The main feature of Addis Ababa's light railway (AALR) ticketing window is either congestion or underutilized 106 – 137 and 12 - 18 percent respectively. That is why the study set the main objective to analyze and optimize AALR ticketing windows. So, the researcher first studied the problem for the specified ticketing windows. Secondly, establish and analyze the performance of a new model for the current and future design periods. And finally, it recommends the number of clerks based on the findings. The congestion and underutilization problem of each ticketing window is solved through a mathematical method called Queue Theory with a combination of special and statically analysis methods and train timetable optimization of urban railway by Arena. The study indicates that the congestion rate of the AALR at the congested station is between 106 & 137%. Similarly, the underutilization of the ticketing window is between 12 & 18%. Therefore, the result indicates that adding a single clerk could reduce the traffic intensity to 82% in congested windows. Similarly, reducing to 2 clerks can improve up to 35.5% the underutilization window. Finally, the optimum number of clerks required for the rest of the design period is determined and summarized using a combination of queuing theory, spatial and analytical method and Arena software timetable optimization.
Published in | Urban and Regional Planning (Volume 8, Issue 3) |
DOI | 10.11648/j.urp.20230803.14 |
Page(s) | 52-58 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Optimization, Queue Theory, Waiting Time
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APA Style
Tamene Taye Worku, Yeserah Gebeyehu Asegie. (2023). Analysis and Optimization of Addis Ababa Light Railway Ticketing Window. Urban and Regional Planning, 8(3), 52-58. https://doi.org/10.11648/j.urp.20230803.14
ACS Style
Tamene Taye Worku; Yeserah Gebeyehu Asegie. Analysis and Optimization of Addis Ababa Light Railway Ticketing Window. Urban Reg. Plan. 2023, 8(3), 52-58. doi: 10.11648/j.urp.20230803.14
AMA Style
Tamene Taye Worku, Yeserah Gebeyehu Asegie. Analysis and Optimization of Addis Ababa Light Railway Ticketing Window. Urban Reg Plan. 2023;8(3):52-58. doi: 10.11648/j.urp.20230803.14
@article{10.11648/j.urp.20230803.14, author = {Tamene Taye Worku and Yeserah Gebeyehu Asegie}, title = {Analysis and Optimization of Addis Ababa Light Railway Ticketing Window}, journal = {Urban and Regional Planning}, volume = {8}, number = {3}, pages = {52-58}, doi = {10.11648/j.urp.20230803.14}, url = {https://doi.org/10.11648/j.urp.20230803.14}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.urp.20230803.14}, abstract = {The main feature of Addis Ababa's light railway (AALR) ticketing window is either congestion or underutilized 106 – 137 and 12 - 18 percent respectively. That is why the study set the main objective to analyze and optimize AALR ticketing windows. So, the researcher first studied the problem for the specified ticketing windows. Secondly, establish and analyze the performance of a new model for the current and future design periods. And finally, it recommends the number of clerks based on the findings. The congestion and underutilization problem of each ticketing window is solved through a mathematical method called Queue Theory with a combination of special and statically analysis methods and train timetable optimization of urban railway by Arena. The study indicates that the congestion rate of the AALR at the congested station is between 106 & 137%. Similarly, the underutilization of the ticketing window is between 12 & 18%. Therefore, the result indicates that adding a single clerk could reduce the traffic intensity to 82% in congested windows. Similarly, reducing to 2 clerks can improve up to 35.5% the underutilization window. Finally, the optimum number of clerks required for the rest of the design period is determined and summarized using a combination of queuing theory, spatial and analytical method and Arena software timetable optimization.}, year = {2023} }
TY - JOUR T1 - Analysis and Optimization of Addis Ababa Light Railway Ticketing Window AU - Tamene Taye Worku AU - Yeserah Gebeyehu Asegie Y1 - 2023/07/26 PY - 2023 N1 - https://doi.org/10.11648/j.urp.20230803.14 DO - 10.11648/j.urp.20230803.14 T2 - Urban and Regional Planning JF - Urban and Regional Planning JO - Urban and Regional Planning SP - 52 EP - 58 PB - Science Publishing Group SN - 2575-1697 UR - https://doi.org/10.11648/j.urp.20230803.14 AB - The main feature of Addis Ababa's light railway (AALR) ticketing window is either congestion or underutilized 106 – 137 and 12 - 18 percent respectively. That is why the study set the main objective to analyze and optimize AALR ticketing windows. So, the researcher first studied the problem for the specified ticketing windows. Secondly, establish and analyze the performance of a new model for the current and future design periods. And finally, it recommends the number of clerks based on the findings. The congestion and underutilization problem of each ticketing window is solved through a mathematical method called Queue Theory with a combination of special and statically analysis methods and train timetable optimization of urban railway by Arena. The study indicates that the congestion rate of the AALR at the congested station is between 106 & 137%. Similarly, the underutilization of the ticketing window is between 12 & 18%. Therefore, the result indicates that adding a single clerk could reduce the traffic intensity to 82% in congested windows. Similarly, reducing to 2 clerks can improve up to 35.5% the underutilization window. Finally, the optimum number of clerks required for the rest of the design period is determined and summarized using a combination of queuing theory, spatial and analytical method and Arena software timetable optimization. VL - 8 IS - 3 ER -