ASSESSING SUSTAINABLE MOBILITY AT NEIGHBOURHOOD LEVEL Cluster Analysis and Self Organising Maps (SOM) neural network
Paola BolchiLidia DiappiIla MalteseIlaria Mariotti
DiAP, Politecnico di Milano
INPUT 2012
University of Cagliari
Cagliari, 10 - 12 / 05 / 2012
STRUCTURE
• Aim of the work
• Literature review on SM and its evaluation
• Data and methodology• Data and methodology
• Descriptive statistics, Cluster Analysis and SOM Neural
Network
• Results
• Conclusion and discussion
• Further research questions
AIM OF THE WORK
Investigate the SM strategies atneighbourhood level in 37European sustainableEuropean sustainableneighbourhoods.
Differences and commonalitiesamong the differentneighbourhoods, will be stressed
throughout CA and SOM NeuralNetwork.
Sustainable mobility
•Allows safe basic access and development needs of
individuals, companies and society for equity within and
between successive generations (social aspects).
•Is Affordable, operates fairly and efficiently, offers a •Is Affordable, operates fairly and efficiently, offers a
choice of transport mode, and supports a competitive
economy, as well as balanced regional development
(economic aspects).
•Uses renewable resources and non-renewable
resources in a rational way, while minimizing the impact
on the use of land and the generation of noise
(environmental aspects).
(European Union Council of Ministers of Transport, 2001)
Sustainable mobility: literature
1992 - 1993 1993 - 2000 2000-2005 2005-2010
Impacts on Environment Society -
quality of life
Economy -
equity
Urban
environment,
society and
economy
Disciplines Transport
economics,
transport
+ sociology + psycology,
anthropology,
political
+ planning,
urban studies,
ICT transport
geography,
environmental
engeneering
political
science
ICT
Methods Environmental
impact
assessment,
regression
analysis,
quantitative
modelling
+ scenario
building and
analysis
+ case studies,
interviews,
qualitative
modelling,
institutional
analysis
+ multi-
dimensional
(and multi-
scale)
framework ,
benchmarking
Level Macro Macro Micro/macro Micro
Question on
sustainable
mobility
What is it? When is it
sustainable?
Why is it
difficult to
achieve it?
How is it
possible to
achieve it?
How to
achieve it at
the urban and
suburban
scale?
SourceHolden (2007)
SM evaluation
In the literature it is possibile to find many SM indicators at the urban scale (among the others Gilbert 2002, at the urban scale (among the others Gilbert 2002, Gundmundsson 2003, Litman 2003).
It is also easy to find indicators for the assessment of Sustainability in general, developed by international institutions (OECD, World Bank, EU, ecc...).
Here the focus is on SM indicators at a neighbourhood scale.
Methodology
• 1° step – identification of SM strategies and choice of related indicators in order to create a database;database;
• 2° step – elaborating database indicators throughout Cluster Analysis and SOM Neural Network
Methodology (1st step)
Nijkamp’s Hexagon (1993)
ECOWARE
Holden’s Model (2007)
ECOWARE HARDWARE
ORGWARE
SOFTWARE
CIVICWARE FINWARE
The Nijkamp model
TangibleTangible
Intangible
SM variables – neighbourhood scale
SM degree
ECOWARE Energy Energy saving for mobility
Transport strategies for reducing car use
Effectiveness and integration of Public Transport system
Bicycle and pedestrian paths
Efficiency of private transport system
Parking planning
Alternative fuelled vehicles
HARDWARE
Transport
Alternative fuelled vehicles
Built environement Mixed use of land
Land-use Density
Financ ing, incentives, subsidies Funds for reducing car use
Economic vitality New jobs in the mobility sector
Involvement in policies and programs for SM
Accessibility to information and inclusion in decision making processes
about SM
Partnership Public-private partnership for SM
Education and sensitizing Campaigns of communication and information about SM
Training and knowledge New sensitizing jobs (even volunteers)
Innovation Innovative approach to project and technology use for SM
CIVICWARE Partic ipation Voluntary community involvement in SM (forum, …)
FINWARE
ORGWARELocal Governance
SOFTWARE
Sources: Journals, books, magazines, Websites
Direct SM indicators Indirect SM indicators
Transport strategies for reducing car use:
car sharing;
car pooling;
Funds for reducing car use
New jobs in the mobility sector
Direct and Indirect indicators
car pooling;
collective taxi;
park & ride;
bike sharing...
