Enhancing intelligent agent collaboration for flow optimization of railroad traffic |
| |
Authors: | Jeremy Blum Azim Eskandarian |
| |
Affiliation: | 1. Optit s.r.l., Viale Amendola 56/D, 40026 Imola (BO), Italy;2. Departament d’Economia i Empresa, Universitat Pompeu Fabra and Barcelona GSE, Trias-Fargas 25, 08005 Barcelona, Spain;3. DEI, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy |
| |
Abstract: | Intelligent agents have successfully solved the train pathing problem on a small portion of railroad network [Tsen, 1995, Ph.D. Thesis, Carnegie Mellon University, USA]. As the railroad network grows, it is imperative that the agents collaborate to operate as efficiently as possible. In this paper, the authors demonstrate a collaboration protocol based on a conditional measure of agent effectiveness. Because agent effectiveness is not directly measurable, a suitable metric for agent effectiveness is introduced. Where typically agents run with uniform frequency, the collaboration protocol schedules the agents with a frequency proportional to their expected effectiveness. This protocol introduced a 10-fold improvement in the agent efficiency when tested with a simulation program on a portion of the Burlington Northern railroad. |
| |
Keywords: | Intelligent agent collaboration Network optimization Railroad routing Train pathing |
本文献已被 ScienceDirect 等数据库收录! |
|