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

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