What is it about?
This work aims to model pedestrian mobility in the city through an agent-based reinforcement learning model to represent the mobility patterns better. This improved mobility simulation will help researchers and decision makers to model the cities mobility patterns more accurately, therefore leading to better designed cities.
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Photo by Markus Spiske on Unsplash
Why is it important?
This agent-based model demonstrates a useful learning graph, showing the accuracy of the model is distinct.
Perspectives
For the designing of future cities, understanding the mobility patterns is important and this work's contribution in this regard should not be underestimated.
Burak Bek
HafenCity Universitat Hamburg
Read the Original
This page is a summary of: Innovative Urban Design Simulation: Utilizing Agent-Based Modelling through Reinforcement Learning, November 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3638209.3638213.
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