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