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Publication Detail
Modelling the impact of unexpected interruptions on road network performance using agent-based simulation
  • Publication Type:
    Conference presentation
  • Publication Sub Type:
    Presentation
  • Authors:
    Manley E, CHENG T
  • Date:
    2011
  • Status:
    Published
  • Name of Conference:
    Annual Meeting of the Association of American Geographers
  • Conference place:
    Seattle, USA
  • Conference start date:
    12/04/2011
  • Conference finish date:
    16/04/2011
  • Keywords:
    non-recurrent congestion, urban transport, spatial cognition, routing huersitics, traffic simulation, agent based simulation, temporal
Abstract
Most current transport simulation models are built upon well-established principles of network equilibrium and user optimisation. However, irregularities in road network provision, caused by the closure of a road or emergency works, for example, can lead to changes in traffic flow which do not exhibit properties of network equilibrium, particularly in the urban realm. In response to this shortcoming in the literature, this paper presents a novel method whereby driver behaviour in response to irregular conditions is modelled within an agent-based environment. The drivers' route-choice behaviour is given the most attention, on the basis that it is the unanticipated redistribution of vehicles in a busy road network that produces dynamic network behaviour. Simulated drivers are given an individual spatial knowledge which governs their behaviour in these unexpected situations. These representations of knowledge follow the broad conclusions of Lynch and many others, which indicate that these so-called cognitive maps are not necessarily Euclidean in space estimation, and heavily influenced by landmarks, well-known and personal. Furthermore, a fine-to-coarse routing heuristic is implemented as an approach to model the use of this knowledge in route replanning, in preference to the ubiquitous yet behaviourally unlikely shortest path method. The combination of these models yields a realistic representation of individual behaviour in response to dynamic road conditions. The interaction of many driver agents, responding individually, provides a basis for understanding the complex dynamicity of macroscopic network characteristics over space and time.
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