the impact of network density, travel and location patterns on regional road network vulnerability...
TRANSCRIPT
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?