Improving Addis Ababa light railway transit using a combined Monte Carlo simulation and queuing theory model: Data and model dual-driven approach

Abstract

The primary issues affecting passenger satisfaction of AALRT customers are congestion and long waiting lines. Consequently, the main objective of this study was to develop a model aimed at improving AALRT service. To achieve this goal, the research first examined the congestion conditions of AALRT, followed by an analysis of the properties of the primary data. Next, a new model was developed using the integrated MCQT approach. Finally, this model and the AALRT service were validated through a marginal analysis focusing on service quality, profitability, and the reduction of environmental pollution. The study utilized primary data gathered from selected stations and secondary data obtained from the AALRT revenue office. The data analysis employed an MCQT model based on the probability distributions of boarding and alighting passengers across all corridors, directions, and design periods.

The findings indicate that the AALRT service experiences congestion in the west-east corridor while being underutilized in the north-south corridor. The probability distribution of passenger flows on the AALRT appears to follow uniform, binomial, or negative binomial distributions. In 2019, a maximum of 7 and 3 tramcars per hour was needed for both the west-east and north-south corridors respectively, with the potential to increase to a maximum of 8 double and 8 single tramcars per hour. Overall, the new model enhances service quality, profitability, and greenhouse gas (GHG) reductions in Addis Ababa's public transport system. In summary, the integrated MCQT effectively addresses the limitations of queuing theory, Markov chains, and other related theories.