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Eric J Miller: Abstract and background reading


Even before the COVID-19 pandemic struck, we were facing a “new urban world” of disruption driven by new mobility technologies, social justice demands, climate change, and continuing urban growth, particularly in the global south. Urban modelling needs to play a role, both in informing short-run pandemic control policies and guiding longer-term evolution into a better “new normal”. 

This paper summarizes current research adapting an ABM model of activity/travel to:

Modelling COVID-19 spread and activity/travel impacts.

Modelling mobility service provision.

Transferring ABM models to Latin American.

Common themes across these case studies are the adaptability of ABMs to address complex urban spatial-temporal socio-economic processes, and the lessons being learned supporting the longer-term improvement of ABMs for urban policy analysis.



ICT/platform-based technology is redefining both private and public mobility. Growing demands for greater social, political and economic equity, as typified by movements such as “Occupy Wall Street (‘we are the 99%’)”, “Black Lives Matter”, “Yellow Vests” and the 2019-20 Chilean protests, are challenging not just political and economic structures, but urban and transportation system design fundamentals as well. Climate change impacts are accelerating, and still generally not being addressed with sufficient urgency. And, while not a new phenomenon, global urbanization continues apace, continuously adding stress on urban systems of all kinds, which these systems are often ill-prepared to handle, especially in the global south.

Over-laying, and currently dominating, these many major concerns, the pandemic is not only an unprecedented public health crisis, but it represents a “direct attack” on the whole raison d’etre of cities, which is to bring people together to interact. How to manage social distancing while at the same time enabling the essential economic and social – and, indeed, human – needs to interact is the urban planning and design problem at the moment, not just to control the pandemic but, literally, to ensure the survival of our cities – and, hence, as “containers of civilization” (Mumford, 1961), our economies and societies.

Urban modelling, of land use, activity/travel and urban economies, needs to play a fundamental role, both in terms of informing short-run policies as we navigate the pandemic and of guiding our evolution into a “new normal” that is more equitable, sustainable and resilient.

“First-generation” agent-based microsimulation (ABM) models are reasonably well established as policy-sensitive, behaviourally-sound approaches to modelling urban processes from activity and travel to housing and labour markets (Miller, 2018a,b). But, many known weaknesses/limitations also exist in even state-of-the-art models that require improving if they are to address adequately the many disruptions we have been, and will continue, experiencing (Miller, 2020).

This talk briefly summarizes several current research efforts underway at the University of Toronto, as well as with colleagues at the University of New South Wales and the Universidad de Concepción, investigating the adaptation of an operational, large-scale ABM model of daily activity and travel behaviour (TASHA/GTAModel: Miller and Roorda, 2003; Vaughan and Miller, 2015).



Allendes, V., J.A. Carrasco and E.J. Miller (in preparation) “Spatial and temporal transferability of microsimulation activity-based models: An application of TASHA in Chile”, draft manuscript.

Calderón, F. and E.J. Miller (2020a) “A Literature Review of Mobility Services: Definitions, Modelling State-of-the-Art, and Key Considerations for a Conceptual Modelling Framework”, Transport Reviews, 40:3, 312-332. DOI: 10.1080/01441647.2019.1704916. 

Calderón, F. and E.J. Miller (2020b) “Modelling Within-Day Ridehailing Service Provision with Limited Data”, forthcoming, Transportmetrica B. 

Calderón, F. and E.J. Miller (2020c) “A Conceptual Framework for Modelling the Supply Side of Mobility Services within Large-Scale Agent-Based Travel Demand Models”, submitted to Transportation Letters.

Mumford, L. (1961) The City in History, New York: Harcourt Brace Jovanovich. 

Miller, E.J. (2020) “Travel Demand Models, The Next Generation: Boldly Going Where No-One Has Gone Before”, Chapter 2 in Mapping the Travel Behavior Genome, The Role of Disruptive Technologies, Automation and Experimentation, K.G. Goulias and A.W. Davis (Eds.), Elsevier, 29-46. 

Miller, E.J. (2018a) “Agent-Based Activity/Travel Microsimulation: What’s Next?”, in Briassouli, et al. (eds), Spatial Analysis: Tools and Land Use, Transport and Environmental Applications, Springer, 119-150. 

Miller, E.J. (2018b) “The Case for Microsimulation Frameworks for Integrated Urban Models”, Journal of Transport and Land Use, 11:1, 1025-1037. 

Miller, E.J., F.F. Calderón, J. Vaughan, B. Yusuf and A. Faghih Imani (2017) SATA: Simulador de Actividad de Transporte de Asunción, Development of the SATA Prototype Volume I: Final Report, report to the Latin American Development Bank, Toronto: University of Toronto Transportation Research Institute, April. 

Miller, E.J. and M.J. Roorda (2003) “A Prototype Model of Household Activity/Travel Scheduling”, Transportation Research Record, Journal of the Transportation Research Board, No. 1831, 114-121.

Najmi, A., F. Safarighouzhidi, E.J. Miller, R. MacIntyre and T.H. Rashidi (2020a) “Easing or tightening control strategies: Determination of COVID-19 parameters for an agent-based model”, submitted to PLOS One. 

Vaughan, J. and E.J. Miller (2015) GTAModel V4.0, Design, Background and Calibration as of 10/11/2015, Toronto: Travel Modelling Group, University of Toronto Transportation Research Institute, November.