Abstract
Public transport plays an important role in our live. It is very important to have a reliable service. Up to 1000 km, trains and buses play the main role in the public transport. The number of the people and which kind of transport they prefer is important information for transport operators. In this paper is proposed algorithm for transport modeling and passenger flow, based on Ant Colony Optimization method. The problem is described as multi-objective optimization problem. There are two optimization purposes: minimal transportation time and minimal price. Some fuzzy element is included. When the price is in a predefined interval it is considered the same. Similar for the starting traveling time. The aim is to show how many passengers will prefer train and how many will prefer buses according their preferences, the price or the time. The InterCriteria Analysis (ICrA) is applied over numerical results obtained from ACO algorithm in order to estimate the algorithm performance. The ICrA results show that the proposed ACO algorithm performs very well.
