Evaluating the impact of AI-based priced parking with social simulation

Kappenberger, Jakob ; Theil, Kilian ; Stuckenschmidt, Heiner

DOI: https://doi.org/10.1007/978-3-031-19097-1_4
URL: https://link.springer.com/chapter/10.1007/978-3-03...
Document Type: Conference or workshop publication
Year of publication: 2022
Book title: Social Informatics: 13th International Conference, SocInfo 2022, Glasgow, UK, October 19–21, 2022, proceedings
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 13618
Page range: 54-75
Conference title: SocInfo 2022
Location of the conference venue: Glasgow, UK
Date of the conference: 19.-21.10.2022
Publisher: Hopfgartner, Frank ; Jaidka, Kokil ; Mayr, Philipp ; Jose, Joemon ; Breitsohl, Jan
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-031-19096-4 , 978-3-031-19097-1
ISSN: 0302-9743 , 1611-3349
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): urban informatics , fair machine learning , social simulation
Abstract: Across the world, increasing numbers of cars in urban centers lead to congestion and adverse effects on public health as well as municipal climate goals. Reflecting cities’ ambitions to mitigate these issues, a growing body of research evaluates the use of innovative pricing strategies for parking, such as Dynamic Pricing (DP), to efficiently manage parking supply and demand. We contribute to this research by exploring the effects of Reinforcement Learning (RL)-based DP on urban parking. In particular, we introduce a theoretical framework for AI-based priced parking under traffic and social constraints. Furthermore, we present a portable and generalizable Agent-Based Model (ABM) for priced parking to evaluate our approach and run extensive experiments comparing the effect of several learners on different urban policy goals. We find that (1) outcomes are highly sensitive to the employed reward function; (2) trade-offs between different goals are challenging to balance; (3) single-reward functions may have unintended consequences on other policy areas (e.g., optimizing occupancy punishes low-income individuals disproportionately). In summary, our observations stress that fair DP schemes need to account for social policy measures beyond traffic parameters such as occupancy or traffic flow.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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ORCID: Kappenberger, Jakob ORCID: 0000-0003-1290-0199 ; Theil, Kilian ORCID: 0000-0002-5446-8894 ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859 ["search_editors_ORCID" not defined] Hopfgartner, Frank ; Jaidka, Kokil ; Mayr, Philipp ORCID: 0000-0002-6656-1658 ; Jose, Joemon ; Breitsohl, Jan

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