HCAISep 6, 2019

Calibrating Wayfinding Decisions in Pedestrian Simulation Models: The Entropy Map

arXiv:1909.03054v1
Originality Synthesis-oriented
AI Analysis

This work addresses the need for better uncertainty visualization in pedestrian simulation models, primarily for researchers and modelers in urban planning or crowd dynamics, but it is incremental as it builds on existing simulation frameworks.

The paper tackled the problem of visualizing uncertainty in pedestrian movement intentions within simulation models by introducing entropy maps, which depict decision randomness through heatmaps, and demonstrated their relevance for modelers in experiments.

This paper presents entropy maps, an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. In particular, entropy maps show the instantaneous level of randomness in decisions of a pedestrian agent situated in a specific point of the simulated environment with an heatmap approach. Experimental results highlighting the relevance of this tool supporting modelers are provided and discussed.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes