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Reinforcement Learning to Reach Equilibrium Flow on Roads in Transportation System
Ref: CISTER-TR-190612       Publication Date: 6, Mar, 2019

Reinforcement Learning to Reach Equilibrium Flow on Roads in Transportation System

Ref: CISTER-TR-190612       Publication Date: 6, Mar, 2019

Abstract:
Traffic congestion threats the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and have a different decision on transportation modes. Modification and improvement of these systems affect commuters’ perspective and social welfare. In this study, the effect of equilibrium road flow on commuters’ utilities with a different type of transportation mode will be discussed. A simple network with two modes of transportation will be illustrated to test the efficiency of minority game and reinforcement learning in commuters’ daily trip decision making based on time and mode. The artificial society of agents is simulated to analyze the results.

Authors:
Hajar Baghcheband


Events:

DSIE 2019
6, Mar, 2019
14th Doctoral Symposium in Informatics Engineering
Porto, Portugal


14th International Conference on Software Technologies (DSIE 2019).
Porto, Portugal.



Record Date: 12, Jun, 2019