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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12927
Title: Adopting Freight Demand Forecasting Model for Addis Ababa-Djibouti Railway
???metadata.dc.contributor.*???: Birhanu Beshah (PhD)
Arefeayne, Amare
Keywords: Freight Demand;Addis Ababa-Djibouti Railway;Forecasting
Issue Date: Oct-2016
Publisher: AAU
Abstract: Freight transportation is an essential component of any economic activity which makes a continuous change as a result of growth but many economic activities depend on traffic congestion and truck travel time along the origins and destinations which makes series problem mainly for land locked countries like Ethiopia. This is because government and organization pay undesired tax for the stay of goods at the port. In order to solve this problem different countries use different forecasting models which are designed to predict the future need of the country. This thesis work identifies major freight forecasting projection models, discusses those models, chooses the better model based on the merits and demerits of each model and forecasts with one of the classic freight forecasting model which is called four-stage aggregate model (hybrid with its own parameters) to show how it works in the context of Ethiopia by taking traffic congestion as a main problem. As part of this work trip generated using growth factor and linear regression and mode choice along the path has been done by incorporating the truck traffic count, commodity tonnage share and an expense given to each item. This hybrid four-stage aggregate model which begins with trip generation step and gives a result of 3,351,406 trips when working with Annual Growth Factor (AGF) and 57,974 trips with linear regression method. And a comparison between this a model and a model forecasted by ERC has been compared and found ERC model which generates 119,419 trips nearly comparable with linear regression method even though the parameters used here are different. From the total number of trips generated trains took 79 percent mode share and truck took remaining modal share. The distribution and an assignment step is not done because, the demand investigating here focuses only origin and destination. Finally the thesis find out forecasting with four-stage aggregate model(Hybrid model) which uses traffic counts, commodity tonnage share and an expense growth rate given for each items as input parameter is better model in order to generate number of trips along the line. This is because import and export tonnage rates can easily affected by the influence of economy, industrial location patterns, globalization of business, fuel prices, environmental factors and others which this all cumulatively can affect the traffic congestion.
URI: http://hdl.handle.net/123456789/12927
Appears in Collections:Railway Engineering

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