Involvement in policies and programs for sustainability
Accessibility to information and inclusion in
decisional making processes
Public-private partnership
Effectiveness and integration of public transport
system
Communication and information, assistance to users
Bicycle and pedestrian paths New sensitizing jobs
Private transport efficiency:
traffic calming measures
car free; ...
Innovative approach to project and technology use
Community involvement
Life quality improvement (comfort, security, air
quality, ...)
Parking planning (planning typologies: open air,
underground, ....)
Alternative fuelled vehicles
Energy saving for mobility
-road-light,
- recharging vehicles
Context variables
Neighbourhood population
Neighbourhood area (kmq)
Context variables
Neighbourhood area (kmq)
Neighbourhood density
City population
City area
City density
Mixed use of land: (i) % of residential area over total
area; (ii) number of functions
Green area: % of green area over the total
GDP – NUTS3 province
Country of location
Data
37 sustainableNeighbourhoods in 28 Cities in9 European Countries
• BP for sustainability• >500 inhab., >0.010 kmq
Country City
Austria Bad Ischl, Linz, Wien
Germany Freiburg, Munich, Hannover, Hamburg, Tubingen, Stuttgard
Spain Zaragoza
Finland Helsinki
Italy Torino, Roma, Modena, Reggio Emilia, • >500 inhab., >0.010 kmq• Resid. <90% tot area
Italy Torino, Roma, Modena, Reggio Emilia, Bologna, Brescia, Mantova, Bolzano,
Siena, Pesaro
The Netherlands
Amsterdam, Rotterdam
Norway Oslo
Sweden Malmo, Stockholm
UnitedKingdom
London, Perth
Variables Description Measure
Characteristics of the Neighbourhood
Area Neighbourhood surface Kmq
Population Neighbourhood inhabitants Number of inhabitants
Density Population / surface n./kmq
North Europe If the neigbourhood is located in Northen Europe Dummy variable: 0, 1
Residential
area
Share of residential surface over totalsurface %
Mix Number of functions present in the neighbourhood 1 to 6
Green area Share of green area over the total surface %
SM indicators at neighbourhood level
Energy saving Energy saving for mobility 1 to 3
Transp.
Reduct.
Transport strategies for reducing car use 1 to 3
Lpt Effectiveness and integration of public transport system 1 to 3 Bicycle paths Bicycle and pedestrian paths 1 to 3 Efficient
Planning
Private transport efficiency 1 to 3
Parking Parking planning 1 to 3
SourceUrban AuditEurostat
Parking
Planning
Parking planning 1 to 3
Alternative
fuelled vehicles
Alternative fuelled vehicles 1 to 3
SM average Average value of the SM indicators, excluding access to information,
sensitivity and community involvement
1 to 3
Access to
information
Accessibility to information and inclusion in decision making processes 1 to 3
Sensitizing Communication and information, assistance to users 1 to 3 Involvment Community involvement 1 to 3 Sens_Inv Communication and information, assistance to users and community
involvement (average) 1 to 3
Indicators at urban level
Area City area Kmq
Population City inhabitants Number of inhabitants
Density City Population / area n./kmq
Indicator at NUTS 3 province
GDP 1998 GDP at the year 1998 Euros / Source: Eurostat
Sources: Journals, books, magazines, Websites
Methodology (2nd step)
• CLUSTER ANALYSIS – based on linear models,
WELL COMPARED TOWELL COMPARED TO
• SELF ORGANISING MAPS (SOM) neural network –adaptive non-linear method
CA results: neighbourhoods.
5a) Neighbourhood
Cluster Area Pop. Density Resid. Mix
SM
average Access.
