Statistical calibration of agent-based models of transport from aggregate data
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
This research focuses on statistical calibration of agent-based models (ABMs) of transport based on aggregate data. ABMs aim to understand the effect of interacting individual choices (e.g. route travel choices) on emergent behaviour (e.g. network traffic patterns) via a set of probabilistic rules. ABMs' recent rise in popularity in epidemic modelling raised important questions about their identifiability and predictive power especially in policy scenario testing (e.g. introduction of pedestrianised streets). The research aims develop computational statistics and machine learning techniques to address those questions by understanding discrete individual agent choices from coarsely observed data. This poses great computational challenges as it necessitates the exploration of a combinatorial space of agent decisions. The problem is tackled in two contexts: population synthesis and ABM simulation. The former is a pre-processing step that creates artificial agents whose socio-economic attributes have the same statistical properties as those described by population averages while the latter uses coarse data to reconstruct agent trajectories in space-time generated by discrete agent choices and/or continuous decision parameters.
Planned Impact
The primary impact of the FIBE2 CDT will be the benefit to society that will accrue from the transformative effect that FIBE2 graduates will have upon current and future infrastructure. The current FIBE CDT has already demonstrated significant impact and FIBE2 will extend this substantially and with particular focus on infrastructure resilience. There will be further impacts across academic research, postgraduate teaching, industry-academia partnering and wider society. Our CDT students are excellent ambassadors and their skills and career trajectories are inspirational. Their outputs so far include >40 journal and conference papers, contributions to a CIRIA report, a book chapter and >15 prizes (e.g. Cambridge Carbon Challenge, EPSRC Doctoral Prizes, best presentation awards). Our students' outreach activities have had far reaching impacts including: Science Festival activities and engineering workshops for school girls. Our innovative CDT training approaches have shifted the culture and priorities in academia and industry towards co-creation for innovation. Our FIBE CDT features in the EPSRC document 'Building Skills for a Prosperous Nation'. Our attention to E&D has resulted in 50% female students with the inspirational ethos attracting students from wide ranging educational backgrounds.
FIBE2 CDT will build on this momentum and expand the scope and reach of our impact. We will capitalise on our major research and training initiatives and strategic collaborations within academia, industry and government to train future infrastructure leaders to address UK and global challenges and this will have direct and significant technical, economic and social impacts for UK infrastructure, its associated stakeholders and civil society at large.
As well as the creation of cohorts of highly skilled research cohorts with cross-disciplinary technical skills, further specific impacts include:
-a transformational cross-disciplinary graduate training and research approach in infrastructure with depth and breadth.
-new forms of Industry-University partnerships. Co-creation with industry of our training and research initiatives has already led to new forms of partnerships such as the I+ scheme, and FIBE2 will further extend this with the 'employer model' variant and others.
-skilled research-minded challenge-focused graduates for UK employers who will derive significant benefit from employing them as catalysts for enterprise, knowledge exchange and innovation, and thus to business growth opportunities.
-enhanced global competitiveness for industrial partners. With our extensive network of 27 industry partners from across all infrastructure sectors who will actively shape the centre with us, we will deliver significant impact and will embrace the cross-disciplinary research emergeing from the CDT to gain competitive advantage.
-support for policy makers at the highest levels of national and local government. The research outcomes and graduates will contribute to an evidence-based foundation for improved decision-making for the efficient management, maintenance and design of infrastructure.
-world-class research outcomes that address national needs, via the direct engagement of our key industrial partners. Other academic institutions will benefit from working with the Centre to collectively advance knowledge.
-wider professional engagement via the creation of powerful informal professional networks between researchers, practitioners, CDT alumni and CDT students, working nationally and internationally, including some hosted by FIBE2 CDT industry partners.
-future generations of infrastructure professional inspired by the FIBE2 CDT's outreach activities whereby pupils, teachers and parents gain insight into the importance of infrastructure engineering.
-the generation of public awareness of the importance of a resilient infrastructure to address inevitable and often unexpected challenges.
FIBE2 CDT will build on this momentum and expand the scope and reach of our impact. We will capitalise on our major research and training initiatives and strategic collaborations within academia, industry and government to train future infrastructure leaders to address UK and global challenges and this will have direct and significant technical, economic and social impacts for UK infrastructure, its associated stakeholders and civil society at large.
As well as the creation of cohorts of highly skilled research cohorts with cross-disciplinary technical skills, further specific impacts include:
-a transformational cross-disciplinary graduate training and research approach in infrastructure with depth and breadth.
-new forms of Industry-University partnerships. Co-creation with industry of our training and research initiatives has already led to new forms of partnerships such as the I+ scheme, and FIBE2 will further extend this with the 'employer model' variant and others.
-skilled research-minded challenge-focused graduates for UK employers who will derive significant benefit from employing them as catalysts for enterprise, knowledge exchange and innovation, and thus to business growth opportunities.
