Ant Colony Optimization Algorithm for Fuzzy Transport Modelling

• Autorzy: Fidanova, Stefka , Roeva, Olympia , Ganzha, Maria
• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Public transport plays an important role in our live. The good service is very important. 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 modelling 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.

Słowa kluczowe

PL

teoria zbiorów rozmytych; optymalizacja; transport publiczny

EN

fuzzy set theory; optimisation; public transport

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 237--240
• Opis fizyczny: Bibliogr. 13 poz., wz., tab.

Twórcy

autor

Fidanova, Stefka

• IICT, BAS Sofia, Bulgaria,

autor

Roeva, Olympia

• IBPhBME, BAS Sofia, Bulgaria,

autor

Ganzha, Maria

• SRI, PAS Warsaw, Poland,

Bibliografia

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• 9. F. S. Hanseler,N. Molyneaux, M. Bierlaire, and A. Stathopoulos, Schedule-based estimation of pedestrian demand within a railway station, Proceedings of the Swiss Transportation Research Conference (STRC) 14-16 May, 2014.
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Uwagi

1. Work presented here is partially supported by the National Scientific Fund of Bulgaria under grant DFNI DN12/5 “Efficient Stochastic Methods and Algorithms for Large-Scale Problems”, Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program and by the Bulgarian scientific fund by the grant DFNI DN 02/10.

2. Track 1: Artificial Intelligence

3. Technical Session: 13th International Workshop on Computational Optimization

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).

Identyfikatory

DOI

10.15439/2020F15