Sens
Inv
North
Europe
Green
area
1 .28 2703.85 14444.01 .73 2.90 2.32 2.61 2.57 .38 .33 1 .28 2703.85 14444.01 .73 2.90 2.32 2.61 2.57 .38 .33
2 1.9 10087.56 20367.39 .63 3.77 2.36 2.44 2.44 .78 .34
3 1.4 5082 20645.56 .77 2.33 2.26 2.66 2.33 .67 .28
4 .24 8850 36875 .5 6 2.42 2 2 1 .24
Media .86 5051.64 17496.74 .70 3.10 2.32 2.56 2.48 .54 .33
CA results cities and NUTS3
5b) City NUTS 3
Cluster Area Pop. Density GDP_1998
1 137.71 152150.7 1294.15 33223.81
2 455.44 643759.3 2870.41 40791.22
3 800.83 1900245 2738.95 49200 3 800.83 1900245 2738.95 49200
4 8760 7413100 846.24 50600
Media
555.57
751447.8
1899.75
38124.89
CA results SM
5c) Neigbourhood – SM indicators
Direct SM indicators
Indirect SM indicators
Cluster
Transp.
Reduct.
Lpt Bicycle
paths
Efficient
Planning
Parking
Planning
Alternative
fuelled
vehicles
Energy
saving
Access to
information
Sensitivity Community
involvment
1 2.47 2.62 3 2.24 2.28 1.86 1.80 2.62 2.38 2.52 1 2.47 2.62 3 2.24 2.28 1.86 1.80 2.62 2.38 2.52
2 2.33 2.55 2.89 2.22 2.22 2.22 2.11 2.44 2.44 2.66
3 2.33 2.83 2.83 2.33 2.33 1.66 1.5 2.66 2.33 1.83
4 3 3 3 2 3 1 2 2 2 3
Media 2.43 2.65 2.94 2.24 2.30 1.90 1.84 2.57 2.38 2.46
CA results
CL SM ACCESS Neigh.
SIZE
MIX density GREEN CITY
SIZE
GDP Eu POS
1 + ++ -- -- -- -- -- -- South
2 ++ + ++ + + ++ + -- Central-
North North
3 - ++ ++ -- + -- ++ ++ Both
4 ++ -- -- ++ ++ -- ++ ++ North
CA results
Cluster Description Neighbourhoods Cluster 1 Medium values for SM; high access to
information and inclusion in decisional making
processes, sensitizing to sustainable mobility and community involvement. Small neighbourhoods, low dense. Residential area prevails and very
little different functions. Small green area. Small cities and low dense; low GDP, compared to the
average, mainly located in the South of Europe.
Amschl, Bo01, Borgo delle corti, Casanova, Cognento, Ecocity Bad
ischl, Ecocity Umbertide, Ecocity Tubingen, Fairfield, Giuncoli, Lunetta, Malizia, Parco Ottavi,
Rieselfeld, S.Francesco Biopep, San Rocco, San Pietro, Solar city,
Vauban, Villa Fastiggi, Violino
Cluster 2 Good values for SM, medium values for accessibility to information, sensitizing to sustainable mobility and community
Burgholzhof, Gwl, Hammarby, Kronsberg, Nieuw Terbregge, Pilastredet, Valdespartera, Vikki, sustainable mobility and community
involvement. Large neighbourhoods with medium density values; large green area and
number of func tions on average. Medium-small cities, with lower GDP, mainly located in the
centre-north of Europe.
Pilastredet, Valdespartera, Vikki,
Villaggio Olimpico
Cluster 3 Medium-low SM values (the lowest). High value for accessibility to information but low va lue for sensitising and community involvment. Large
neighbourhoods with medium density. Residential area prevails, and little number of
functions. Small surface of green areas. Cities medium-large with high density and high GDP, located both in the centre-north and in the south.
