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Publication Detail
Integrated spatio-temporal analysis of road network performance
  • Publication Type:
    Conference presentation
  • Publication Sub Type:
  • Authors:
  • Date:
  • Status:
  • Name of Conference:
    Annual Meeting of the Association of American Geographers
  • Conference place:
    Seattle, USA
  • Conference start date:
  • Conference finish date:
  • Keywords:
    temporal, network complexity, space-time analysis, traffic
Recent traffic surveys and analysis of road network performance in London indicate a decline in traffic flows and, perversely, a decline in speeds and increase in congestion. Analysis of road network performance is intricate. This is because the road network is essentially an open system with many factors impacting upon it, and because travellers can adapt their behaviour and impact upon monitored performance outcomes in many different ways. These factors have different patterns of influence in both time and space, and analysis of the distinct cause-effect patterns is complicated by the non-linearity of their effects. Modelling spatial-temporal dependency of the factors is the bottleneck in analysis of the network performance. The challenge is to model dependency in both space and time seamlessly and simultaneously, in order that the accuracy of analysis can be improved. Another challenge is to fully accommodate the topology (links and hierarchies) and geometry (distances and directions) of real road networks in the analysis. These are also fundamental challenges to modelling the complexity of other types of networks. This presentation will report the progress made in an on-going project to tackle these challenges through innovative combination of novel machine learning methods with advanced statistical approaches. Using real-time traffic data for London, STANDARD presents an innovative approach to integrated space-time analysis of vehicular traffic, drawing upon concepts from network complexity and data mining. The modelling procedure integrates spatio-temporal prediction, outlier detection and 3D visualization of traffic data in Central London
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