School of Computing, Dublin City University, Ireland


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Research:    Social Systems // Environmental and Transport // Financial and Socioeconomic Modelling // Assisted Living // Biocomputation // Data Science


Social Systems:

Environmental and Transport

Pedestrian movement studies are focusing on modelling a range of phenomena starting from pedestrian flow to individual behaviours. Key aspects of each model include understanding of activity agenda and pedestrian-environment interaction. Unlike vehicles, pedestrians are more adaptable to fast decision changes, such as route and destination choices. Questions of interest include phenomena such as congestion, following psychology and street-space perception and are important to understanding evacuation and travel dynamics. Of interest in this study is analysing the impact of common urban layouts in context of pedestrian movement decisions, when various behaviours are taken into account.

The agent-based paradigm (ABM) is used as appropriate modelling tool to describe randomness and individuality of agents in typical groups. ABM helps to overcome main limitations of using Geospatial Information Systems, which are static in nature and unable to represent network changes over time, with "agents" being individuals with their own characteristics and attributes. Agents interact with both the environment and one another to represent human decision behaviour that gives rise to the phenomena described above.

Open Street Map (OSM) is an open, crowdsourced, database of geographical information (including streets, buildings, public transport routes etc.). In the current work, OSM is used to source spatial environment data, in particular of complex urban layouts including street size, interconnection degrees and destination venues of interest.

Image: Extracting flow data from pedestrian movement

Radiative Transfer provides a mathematical description of the propagation of radiation through media that absorb, scatter and/or emit photons. The optical domain (ultra-violet to thermal infrared) within the Earth’s atmosphere-surface system is of particular interest for Earth Observation (EO). Optical instruments (satellite, airborne, in-situ) are commonly used to extract environmental information on water quality and land productivity. Radiative transfer modelling thus provides the link between such instruments and the target being observed. The deployment of such systems in non-standard settings, for example for waste water monitoring in treatment plant intakes and in industrial outflows, presents considerable challenges both in instrument system design and operation, and in related radiative transfer modelling.

Chandrasekhar, S., 1960 Radiative Transfer, (New York: Dover Publications Inc).
Thomas G. E. and Stamnes, K., 2002, Radiative Transfer in the Atmosphere and Ocean (Cambridge, Atmospheric and Space Science Series).
Miller, R.E., Del Castillo, C.E., Mckee, B.A. (Eds), 2005, Remote Sensing of Coastal Aquatic Environments (Springer)

Traffic flow and transportation systems are classical examples, of socio-economic systems exhibiting complexity, and attempts to model and analyse these go back a long way. There are many layers to the complexity, not least the unpredictability of human behaviour, the heterogeneity of vehicles, the lack of regularity in road systems and the non-linear group dynamics. The development of the network level traffic approach was based on the two-fluid theory of town traffic, (Herman and Prigogine (1979,), Herman and Ardekani (1984) and subsequently), which related average speed of moving cars to the number in a street. Other work has involved development of fast simulation models for progression of cars along a street through bit-manipulation programmes, e.g. Cremer and Ludwig (1986). Cellular Automaton models have been popular since the early 90's (e.g. Nagel and Schreckenberg (1992) and use rule-sets to control car movement. For this reason they are also known as microscopic models, although aggregate traffic parameters (in the microscopic sense) are also used to describe the traffic behaviour. A typical road transport system includes obstacles, different road geometries and configurations, (i.e. intersections, roundabouts, multiple lanes etc.) as well as control features, like lights and crossings. In any multi-lane situation, the complexity increases as lane-changing rules must also be considered in addition to the usual manoeuvres. Further, neither traffic units, nor drivers are consistent and homogeneous in the real world, so that driver behaviour and traffic mix must also be taken into account and is often crucial in urban situations.

Image: Creating a model of traffic flow

In recent years, models for both freeway and urban/inter-urban flow have been developed, with interest in the group predominantly focused on the latter. Models of gap-acceptance type, Brilon and Wu (1999), Tian et al. (2000) have been used, in conjunction with CA road and ring rules to form hybrid MAP models for heterogeneous and inconsistent driver behaviour at specific urban road features. Work has focused on extending these hybrid models to incorporate vehicle heterogeneity and on looking at agent-based potential for intelligent MAPping, as well as - most recently, 'green city' travel and driver behaviour. This last has seen the development of models, which include non-motorised vehicles, specificity bicycles.

Brilon W.and Wu N. (1999) Transportation Research Part A 33, 275.
Cremer M. and Ludwig J. (1986) Mathematics and Computers in Simulations, 28, 297.
Herman R.and Prigogine I. (1979) Science, 204, 148.
Herman R. and Ardekani S. (1984) Transportation Science, 18, 101.
Nagel K. and Schreckenberg M. (1992) J. Phys. France 2, 2221.
Tian Z. Z., Troutbeck R. , Kyte M., Brilon W., Vandehey M., Kittelson W. , Robinson B. (2000) Transportation Research CircularE-C108 : 4th Intl. Symposium on Highway Capacity, 397.

Selected Group Publications:

  • Deo P. and Ruskin, H.J. (2014), Urban signalised intersections: Impact of vehicle heterogeneity and driver type on cross-traffic manoeuvres, Physica A: Statistical Mechanics and its Applications, vol. 405, 1 July 2014, pp.140 - 150. DOI:

  • Bezbradica, M. and Ruskin, H.J. (2014), Modelling Impact of Morphological Urban Structure and Cognitive Behaviour on Pedestrian Flows, Murgante, B. et al. (eds.). ICCSA 2014. Springer, LNCS 8582. pp. 268-283. DOI: 10.1007/978-3-319-09147-1_20.

  • Vasic, J. and Ruskin, H.J., Interaction of cars and bicycles on a one-way road intersection - a network CA-based model. Kozlov, V.V., Buslaev, A.P., Bugaev, A.S., Yashina, M.V., Schadschneider, A., Schreckenberg, M. (Eds.) Traffic and Granular Flow '11. Springer, 2013.

  • Vasic, J. and Ruskin, H.J., Agent-based space-time discrete simulation of urban traffic including bicycles. ABMTRANS'12, Procedia Computer Science. vol. 10, pp. 860-865, 2012.

  • Wang R. and Ruskin H.J., Special Issue on Modelling Complex Systems, International Journal of Modelling, Identification and Control, Vol 1, No 3, 2008.

    Researchers: Heather J. Ruskin, Liam Tuohey, Marija Bezbradica, Jelena Vasic(Assoc.), Ruili Wang (Assoc.)


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