The impact of network density, travel and location patterns on regional road network vulnerability Erik Jenelius Lars-Göran Mattsson Div. of Transport.

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Presentation 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 Analysis Royal 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 links Large 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 cells We 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?