Multi-objective Optimization of Train Speed Profiles: The Case of Ayat to Megenagna Line of Addis Ababa Light Rail Transit
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Date
2014-07
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Addis Ababa University
Abstract
The plan for the operation of trains on the Addis Ababa Light Rail Transit (AALRT) is based on
a fixed interval of riding between stations. Fixed riding time between stations will have the effect
of inefficient energy consumption by the trains. Furthermore, the transportation capacity of the
network cannot be optimal. By properly managing the reference trajectories the trains use, it is
possible to have optimum train operation with respect to energy consumption as well as network
capacity.
In this thesis, an optimization of train speed profiles is done. A multi-objective optimization
problem has been formulated by making energy and time as the components of the two element
objective vector function. A point mass model of the operation of trains has been developed by
considering all the important force components acting on the train. The distance to travel
between stations is discretized into 20 equal length elements where a two stage solution
procedure has been applied to get to the final results. The first stage of the solution procedure is
the application of a multi-objective genetic algorithm based optimization technique taking vector
of riding modes as the decision variable. Using the developed algorithms for the calculation of
cost functions for every type of riding mode, the MATLAB optimization toolbox determines a
Pareto-optimal set of riding modes. The second stage of the solution process smoothes out the
results found in the previous stage of the solution process without bringing about considerable
change in the values of the cost functions.
Different solutions for every section from Ayat station to Megenagna station are generated and
they are essentially tradeoff solutions. It has been observed that the fastest ride between stations
can be completed within a time of less than 3 minutes. This is equivalent to a 50% reduction in
riding time over the plan. By shifting from the fastest to the slowest trajectories, it is possible to
save up to 38.18% of energy, while 23.98% reduction in riding time can be achieved by
preferring the fastest profiles over the slowest ones.
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Keywords
Speed profile, Energy consumption, Running time, Multi-objective, Optimization