4 Search Results for "Heppenstall, Alison"

Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.21
Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy (Short Paper)

Authors: Robert Clay, Luke Archer, Alison Heppenstall, and Nik Lomax

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Rates of anxiety and depression are increasing due to financial stress caused by energy pricing with over half of UK homes unable to afford comfortable heating. UK Government policies to address this energy crisis have been implemented with limited evidence and substantial criticism. This paper applies the dynamic microsimulation MINOS, which utilises longitudinal Understanding Society data, to evidence change in mental well-being under the Energy Price Cap Guarantee and Energy Bill Support Scheme Policies. Results demonstrate an overall improvement in Short Form 12 Mental Component Score (SF12-MCS) both on aggregate and over data zone spatial areas for the Glasgow City region compared with a baseline of no policy intervention. This is work in progress and discussion highlights potential future work in other energy policy areas, such as Net Zero.
Cite as

Robert Clay, Luke Archer, Alison Heppenstall, and Nik Lomax. Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 21:1-21:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{clay_et_al:LIPIcs.GIScience.2023.21,
  author =	{Clay, Robert and Archer, Luke and Heppenstall, Alison and Lomax, Nik},
  title =	{{Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{21:1--21:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.21},
  URN =		{urn:nbn:de:0030-drops-189160},
  doi =		{10.4230/LIPIcs.GIScience.2023.21},
  annote =	{Keywords: Dynamic Microsimulation, Mental Health, Energy Poverty}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.29
Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow (Short Paper)

Authors: Zixin Feng, Qunshan Zhao, and Alison Heppenstall

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
With the new policy aimed at advancing the phase-out date for the sale of new petrol and diesel cars and vans to 2030, the electric vehicle (EV) market share is expected to rise significantly in the coming years. This necessitates a deeper understanding of the driving and charging behaviours of EV drivers to accurately estimate future charging demand distribution and benefit for future infrastructure development. Traditional data-based approaches are limited in illustrating the granular spatiotemporal dynamics of individuals. Recent studies that use conventional vehicle trajectory data also have the sampling bias problem, despite their analyses being conducted at a finer resolution. Moreover, studies that use simulation approaches are often either based on limited behaviour rules for EV drivers or implemented in an artificial grid environment, showing limitations in reflecting real-world situations. To address the challenges, this work introduces an agent-based model (ABM) with complex behaviour rules for EV drivers, taking into account the drivers’ sensitivities to financial and time costs, as well as route deviation. By integrating the simulation model with the origin and destination information of drivers, this work can contribute to a better understanding of the behaviour patterns of EV drivers.
Cite as

Zixin Feng, Qunshan Zhao, and Alison Heppenstall. Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 29:1-29:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{feng_et_al:LIPIcs.GIScience.2023.29,
  author =	{Feng, Zixin and Zhao, Qunshan and Heppenstall, Alison},
  title =	{{Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{29:1--29:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.29},
  URN =		{urn:nbn:de:0030-drops-189243},
  doi =		{10.4230/LIPIcs.GIScience.2023.29},
  annote =	{Keywords: Electric vehicles, agent-based modelling, charging demand, route choices}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.38
Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER) (Short Paper)

Authors: Alison Heppenstall, J. Gary Polhill, Mike Batty, Matt Hare, Doug Salt, and Richard Milton

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Exascale computing can potentially revolutionise the way in which we design and build agent-based models (ABM) through, for example, enabling scaling up, as well as robust calibration and validation. At present, there is no exascale computing operating with ABM (that we are aware of), but pockets of work using High Performance Computing (HPC). While exascale computing is expected to become more widely available towards the latter half of this decade, the ABM community is largely unaware of the requirements for exascale computing for agent-based modelling to support policy evaluation. This project will engage with the ABM community to understand what computing resources are currently used, what we need (both in terms of hardware and software) and to set out a roadmap by which to make it happen.
Cite as

Alison Heppenstall, J. Gary Polhill, Mike Batty, Matt Hare, Doug Salt, and Richard Milton. Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 38:1-38:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{heppenstall_et_al:LIPIcs.GIScience.2023.38,
  author =	{Heppenstall, Alison and Polhill, J. Gary and Batty, Mike and Hare, Matt and Salt, Doug and Milton, Richard},
  title =	{{Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER)}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{38:1--38:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.38},
  URN =		{urn:nbn:de:0030-drops-189334},
  doi =		{10.4230/LIPIcs.GIScience.2023.38},
  annote =	{Keywords: Exascale computing, Agent-Based Modelling, Policy evaluation}
}
Document
DOI: 10.4230/DagRep.12.9.150
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)

Authors: Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery

Published in: Dagstuhl Reports, Volume 12, Issue 9 (2023)

Abstract
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today’s scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time- and spatial-scales, resulting in complex interactions and emergent behaviours. Understanding these phenomena - and leveraging scientific advances to deliver innovative solutions to improve society’s health, wealth, and well-being - requires new ways of analysing complex systems. The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. AI for science is a rendezvous point. It brings together expertise from AI and application domains; combines modelling knowledge with engineering know-how; and relies on collaboration across disciplines and between humans and machines. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers working together to design and deploy effective AI tools. This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.
Cite as

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, and Jessica Montgomery. Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). In Dagstuhl Reports, Volume 12, Issue 9, pp. 150-199, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{berens_et_al:DagRep.12.9.150,
  author =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  title =	{{Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382)}},
  pages =	{150--199},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{9},
  editor =	{Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.150},
  URN =		{urn:nbn:de:0030-drops-178125},
  doi =		{10.4230/DagRep.12.9.150},
  annote =	{Keywords: machine learning, artificial intelligence, life sciences, physical sciences, environmental sciences, simulation, causality, modelling}
}
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