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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 919-930 |
| Number of pages | 12 |
| Journal | Transportation Research Part A: Policy and Practice |
| Volume | 36 |
| Issue number | 10 |
| DOIs | |
| State | Published - Dec 1 2002 |
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All Science Journal Classification (ASJC) codes
- Management Science and Operations Research
- Civil and Structural Engineering
- Transportation
Cite this
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Enhancing intelligent agent collaboration for flow optimization of railroad traffic. / Blum, Jeremy Joseph; Eskandarian, Azim.
In: Transportation Research Part A: Policy and Practice, Vol. 36, No. 10, 01.12.2002, p. 919-930.Research output: Contribution to journal › Article
TY - JOUR
T1 - Enhancing intelligent agent collaboration for flow optimization of railroad traffic
AU - Blum, Jeremy Joseph
AU - Eskandarian, Azim
PY - 2002/12/1
Y1 - 2002/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0036894026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036894026&partnerID=8YFLogxK
U2 - 10.1016/S0965-8564(02)00002-2
DO - 10.1016/S0965-8564(02)00002-2
M3 - Article
AN - SCOPUS:0036894026
VL - 36
SP - 919
EP - 930
JO - Transportation Research, Part A: Policy and Practice
JF - Transportation Research, Part A: Policy and Practice
SN - 0965-8564
IS - 10
ER -