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Updated: Jun 12, 2025

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
Published on: April 13, 2016
Nonlinear dynamical social and political prediction algorithm for city planning and public participation using the
R Bader1, S Linke2, S Gernert3
1Institute of Systematic Musicology, University of Hamburg, Neue Rabenstr. 13, 20354 Hamburg, Germany.
Abstract:
A nonlinear-dynamical algorithm for city planning is proposed as an impulse pattern formulation (IPF) for predicting relevant parameters such as health, artistic freedom, or financial developments of different social or political stakeholders over the cause of a planning process. The IPF has already shown high predictive precision at low computational cost in musical instrument simulations, brain dynamics, and human-human interactions. The social and political IPF consists of three basic equations of system state developments, self-adaptation of stakeholders, two adaptive interactions, and external impact terms suitable for respective planning situations. Typical scenarios of stakeholder interactions and developments are modeled by adjusting a set of system parameters. These include stakeholder reaction to external input, enhanced system stability through self-adaptation, stakeholder convergence due to adaptive interaction, as well as complex dynamics in terms of fixed stakeholder impacts. A workflow for implementing the algorithm in real city planning scenarios is outlined. This workflow includes machine learning of a suitable set of parameters suggesting best-practice planning to aim at the desired development of the planning process and its output.
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