Optimizing urban public transportation with ant colony algorithm

Elena Kochegurova, Ekaterina Gorokhova

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

Transport system in most cities has some problems and should be optimized. In particular, timetable of the city public transportation needs to be changed. Metaheuristic methods for timetabling were considered the most efficient. Ant algorithm was chosen as one of these methods. It was adapted for optimization of an urban public transport timetable. A timetable for one bus route in the city of Tomsk, Russia was created on the basis of the developed software. Different combinations of parameters in ant algorithm allow obtaining new variants of the timetable that better fit passengers’ needs.

Original languageEnglish
Pages (from-to)489-497
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9875 LNCS
DOIs
Publication statusPublished - 2016
Event8th International Conference on Computational Collective Intelligence, ICCCI 2016 - Halkidiki, Greece
Duration: 28 Sep 201630 Sep 2016

Keywords

  • Ant algorithm
  • Optimization
  • Timetable
  • Transport

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Optimizing urban public transportation with ant colony algorithm'. Together they form a unique fingerprint.

Cite this