Analysis of Passenger Demand Forecasting Models in The Context Of AALRT

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Date

2016-10

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Publisher

AAU

Abstract

High rate growth of population and economic activity of Addis Ababa increases the traffic flow of the city and results in high congestion of traffic and unbalance between the demand and mode of transport. To minimize this congestion of traffic and unbalance appropriate passenger demand forecasting model is needed. So, this study tries to fill this gap. To select a suitable rail passenger demand forecasting model the two most common rail passenger demand forecasting models are compared based on their limitation and delimitation in the context of AALRT. According to this comparison the four stage passenger demand forecasting is found better than the elasticity passenger demand forecasting model for new rail service. Then, the analysis of estimating of passenger demand forecasting has been done by using the four stage passenger demand forecasting model. The four stage model has trip generation, trip distribution, mode choice or modal split and trip assignment stages. The study analysed through the first three stages because AALRT has only one line to travel from origin to destination of the trip maker. So, in this study the data analysis has been based on the data collected from different sources (like central statistics agency, city government of Addis Ababa education bureau, etc.). The data analysis result shows 11,666,568 trips are generated in Addis Ababa and 876072 total trips are generated in Kirkos sub city in the generation step; the generated trips in Kirkos has distributed into neighbor sub cities of Kirkos and in itself (Kirkos, Arada, Lideta, Bole Nifas Silk Lafto and Yeka is 662986.8, 6694, 66086, 33324, 24023 and 57766 respectively); choose the best transport mode to travel from origin zone to the destination zone. As the finding of this thesis shows that the four stage rail passenger demand forecasting model is a better model in the context of AALRT. Finally the study recommended for ERC and other organizations to use four stage passenger demand forecasting model to estimate their demands.

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Keywords

AALRT, passenger demand forecasting, four stage model

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