Paola Bolchi, Lidia Diappi, Ila Maltese and Ilaria Mariotti on "ASSESSING SUSTAINABLE MOBILITY AT NEIGHBOURHOOD LEVEL"

1. ASSESSING SUSTAINABLE MOBILITY AT
NEIGHBOURHOOD LEVEL
Cluster Analysis and Self Organising Maps (SOM) neural network
Paola Bolchi
Lidia Diappi
Ila Maltese
Ilaria Mariotti
DiAP, Politecnico di Milano
INPUT 2012
University of Cagliari
Cagliari, 10 - 12 / 05 / 2012

2. STRUCTURE
• Aim of the work
• Literature review on SM and its evaluation
• Data and methodology
• Descriptive statistics, Cluster Analysis and SOM Neural
Network
• Results
• Conclusion and discussion
• Further research questions

3. AIM OF THE WORK
Investigate the SM strategies at
neighbourhood level in 37
European sustainable
neighbourhoods.
Differences and commonalities
among the different
neighbourhoods, will be stressed
throughout CA and SOM Neural
Network.

4. 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
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)

5. Sustainable mobility: literature
1992 - 1993 1993 - 2000 2000-2005 2005-2010
Impacts on Environment Society - Economy - Urban
quality of life equity environment,
society and
economy
Disciplines Transport + sociology + psycology, + planning,
economics, anthropology, urban studies,
transport political ICT
geography, science
environmental
engeneering
Methods Environmental + scenario + case studies, + multi-
impact building and interviews, dimensional
assessment, analysis qualitative (and multi-
regression modelling, scale)
analysis, institutional framework ,
quantitative analysis benchmarking
modelling
Level Macro Macro Micro/macro Micro
Question on What is it? When is it Why is it How to
sustainable sustainable? difficult to achieve it at
mobility achieve it? the urban and
How is it suburban Source
possible to scale? Holden (2007)
achieve it?

6. SM evaluation
In the literature it is possibile to find many SM indicators
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.

7. Methodology
• 1° step – identification of SM strategies and
choice of related indicators in order to create a
database;
• 2° step – elaborating database indicators
throughout Cluster Analysis and SOM Neural
Network

8. Methodology (1st step)
Nijkamp’s Hexagon (1993) Holden’s Model (2007)
ECOWARE HARDWARE
CIVICWARE FINWARE
SOFTWARE
ORGWARE

9. The Nijkamp model
Tangible
Intangible

10. SM variables – neighbourhood scale
ECOW ARE Energy Energy saving for mobility
Transport strategies for reducing car use
Effectiveness and integration of Public Transport system
SM degree
Transport Bicycle and pedestrian paths
HARDW ARE Efficiency of private transport system
Parking planning
Alternative fuelled vehicles
Built environement Mixed use of land
Land-use Density
Financ ing, inc entives, subsidies Funds for reducing car use
FINW ARE
Ec onomic vitality New jobs in the mobility sector
Involvement in policies and programs for SM
Loc al Governanc e Accessibility to information and inclusion in decision making processes
ORGW ARE
about SM
Partnership Public-private partnership for SM
Educ ation and sensitizing Campaigns of communication and information about SM
SOFTW ARE Training and know ledge New sensitizing jobs (even volunteers)
Innovation Innovative approach to project and technology use for SM
CIV ICW ARE Partic ipation Voluntary community involvement in SM (forum, …) Sources:
Journals, books,
magazines, Websites

11. Direct and Indirect indicators
Direct SM indicators Indirect SM indicators
Transport strategies for reducing car use:
car sharing; Funds for reducing car use
car pooling; New jobs in the mobility sector
Involvement in policies and programs for sustainability
collective taxi;
Accessibility to information and inclusion in
park & ride; decisional making processes
bike sharing... Public-private partnership
Effectiveness and integration of public transport Communication and information, assistance to users
system
Bicycle and pedestrian paths New sensitizing jobs
Private transport efficiency: Innovative approach to project and technology use
traffic calming measures Community involvement
car free; ... 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

12. Context variables
Context variables
Neighbourhood population
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

13. Country City
Data Austria
Germany
Bad Ischl, Linz, Wien
Freiburg, Munich, Hannover, Hamburg,
Tubingen, Stuttgard
37 sustainable
Neighbourhoods in 28 Cities in
9 European Countries Spain Zaragoza
Finland Helsinki
• BP for sustainability
Italy Torino, Roma, Modena, Reggio Emilia,
• >500 inhab., >0.010 kmq Bologna, Brescia, Mantova, Bolzano,
• Resid. <90% tot area Siena, Pesaro
The Amsterdam, Rotterdam
Netherlands
Norway Oslo
Sweden Malmo, Stockholm
United London, Perth
Kingdom

