to needs of a rapidly growing city, has led to a steady surge in fatalities over the years, primarily in the metropolitan region beyond city limits. Besides fatalities, recent research shows, that crowding has led to extreme fear and insecurity, especially in women and young commuters vulnerable to petty thefts and sexual abuse. There is a critical need to decongest the Mumbai suburban rail network across the system: including station areas, platforms, inside coaches and on access infrastructure like pedestrian ramps, bridges and stairways. Concepts, such as level of service (LOS), overcrowding and crowd management can be used to address this need, however, there are two critical challenges. One, these concepts, largely developed in global North cities, are inadequate in dealing with the kinds of commuter densities and complex station area economies, typical to cities like Mumbai. Two, conventional data gathering methods are time consuming, costly, and inflexible in capturing dynamic commuter behavior, critical to the science of crowd management. This paper aims to address these two challenges by, articulating a set of ‘probes’ that can inform a localized framework towards effective crowd management on the Mumbai local trains, and proposing dynamic data capture methods that inform and enable a scientific planning process.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Photos; References; Tables;
  • Pagination: 21p

Subject/Index Terms

Filing Info

  • Accession Number: 01763515
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02566
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:03AM