-enhanced global competitiveness for industrial partners. With our extensive network of 27 industry partners from across all infrastructure sectors who will actively shape the centre with us, we will deliver significant impact and will embrace the cross-disciplinary research emergeing from the CDT to gain competitive advantage.
-support for policy makers at the highest levels of national and local government. The research outcomes and graduates will contribute to an evidence-based foundation for improved decision-making for the efficient management, maintenance and design of infrastructure.
-world-class research outcomes that address national needs, via the direct engagement of our key industrial partners. Other academic institutions will benefit from working with the Centre to collectively advance knowledge.
-wider professional engagement via the creation of powerful informal professional networks between researchers, practitioners, CDT alumni and CDT students, working nationally and internationally, including some hosted by FIBE2 CDT industry partners.
-future generations of infrastructure professional inspired by the FIBE2 CDT's outreach activities whereby pupils, teachers and parents gain insight into the importance of infrastructure engineering.
-the generation of public awareness of the importance of a resilient infrastructure to address inevitable and often unexpected challenges.
People |
ORCID iD |
| Ioannis Zachos (Student) |
Publications
Zachos I
(2024)
Table inference for combinatorial origin-destination choices in agent-based population synthesis
in Stat
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S02302X/1 | 30/09/2019 | 30/03/2028 | |||
| 2277256 | Studentship | EP/S02302X/1 | 30/09/2019 | 08/05/2024 | Ioannis Zachos |
| Title | Video Explainer: Generating Origin-Destination Matrices in Neural Spatial Interaction Models |
| Description | NeurIPS 2024 Presentation: "Generating Origin-Destination Matrices in Neural Spatial Interaction Models" ? Discover how we can efficiently generate origin-destination matrices used in for agent-based simulations. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2024 |
| Impact | - |
| URL | https://www.youtube.com/watch?v=Rv9sNkG5N-0 |
| Description | Recent research has led to significant advancements in understanding and modeling how individuals move between different locations, which is crucial for fields like transportation planning, economics, and public health. Traditionally, to study these movements, experts have used Origin-Destination Matrices (ODMs). These matrices are like detailed tables that record the number of trips or interactions from one place (the origin) to another (the destination). However, creating accurate ODMs has been challenging. Existing methods often rely on continuous approximations and then convert these into discrete counts, a process that can introduce errors and overlook important details. To address these challenges, researchers have developed a new, efficient framework that directly works with the discrete nature of these movements. This approach uses advanced neural network techniques to learn and predict the intensity of trips between locations. By doing so, it provides a more accurate and computationally efficient way to generate ODMs. In practical tests, such as modeling movements in cities like Cambridge in the UK and Washington, D.C., this method has outperformed previous techniques, offering better accuracy at a fraction of the computational cost. In summary, this research has produced a powerful tool for accurately modeling how people move between places. This tool is not only more precise but also more efficient, making it valuable for applications in transportation, economic planning, and understanding the spread of diseases. |
| Exploitation Route | The outcomes of this research can be taken forward and applied in several practical ways across different fields: 1. Urban Planning & Transportation - City planners and transport authorities can use the improved Origin-Destination Matrix (ODM) modeling techniques to optimize public transport routes, reduce congestion, and improve infrastructure planning. 2. Public Health & Epidemiology - Health researchers can apply these methods to track movement patterns and predict how infectious diseases spread, leading to better containment strategies and resource allocation. 3. Retail & Business Analytics - Businesses can use this model to understand customer movement patterns, optimize store locations, and improve supply chain logistics. 4. Disaster Response & Emergency Planning - Governments and relief organizations can use these insights to predict evacuation patterns and deploy resources more efficiently in crisis situations. 5. Academic Research & AI Development - Other researchers in machine learning, artificial intelligence, and computational modeling can build on this approach to refine movement predictions further and apply them to new datasets. 6. Policy & Economic Development - Policymakers can use the model to assess the impact of new policies (e.g., congestion charges, housing developments) on commuting and economic activities. Overall, this research provides a more accurate and efficient tool for understanding movement patterns, which can have far-reaching benefits for society, businesses, and scientific research. |
| Sectors | Environment Government Democracy and Justice Transport |
| Title | GeNSIT: Generating Origin-Destination Matrices in Neural Spatial Interaction Models |
| Description | This repository introduces a computational framework named GeNSIT see for exploring the constrained discrete origin-destination matrices of agent trip location choices using closed-form or Gibbs Markov Basis sampling. The underlying continuous choice probability or intensity function (unnormalised probability function) is modelled by total and singly constrained spatial interaction models (SIMs) or gravity models embedded in the well-known Harris Wilson stochastic differential equations (SDEs). We employ Neural Networks to calibrate the SIM parameters. We include Markov Chain Monte Carlo (MCMC) schemes leveraged to learn the SIM parameters in previous works. |
| Type Of Technology | Webtool/Application |
| Year Produced | 2024 |
| Open Source License? | Yes |
| Impact | - |
| URL | https://github.com/YannisZa/gensit |