Analysis of Train Energy Consumption Reduction by Passing Low Passenger Flow Stations in Off-peak Hour Case Study on the Line of E-W Addis Ababa LRT
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Date
2015-05
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Addis Ababa University
Abstract
In this thesis in order to make the analysis of train energy consumption reduction, the E-W line
of AA LRT has been taken. The length of the route is about 16.76 km, and there are 22 stations.
This gives an average of about 798 m between each two stations. The passenger flow of E-W
line is forecasted as 734.4 thousands persons/day based on the passenger transport survey. There
are 41 trains in four directions and it will enable the light rail transit to provide transportation
service to 15,000 people per hour in one direction.
The objective of the research is to analyze energy consumption reduction of AA LRT E-W line
for one complete cycle trip. The problem has been formulated by reducing number of stations in
off-peak hour of train operation considering low passenger flow of stations. There has been
selection of six low passenger flow stations which are EW5, EW7, EW8, EW14, EW18 and
EW20. They are selected based on passenger flow of stations and spacing between stations.
The model of the train motion has been formulated by considering the important acting force
components, such as tractive effort and train resistances. The method of analyzing train energy
consumption model was based on power and time, and the software which is used to create the
simulation of the train energy consumption has been analyzed using MATLAB SIMULINK loop.
It has been observed that energy consumption of train has increased significantly with
increasing number of train stops at stations. The results have shown that there is a very large
difference in the energy consumption in the case of number of train stops at stations. From the
analysis the train, by passing six low passenger flow stations, consumed only about 79.42% of
energy consumption of the train stopping at all stations for one complete cycle trip per train.
That is, reducing the number of train stops at stations gives a large reduction of energy
consumption.
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Keywords
Predict low passenger flow stations, model of train energy consu