14. 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 Share of residential surface over totalsurface %
area
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. Transport strategies for reducing car use 1 to 3
Reduct.
Lpt Effectiveness and integration of public transport system 1 to 3
Bicycle paths Bicycle and pedestrian paths 1 to 3
Efficient Private transport efficiency 1 to 3
Planning
Parking Parking planning 1 to 3
Planning
Alternative Alternative fuelled vehicles 1 to 3
fuelled vehicles
Average value of the SM indicators, excluding access to information, 1 to 3
SM average sensitivity and community involvement
Access to Accessibility to information and inclusion in decision making processes 1 to 3
information
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 1 to 3
involvement (average)
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
Source
Sources: Urban Audit
Journals, books, Eurostat
magazines, Websites

15. Methodology (2nd step)
• CLUSTER ANALYSIS – based on linear models,
WELL COMPARED TO
• SELF ORGANISING MAPS (SOM) neural network –
adaptive non-linear method

17. CA results: neighbourhoods.
5a) Neighbourhood
SM Sens North Green
Cluster Area Pop. Density Resid. Mix average Access. Inv Europe area
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

18. 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
4 8760 7413100 846.24 50600
555.57 751447.8 1899.75 38124.89
Media

19. CA results SM
Neigbourhood – SM indicators
5c)
Direct SM indicators Indirect SM indicators
Transp. Lpt Bicycle Efficient Parking Alternative Energy Access to Sensitivity Community
Reduct. paths Planning Planning fuelled saving information involvment
Cluster vehicles
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

20. CA results
CL SM ACCESS Neigh. MIX density GREEN CITY GDP Eu POS
SIZE SIZE
1 + ++ -- -- -- -- -- -- South
2 ++ + ++ + + ++ + -- Central-
North
3 - ++ ++ -- + -- ++ ++ Both
4 ++ -- -- ++ ++ -- ++ ++ North

21. CA results
Cluster Description Neighbourhoods
Cluster 1 Medium values for SM; high access to Amschl, Bo01, Borgo delle c orti,
informa tion and inclusion in decisional making Casanova, Cognento, Ecocity Bad
processes, sensitizing to sustainable mobility and ischl, Ecocity Umbertide, Ecocity
community involvement. Small neighbourhoods, Tubingen, Fairfield, Giuncoli,
low dense. Residential area prevails and ver y Lunetta, Malizia, Parco Ottavi,
little different functions. Small green area. Small Rieselfeld, S.Francesco Biopep, San
cities and low dense; low GDP, compared to the Rocco, San Pietro, Solar city,
average, mainly located in the South of Europe. Vauban, Villa Fastiggi, Violino
Cluster 2 Good values for SM , medium values for Burgholzhof, Gwl, Hammarby,
accessibility to information, sensitizing to Kronsberg, Nieuw Terbregge,
sustainable mobility and community Pilastredet, Valdespartera, Vikki,
involvement. Large neighbourhoods with Villaggio Olimpico
medium density values; large green area and
number of func tions on average. Medium -small
cities, with lower GDP, mainly loc ated in the
centre-north of Europe.
Cluster 3 Medium-low SM values (the lowest). High value Falkenried-Terra ssen, Hafencity,
for accessibility to information but low va lue for Lunghezzina, Nordmanngasse,
sensitising and community involvment. La rge Parco Plinio, Riem
neighbourhoods with medium density.
Residential area prevails, and little number of
functions. Sma ll surface of green areas. Cities
medium-large with high density and high GDP,
located both in the centre-north and in the south.
Cluster 4 Good values for SM, low values for accessibility Gmv
and sensitizing and community involvem ent.
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.

22. Neural Network

23. 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 b

24. c11 c12 c13
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
0.0 0.0
c21 c22 c23
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
0.0 0.0
c31 c32 c33
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
0.0 0.0

26. CL2
SOM31
SOM 31
++
MIX
++
SM
++
ACC
++
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
N.size
supQkmq
popQ
NEWDensQ
VerdePerc
--
Resid
mixNEW
C.size
en_sav
tranredcar
tpl
bicyc
c31
efficptran
--
parkp
GDP
alterfv
access
sens
involv
kmq city
pop_city
Pos
Dcity
North
GDP_1998
NorthEU

27. CL1
SOM23
SOM 23
--
MIX
+
SM
++
ACC
--
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
N.size
supQkmq
popQ
NEWDensQ
VerdePerc
Resid
--
mixNEW
en_sav
C.size
tranredcar
tpl
bicyc
c23
efficptran
parkp
--
alterfv
GDP
access
sens
involv
kmq city
pop_city
Dcity
Pos
GDP_1998
South
NorthEU

28. 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;
• 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

29. 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
• 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

30. Suggestions are welcome!
Paola Bolchi
Lidia Diappi
Ila Maltese
Ilaria Mariotti
DiAP, Politecnico di Milano
ila.maltese@polimi.it

31. 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 B iopep Nonatola - MO 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
Nordmannga sse Wien AU
Valdespartera Zaragoza ES