Case Studies on the Braess Paradox: Simulating Route Recommendation and Learning in Abstract and Microscopic Models

A new link in a traffic network may not reduce the total travel time, and, in fact, the travel time often increases as does the cost for commuters. This phenomenon is called the Braess Paradox, and it happens because drivers do not face the true social cost of an action. Although some measures have been proposed to minimize the effects of the paradox, it is not realistic to assume that the drivers would have all the necessary knowledge in order to compute their rewards from a point-of-view which is not their own. This paper discusses the effects of giving route recommendation to drivers in order to divert them to a situation in which the effects of the paradox are reduced. Two contributions are presented: a generalized cost function for the abstract model, which is valid for any number of drivers, and the calibration and results for a microscopic simulation, where the cost functions are not necessary anymore. These are replaced in the microscopic simulation by the real commuting time perceived by each driver. In all cases the authors use a learning mechanism to allow drivers to adapt to the changes in the environment. Different rates of drivers receive route recommendation with different rates of acceptance. Regarding the abstract model, the simulations show that it is useful to manipulate the route information given to agents, which allows the control system to divert them to the more convenient alternative from the point of view of both the overall system and the individual agents. The microscopic simulation successfully reproduced the results achieved with the abstract scenario with basic learning.

Language

  • English

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  • Accession Number: 01005779
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: Oct 18 2005 10:21PM