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The impact of network density, travel and location patterns on
regional road network vulnerability
Erik Jenelius
Lars-Göran Mattsson
Div. of Transport and Location AnalysisRoyal Institute of Technology (KTH)
ERSA 2010 Congress, Jönköping, Sweden
Spatial patterns in accessibility
• Accessibility to activities and locations affects location and generates travel demand
• Desirable to be located close to activities/work force/customers
• Market competition leads to trade-offs between accessibility and housing costs
• Spatial location and travel patterns emerge
The road infrastructure
• A more developed road network gives shorter travel times, greater accessibility
• Largest benefits of new road investments typically in dense areas
• Trade-off between transport efficiency and regional equity/development
Road network vulnerability
• Traditionally one only considers the situation where the road network is fully operational
• We consider the impacts on accessibility of network disruptions (link closures) - vulnerability
• Spatial patterns of vulnerability Where do disruptions have the worst overall impacts? Where are travellers most affected by disruptions?
• The influence of supply-side and demand-side variables: Development of the road network (density) Regional location and travel patterns
Network disruptions
• Some causes are internal to transport system: accidents, technical failures etc.
• Usually affect only a single link
• Other causes are external: floods, landslides, heavy snow etc.
• Often affect multiple links in an extended area
• We consider vulnerability to both kinds
Analysing area-covering disruptions
• The study area (Sweden) is covered by square cell grids
• Each grid cell represents location and extent of area-covering disruption
• All links intersecting the cell are closed, all others unaffected
Disruption impacts
• Basic data: Normal travel demand between zones, road network with link travel times(from Swedish transport modelling system Sampers)
• We consider short closures, ~1 day
• We assume no change in destination or mode choice during closure
• Travellers choose fastest route, may delay trip until after closure
• Accessibility impact evaluated as travel time increase
Study area characteristics
Link and cell importance
• The overall impact of disruption of a link or cell is known as its importance
• Answers: Where do disruptions have the worst overall impacts?
Link and cell importance
Determinants of importance
• Single links: Link flow and availability of alternative routes - local redundancy
• Cells: Small cells: similar to single linksLarge cells: travel demand within, into, out of and through cell - population concentration
Regional user exposure
• The average impact per traveller starting in region of certain disruption scenario is known as its user exposure
• Answers: Where are travellers most affected by disruptions?
• Worst-case user exposure: Worst possible impact of link or cell disruption
• Expected user exposure: Mean impact across disruptions of all links or all considered cellsWe assume link closure probability prop. to link length, cell closure probability equal for all cells
Worst-case exposure
Determinants of worst-case exposure
• Single links: Worst-case exposure high if large share of regional trips use link with particularly poor (possibly no) alternatives
• Cells: Worst-case exposure depends on concentration of population to one central settlement
• Quite different spatial patterns
Expected exposure
Determinants of expected exposure
• Single links: Expected exposure high if regional trips are long (likely affected) and network density is low (poor alternatives) - determined with regression analysis
• Cells: Determinants are complex, but similar to for single links
• Spatial patterns different from worst-case exposure
Conclusions
• Changes in accessibility due to short network disruptions show different spatial patterns than baseline accessibility (travel time)
• Spatial patterns can be explained by factors related to network development (density/redundancy), travel patterns (flow, travel times) and location patterns (concentration)
• Interesting empirical question: Are vulnerability issues endogenized in housing prices? Does relation with travel and location patterns run in both directions?