AI for Happiness: Pedestrian Path Generation through Agent-Based Simulations with Deep Reinforcement Learning

Ayse Glass; Burak Bek; Eylul K. Menges; Iryna Okhrin; Jörg Noennig; Mariela Rossana Sanchez; Pramod Baddam; René Jäkel; Roman Glass

AI for Happiness: Pedestrian Path Generation through Agent-Based Simulations with Deep Reinforcement Learning

Ayse Glass, Burak Bek, Eylul K. Menges, Iryna Okhrin, Jörg Noennig, Mariela Rossana Sanchez, Pramod Baddam, René Jäkel, Roman Glass

Abstract

AI integration in Smart Cities, primarily through agent-based simulations,
holds transformative potential for understanding and enhancing citizen
behavior. Striking a balance between complexity and computational
feasibility is essential. Our research question is, how can we make agents
behave more realistically? We assumed that happiness is a motivating factor
for the mobility. Insights from a survey of 130 citizens inform our weightings.
We used reinforcement learning (RL) as a method and Q-learning as an
algorithm to generate a baseline, further enhanced with neural networks for
adaptability. This study contributes to data-driven urban design by offering
efficient intelligent agent solutions. The research lays foundations for smart
agents in urban design, which can be used to generate synthetic data.

Originalsprache	Englisch
Seiten	139
Seitenumfang	1
Publikationsstatus	Veröffentlicht - 10 Okt. 2023
Veranstaltung	Digital Total: Computing & Data Science an der Universität Hamburg und in der Wissenschaftsmetropole Hamburg - Universität Hamburg, Hamburg, Deutschland Dauer: 9 Okt. 2023 → 10 Okt. 2023

Tagung/Konferenz

Tagung/Konferenz	Digital Total
Land/Gebiet	Deutschland
Ort	Hamburg
Zeitraum	9/10/23 → 10/10/23

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 11 – Nachhaltige Städte und Gemeinschaften

Andere Dateien und Links

https://www.hcds.uni-hamburg.de/en/current/all-events/digital-total/digital-total-boa.pdf

Dieses zitieren

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abstract = "AI integration in Smart Cities, primarily through agent-based simulations, holds transformative potential for understanding and enhancing citizen behavior. Striking a balance between complexity and computational feasibility is essential. Our research question is, how can we make agents behave more realistically? We assumed that happiness is a motivating factor for the mobility. Insights from a survey of 130 citizens inform our weightings. We used reinforcement learning (RL) as a method and Q-learning as an algorithm to generate a baseline, further enhanced with neural networks for adaptability. This study contributes to data-driven urban design by offering efficient intelligent agent solutions. The research lays foundations for smart agents in urban design, which can be used to generate synthetic data.",

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T1 - AI for Happiness

T2 - Digital Total

AU - Glass, Ayse

AU - Bek, Burak

AU - Menges, Eylul K.

AU - Okhrin, Iryna

AU - Noennig, Jörg

AU - Sanchez, Mariela Rossana

AU - Baddam, Pramod

AU - Jäkel, René

AU - Glass, Roman

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AB - AI integration in Smart Cities, primarily through agent-based simulations, holds transformative potential for understanding and enhancing citizen behavior. Striking a balance between complexity and computational feasibility is essential. Our research question is, how can we make agents behave more realistically? We assumed that happiness is a motivating factor for the mobility. Insights from a survey of 130 citizens inform our weightings. We used reinforcement learning (RL) as a method and Q-learning as an algorithm to generate a baseline, further enhanced with neural networks for adaptability. This study contributes to data-driven urban design by offering efficient intelligent agent solutions. The research lays foundations for smart agents in urban design, which can be used to generate synthetic data.

KW - Artificial intelligence

KW - Computation

KW - Digital Tools

KW - pedestrian orientation

KW - pedestrian route networks

KW - Urban analysis

KW - data science

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M3 - Meeting Abstract

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