Falkenried-Terrassen, Hafencity, Lunghezzina, Nordmanngasse, Parco Plinio, Riem
Cluster 4 Good values for SM, low values for accessibility
and sensitizing and community involvement. Very dense neighbourhoods (small in surface)
and mixed used. Little percentage of green area. Large cities low dense but with the highest GDP on average. All located in the north.
Gmv
Neural NetworkNeural Network
The functioning of the SOM RN: The network is deformed by the learning algorithm to
bring the nodes close to the groups of observations
a ba b
c11
c12
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SOM 31
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supQ
km
q
popQ
NEW
DensQ
Verd
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Resid
mix
NEW
en_sav
tranre
dcar
tpl
bic
yc
effic
ptran
park
p
alte
rfv
access
sens
involv
km
q c
ity
pop_city
Dcity
GD
P_1998
NorthEU
supQ
km
q
NEW
DensQ
Verd
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mix
NEW
en_sav
tranre
dcar
effic
ptran
access
km
q c
ity
pop_city
GD
P_1998
NorthEU
MIX SM ACC N.size C.size GDP Pos
CL2++ ++ ++ ++ -- -- NorthSOM31
SOM 23c23
0.0
0.1
0.2
0.3
0.4
0.5
0.6
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0.8
0.9
1.0
supQ
km
q
popQ
NEW
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bic
yc
effic
ptran
park
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rfv
access
sens
involv
km
q c
ity
pop_city
Dcity
GD
P_1998
NorthEU
supQ
km
q
popQ
NEW
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Resid
mix
NEW
en_sav
tranre
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park
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access
sens
involv
km
q c
ity
pop_city
Dcity
GD
P_1998
NorthEU
MIX SM ACC N.size C.size GDP Pos
CL1-- + ++ -- -- -- SouthSOM23
CONCLUSIONS
• Best north-european performance;
• Best “sustainable mobility” practices are those neighbourhoods which invested a lot in an omogeneous way on all the indicators, both direct and indirect;and indirect;
• Mixitè appears more significant than density and also the presence of green areas.
• In general citizens’ participation is fundamental
• New technologies don’t appear as the most adopted tool for achieving sustainable mobility: land use and green attitudes are preferred;
• Context variables don’t explain so much
• Two methods quite “agree”, despite some differences in selecting elements and grouping them
FURTHER RESEARCH
• Further analysis of FINWARE: income and incentives• GDP: it appears useful to be better analysed• Direct vs indirect also is an interesting topic• Direct vs indirect also is an interesting topic• Quality and type of the variables (discrete and continuous)• Further analysis of other context characteristics: presence of infrastructures• Freight transport could worth be analysed, because it is a key factor for achieving a real and complete sustainable mobility: further analysis on city logistics• Some case study with analysis of citizen’s satisfaction
Suggestions are welcome!
Paola BolchiLidia DiappiIla MalteseIlaria Mariotti
DiAP, Politecnico di Milano
San Pietro Bologna IT
Casanova Bolzano IT
Violino Brescia IT
San Rocco Faenza IT
Giuncoli Firenze IT
Amschl Freiburg DE
Vauban Freiburg DE
Rieselfeld Freiburg DE
Falkenried-Terrassen Hamburg DE
Hafencity Hamburg DE
Kronsberg Hannover DE
Vikki Helsinki FI
Solar c ity Linz AU
Gmv London UK
Bo01 Malmo SW
Lunetta Mantova IT
S.Francesco Biopep Nonatola - MO IT
Cognento Modena IT Cognento Modena IT
Borgo delle corti Modena IT
Riem Monaco DE
Pilestredet Oslo NW
Fairfield Perth UK
Villa Fastiggi Pesaro IT
Parco Ottavi Reggio E. IT
Parco Plinio Roma IT
Lunghezzina Roma IT
Nieuw Terbregge Rotterdam NL
Malizia Siena IT
Hammarby Stocholm SW
Burgholzhof Stuttga rd DE
Villaggio olimpico Torino IT
Ecocity Tubingen Tubingen DE
Ecocity Umbertide Umbertide IT
Nordmanngasse Wien AU
Valdespartera Zaragoza ES
