Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Prediction of delays in public transportation using neural networks

 
: Peters, J.; Emig, B.; Jung, M.; Schmidt, S.

Mohammadian, M. ; IEEE Computational Intelligence Society; European Society for Fuzzy Logic and Technology -EUSFLAT-; European Neural Network Society:
International Conference on Computational Intelligence for Modelling, Control and Automation 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce. Proceedings. Vol.2 : 28 - 30 Nov. 2005, Vienna, Austria; CIMCA 2005 jointly with IAWTIC 2005
Los Alamitos, Calif.: IEEE Computer Society, 2006
ISBN: 0-7695-2504-0
pp.92-97
International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA) <2005, Vienna>
International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC) <2005, Vienna>
English
Conference Paper
Fraunhofer IGD ()
neural network; data analysis; simulation and modeling; prediction

Abstract
The project the authors of this paper are involved in is titled "System for intelligent realtime timetable optimization and monitoring". The objective is to develop a system being able to use delay-predictions for real-timedelay-monitoring, and in the long term, for a timetable optimization in the range of train networks. The presented paper deals with the part of the system responsible for processing existing delays in the network to generate delay predictions for depending trains in the near future. Therefore a rule-based system was developed, processing a set of predefined rules with the input of a specific delay in a deterministic manner, delivering a resulting delay-scenario as output. This rule-based system was used as a comparison to the specially developed neural network in order to evaluate the accuracy and faculty of abstraction of such an artificially intelligent component. An excerpt of the real train network of the Deutsche Bahn was the basis for this research, for simulation purposes we used the SNNS (Stuttgart Neural Network Simulator) [5]. At the end of this paper we can draw a conclusion in favour of the neural network, which is able to abstract from known delay constellations.

: http://publica.fraunhofer.de/documents/N-48525.html