Article
DEFINING AND ASSESSING CONGESTION INSIDE METRO TRAINS AND AT STATION: CASE STUDY OF DELHI METRO, INDIA
DOI: 10.7708/ijtte.2017.7(1).07
7 / 1 / 93-107 Pages
Author(s)
Amit Kumar Jain - Indian Railways, Delhi, India -
Abstract
Congestion in metro trains and stations during peak hours is one of the major challenge being faced by the metro train operators. The metro trains and stations typically remain very crowded during the peak hours and relatively empty during the non peak hours. An optimum combination of demand and supply side measures may be effective to ease out peak hour congestion inside the metro trains. However, the first step is to assess the congestion in quantifiable terms so that the efficacy of demand and supply management measures may be assessed objectively. This paper discusses the concepts of congestion at walkway & Platform of station and inside the trains. The concept of Level of Service (LOS) to define the congestion has been presented. The LOS for walkway and platform has been defined on the basis of Fruin’s model. The present LOS in F Block of Rajiv Chowk Metro station of Delhi Metro in India at different point of time in a day has been observed on the basis of CCTV images. The present LOS at one of the busiest Platform of Rajiv Chowk metro station has also been assessed using the CCTV images. The criteria for LOS inside metro trains has been developed and the LOS inside the train has been assessed for a controlled number of users inside the trains. It has been observed that the congestion inside train is the most critical which requires to be addressed while adopting the demand and supply measure tools. The LOS at the busiest platform, Walkway and inside trains at Rajiv Chowk station of yellow line (Busiest line) of Delhi Metro in India are found to be C, D & E respectively. Since LOS is the worst inside trains, any criteria to ease congestion during peak hour should be focused on improving LOS inside the trains. The metro train operators should aspire to achieve an LOS of D inside the trains.
Number of downloads: 2489
Keywords:
urban transportation;
transport demand management;
elasticity;
differential pricing;
congestion;
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