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?