Minds for Mobile Agents

M4MA

There is an increasing need to quantitatively describe and anticipate the way in which people walk around in public spaces (e.g., to maintain social distance, avoid collisions with automated vehicles etc.). The Predictive Pedestrian framework models the way in which people make step (direction and speed) decisions based on their spatial goals (e.g., to find items in supermarket) and priorities (e.g., to avoid other shoppers, stay near friends etc.). However, this fine-grained description of the cognitive states of simulated mobile agents is computationally demanding, making it difficult to apply in practice. This project efficiently implemented key calculations so that simulation of groups of pedestrians were sped up by an order of magnitude, achieving near real time for complex scenarios (e.g., shoppers in a crowded supermarket). A similar speed up was achieved in calculating the likelihood of model parameters based on observed movement trajectories. This will enable estimation of causes of individual differences in the way diverse people move around in different scenarios, underpinning more realistic simulations of pedestrian behavior that can inform and improve public policy and technological developments, such as the design of buildings and urban spaces and safer interactions with automated systems.

Participating organisations

Environment & Sustainability
Environment & Sustainability
Netherlands eScience Center
University of Amsterdam

Output

Team

DM
Dora Matzke
Principal investigator
University of Amsterdam
EV
Eva Viviani
Research Software Engineer
Netherlands eScience Center
Malte Lüken
Malte Lüken
Research Software Engineer
Netherlands eScience Center
CT
Charlotte Tanis
AH
TB
Tessa Blanken
ML
Michael Lees
University of Amsterdam
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center

Related software

m4ma

M4

An R package containing C++ implementations to speed up the simulation and parameter estimation of the Predictive Pedestrian model.

Updated 12 months ago
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