Shaping the Sydney of tomorrow

Shaping the Sydney of tomorrow

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TransMob: A micro-simulation model for integrated transport and urban planning.

SMART Infrastructure Facility and Transport for NSW have collaborated on developing an interactive, visually intuitive and highly flexible simulation platform to support transport and urban planning in Sydney. In the resulting agent-based model, TransMob, simulation agents represent individuals and households living in an urban area.

The heterogeneity of this synthetic population is represented in terms of demographic characteristics, environmental perceptions (for example, traffic congestion, number of available facilities of various types available per person, availability and affordability of housing stocks) and decision-making behaviours.

Inherently, the simulated population will evolve over time facilitating the interactions between dynamics of residential relocation of households, transportation behaviours, and population growth. The ‘agents’ age, start work, marry, have children, etc., and their changing needs can be identified.

Thanks to this feature, the model can be used for exploring long-term (for example 20 year time horizon) consequences of various transport and land use planning scenarios. Therefore, it can assist in informing key decisions about investment and policy.

The simulation workflow includes an agent-based model (RePAST) and a micro-simulation traffic model (TRANSIMS). In order to view the outputs, a YellowFin based platform is included for the visual interface.

The project was funded by Transport for NSW for three years.

Publications

Item
Huynh, N., Barthelemy, J. & Perez, P. A heuristic combinatorial optimization approach to synthesizing a population for agent based modelling purposes. Journal of Artificial Societies and Social Simulation 2016, 19(4), In press.
Huynh, N., Perez, P., Berryman, M. & Barthelemy, J. Simulating transport and land use interdependencies for strategic urban planning – An agent based modelling approach. Systems 2015, 3(4), 177-210.
Shukla, N., Ma, J., Wickramasuriya, R., Huynh, N. & Perez, P. Modelling mode choice of individual in linked trips with artificial neural networks and fuzzy representation. In Artificial Neural Network Modelling; Shanmuganathan, S. & Samarasinghe, S., Eds.; Springer International Publishing: Switzerland, 2016; pp 405-422.
Huynh, N., Cao, V. Lam., Wickramasuriya, R., Berryman, M., Perez, P. & Barthelemy, J. An agent based model for the simulation of road traffic and transport demand in a Sydney metropolitan area. In ATT 2014: 8th International Workshop on Agents in Traffic and Transportation; 2014; pp 1-7.
Huynh, N. N., Shukla, N., Munoz Aneiros, A., Cao, V. & Perez, P. A semi-deterministic approach for modelling of urban travel demand. In International Symposium for Next Generation Infrastructure (ISNGI 2013); Perez, P. & Campbell, A. Eds.; University of Wollongong: Australia, 2014; pp 191-199.
Shukla, N., Ma, J., Wickramasuriya, R. & Huynh, N. Data-driven modeling and analysis of household travel mode choice. In 20th International Congress on Modelling and Simulation; The Modelling and Simulation Society of Australia and New Zealand Inc: Australia, 2013; pp 92-98.
Huynh, N., Namazi-Rad, M., Perez, P., Berryman, M. J., Chen, Q. & Barthelemy, J. Generating a synthetic population in support of agent-based modeling of transportation in Sydney. In 20th International Congress on Modelling and Simulation; The Modelling and Simulation Society of Australia and New Zealand Inc: Australia, 2013; pp 1357-1363.

Team

Peter Campbell Headshot

Professor Peter Campbell

Email: peter_campbell@uow.edu.au

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Jun Ma Headshot

Dr Jun Ma

Email: jun_ma@uow.edu.au
Telephone: +61 2 4239 2344

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Pascal Perez Headshot

Senior Professor Pascal Perez

Email: pascal_perez@uow.edu.au
Telephone: +61 2 4252 8238

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Jack Yang Headshot

Dr Jie (Jack) Yang

Email: jie_yang@uow.edu.au
Telephone: +61 2 4239 2344

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Rohan Wickramasurya Headshot

Dr Rohan Wickramasuriya

Email: rohan_wickramasuriya_denagamage@uow.edu.au
Telephone: +61 2 4239 2535

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Partners

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Last reviewed: 16 November, 2018