Route learning in iterated transportation simulations

Kai Nagel

Dept. of Computer Science, ETH Zürich, 8092 Zürich, Switzerland, nagel@inf.ethz.ch

Abstract:

Transportation simulation packages need to generate the routes along which vehicles move through the network, and these routes need to be sensitive to congestion. The traditional solution to this problem is static assignment. Unfortunately, static assignment does not work when confronted with more realistic dynamics such as spatially extended and/or time-varying queues. This paper looks at issues which come up when moving away from the static, flow-based representation toward a dynamic, agent-based representation. The important difference is that learning and adaptation are moved away from the system and toward the agents. There is however rather a smooth crossover than a sharp transition between the two views, and intermediate methods are possible, with trade-offs between fast relaxation vs. realistic modeling of human behavior.