Multi-objective Optimization of Train Speed Profiles: The Case of Ayat to Megenagna Line of Addis Ababa Light Rail Transit

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Addis Ababa University


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.



Speed profile, Energy consumption, Running time, Multi-objective, Optimization