Post on 01-Nov-2014
description
by F. Maltinti, D. Melis, F. Annunziata
Seventh International Conference on Informatics and Urban and Regional
Planning, University of Cagliari 10-12 May 2012
Introduction
Aim
Describing methodology
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
Case study
Conclusion and Further studies.
Transport infrastructures guarantee accessibility
Importance of accessibility during emergency
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
situations
Knowledge of road network vulnerability is
fundamental to manage risks
Studying a new methodology for assessing road
network vulnerability to:
Identify a vulnerability index correlated to
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
Identify a vulnerability index correlated to
design and geometric characteristics of roads
Identify critical links of a road network to define
improvements priorities
Guarantee accessibility during emergency
situations
Territorial analysis Road network analysis
Identification of weak
links
Link interruption
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
Calculation of vulnerability
index
Calculation of shortest paths
matrix
Maps of vulnerability and priority
vulnerktot=vulnerk
glob-norm+vulnerkO/Dresc-norm+vulnerk
shorth-path-norm
The proposed Total vulnerability index is expressed by:
where:
1) vulnerkglob-norm=f[ΣiΣj (tk
ij-t0
ij)]is the normalized indicator for vulnerability assessment
connected to the whole network
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
2) vulnerkO/Dresc-norm=f[ΣiΣj (tk
ij-t0
ij)]
3) vulnerkshorth-path-norm=f[ΣiΣj (nk
ij)]
connected to the whole network
is the normalized indicator for the vulnerability
assessment connected to the rescue operation
is the normalized indicator for the vulnerability
assessment connected to the number of shortest paths
involved in a link closure
[i, j =urban nodes, k=weak links]
[i, =relief operation centres, j= urban nodes destination
of relief]
nkij=
1 if ∆tkij>0
0 if ∆tkij<0
The index to assess municipality exposure is:
exposurei=f[(ΣkΣj(tkij–t0
ij)]
exposureweii=f[Pi/Ptot,ΣkΣj(t
kij–t0
ij)]
To consider population the function can be rewrite as follows:
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
We used:
• ArcView GIS, to record georeference and process data, and finally to realize the
network vulnerability map;
• ArcGis Network Analyst extension to calculate the matrix of the shortest paths,
and also to construct the matrix of road distances.
We have obtained travel time by dividing the length by the travel speed of the road
link and travel speed by the design characteristics of the horizontal alignment of the
road to which the link belongs
Hypotheses (coming from Italian Geometric Design Standards of Roads):
• the vehicle is isolated: its speed is not affected by traffic conditions;
• the road gradient does not influence vehicle speed;
• on tangents the vehicle follows the equations of uniformly accelerated linear
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
• on tangents the vehicle follows the equations of uniformly accelerated linear
motion until it reaches the designed speed limit;
• on curves vehicle speed depends on the radius of the curves and on the road
category.
The first one is not easily verified, but it appears valid for roads located in sparsely
populated areas with low traffic volume.
This road network has been
interrupted because of landslides
and floods on different occasions.
Figure shows the vulnerability map
of the road network.
Central links are the most
vulnerable:
� they belong to different
shortest paths
� they are used for rescue
services for which there are no
alternative routes.
The methodology was
applied to Ogliastra road
network. Ogliastra is a
sparsely populated area
located in Sardinia (Italy).
The situation is
completely different
(see the figure on the
right) using function
These figures show municipality exposure to the event:
The figure on the left shows that more vulnerable municipalities are localized in central-
southern area of the region characterized by low population density.
right) using function
considering local
population: more
vulnerable
municipalities are
localized in north-
eastern and south-
eastern area of the
region because they are
more populated than
central-southern area
exposurei=f[(ΣkΣj(tk
ij–t0ij)] exposurewei
i=f[Pi/Ptot,ΣkΣj(tk
ij–t0ij)]
The proposed methodology:
• expresses vulnerability of a road network considering geometric and design
characteristics of the elements of road.
• attaches more importance to peripheral links which are particularly critical because
they have not alternative paths and their closure can completely isolate an area.
input 2012, F. Maltinti "Gis applications for vulnerability assessment of a road network"
The methodology is specifically effective in areas of low population density and
where traffic flow is poor.
It could represent a new tool for roads administrators to define improvement
priorities and to manage emergency situations.
The study is going on including other road design characteristics (e.g. road gradient),
identifying combinations of link failures and considering different hazards in the
definition of the index.
Seventh International Conference on Informatics and Urban and Regional
Planning, University of Cagliari 10-12 May 2012
F. Maltinti "Gis applications for vulnerability assessment of a road network"