The use of the genetic algorithms for optimizing public transport schedules in congested urban areas

Abstract

Transit operators face with travel time variability issues related to the design of new transit lines or related to the improvement of the performances of existing lines, especially when transit vehicles without exclusive line. Nowadays, the availability of large data through automated monitoring allows more in-depth this phenomenon of variability in timetables to be pointed out with recorded data. The lack of schedule stability confuses the traveling public and thus the degree of dissatisfaction increases. This leads to a decrease of the public transportation users, especially because of those users having travel alternatives to public transportation. The reliability of the travel time and the vehicle operation according to a stable timetable are the most relevant attributes for the users fidelisation. At the same time, it is well known that the demand for public transportation in congested networks has a large variability over space and time. In this paper, we analyse the deviations from the planned (declared and publicly) schedule for a tram line in Bucharest city in different stations, in diferrent times of the day and in different days of the week, in order to build an optimisation model for needed adjustments to the planned schedule. The data are collected with the Automatic Vehicle Location (AVL) system installed on trams’ board. The number of the adjustments solutions is very large and the genetic algorithm is engaged for the optimisation model solving.