A Simulation-based Optimization Approach of the Public Transport Network for A Multiple-core City, Kabul

Abstract

Kabul is the capital city of Afghanistan with the massive expansion of population since 2001. By 2025 Kabul’s population is projected to increase by 769; 539 inhabitants. This growth will not be only in the inner city, but with higher growth rate in the urban-cores, affecting the city structure morphologically as well as functionally. Before 2001, Kabul was a monocentric city with a dominant inner city; nowadays due to the rapid growth of population, Kabul is becoming morphologically polycentric city which needs transformation to a functionally polycentric shape. The fact that Kabul is becoming a polycentric city (with stronger urban - cores) makes it necessary to adapt its public transport network to this fact. Nowadays, around 58% of the trips between urban-cores are made by cars causing more congestion, emission, and high user cost. To overcome this problem, it is most likely to improve the public transport system capable of attracting more people to use it rather than cars. So, the idea attained here is to propose a new line linking the urban-cores to each other using the Headway-based Assignment procedure of the VISUM macro simulation software. This new line pretends to strengthen the urban-cores and create a more polycentric public transport network. Two alternatives will be studied as Alternative - 1 follows the present planning of the lines in city and urban-cores with buses that have a distance between stops of around 400 meters. The alternative - 2 shows planning for polycentrism, with the distance between stops around 800 meters and a speed higher than the alternative - 1. Furthermore, for each alternative different routes will be studied with respect to the base year 2016, and the target year 2025. Finally, all the alternatives and sub-alternatives will be compared among themselves and with the reference scenario to find the one which would improve the multi-core structure of Kabul effectively.