Annals of Computer Science and Information Systems, Volume 21

DOI: http://dx.doi.org/10.15439/2020F15

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 237–240 (2020)

Full text

Abstract. 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.

References

1. A. El Amaraoui A.,K. Mesghouni, Train Scheduling Networks under Time Duration Uncertainty, In proc. of the 19th World Congress of the Int. Federation of Automatic Control, 2014, 8762–8767.
2. A. A. Assad, Models for Rail Transportation, Transportation Research Part A General, 143, 1980, 205–220.
3. E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999.
4. O. Diaz-Parra, J. A. Ruiz-Vanoye, B. B. Loranca, A. Fuentes-Penna, R.A. Barrera-Camara, A Survey of Transportation Problems Journal of Applied Mathematics Volume 2014 (2014), Article ID 848129, 17 pages.
5. Ch. Dong, Zh. Xiong, Ch. Shao, H. Zhang A spatial–temporal-based state space approach for freeway network traffic flow modelling and prediction Journal of Transportmetrica A:Transport Science 11(7) (2015), 574-560.
6. M. Dorigo, T. Stutzle. Ant Colony Optimization, MIT Press, 2004.
7. S. Fidanova, K. Atanasov Generalized Net Model for the Process of Hibride Ant Colony Optimization Comptes Randus de l’Academie Bulgare des Sciences, 62(3), 2009, 315–322.
8. Fidanova S.. Metaheuristic Method for Transport Modelling and Optimization Studies in Computational Intelligence, 648, Springer, 2016, 295-302.
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.
10. J. G. Jin, J. Zhao, D. H. Lee, A Column Generation Based Approach for the Train Network Design Optimization Problem, J. of Transportation Research, 50(1), 2013, 1–17.
11. V. K. Mathur, How Well do we Know Pareto Optimality? J. of Economic Education 22(2), 1991, 172–178.
12. N. Molyneaux, F. Hanseler, M. Bierlaire, Modelling of train-induced pedestrian flows in rail- way stations, Proceedings of the Swiss Transportation Research Conference (STRC) 14-16 May, 2014.
13. C. Woroniuk, M. Marinov, Simulation Modelling to Analyze the Current Level of Utilization of Sections Along Rail Rout, J. of Transport Literature, textbf7(2), 2013, 235–252.