Indicators of urban form and sustainable urban transport : introducing simulation-based indicators for the LUISA modelling platform.
Date: 2016-02-06
Institutions: Joint Research Centre
Subjects: EU industrial policy, Europe, database, energy policy, geographical information system, land use, research method, research report, simulation, software, sustainable development, transport infrastructure, urban transport

Indicators  of  urban 
sustainable urban transport 

form  and 

simulation-
Introducing 
based  indicators  for  the 
LUISA modelling platform 

Chris  Jacobs-Crisioni,  Mert  Kompil,  Claudia 
Baranzelli, Carlo Lavalle 

2015  

Report EUR 27708 EN 

 
 
 
 
 
 
 
 
Indicators  of  urban 
sustainable urban transport 

form  and 

Introducing 
simulation-
based  indicators  for  the 
LUISA modelling platform 

 
 
 
 
 
This publication is a Technical report by the Joint Research Centre, the European Commission’s in-house science 
service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific 
output expressed does not imply a policy position of the European Commission. Neither the European 
Commission nor any person acting on behalf of the Commission is responsible for the use which might be made 
of this publication. 

JRC Science Hub 
https://ec.europa.eu/jrc 

Contact information 
Carlo Lavalle 
Address: Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy 
E-mail: carlo.lavalle@jrc.ec.europa.eu 
Tel.: +39 0332 78 5231 

JRC99984 

EUR 27708 EN 

ISBN 978-92-79-54593-1 (PDF) 

ISSN 1831-9424 (online) 

doi:10.2788/59611 (online) 

© European Union, 2015 

Reproduction is authorised provided the source is acknowledged. 

All images © European Union 2015, except: front page, `Spsmiler’ 2006. Source: 
[https://commons.wikimedia.org/wiki/File:Bordeaux-aps-overhead-wire-transition.jpg]  

How  to  cite:  Jacobs-Crisioni,  C.,  Kompil,  M.,  Baranzelli,  C.,  Lavalle,  C.;  (2015);  Indicators  of  urban  form  and 
sustainable urban transport; EUR 27708 EN; doi:10.2788/59611 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of contents 

Acknowledgements ................................................................................................ 3 

Abstract ............................................................................................................... 4 

1. Introduction ................................................................................................... 5 

2. A proxy of demand for transport-related energy consumption ................................. 6 

2.1 Indicator definition ........................................................................................ 7 

2.2 Results ........................................................................................................ 8 

2.2.1. Average travel distances: Masovia Region in Poland .................................... 8 

2.2.1 Average travel distances: A Europe-wide grid map .................................... 10 

2.2.2. A comparison of Functional Urban Areas .................................................. 11 

2.3. Analysis of factors causing travel distance disparities ..................................... 13 

2.4. Network implications .................................................................................. 15 

2.4.1. A proxy of transport consumption at the network link level ........................ 15 

2.4.2. Results ................................................................................................ 16 

2.5. Conclusions and further work ...................................................................... 19 

3.  A  measure  of  urban  form  efficiency  with  the  ease  of  access  to  potential  public 
transport services ............................................................................................... 21 

3.1. Definition of the indicator ............................................................................ 21 

3.2. 

Results ............................................................................................... 23 

4. Conclusions .................................................................................................... 26 

References ......................................................................................................... 27 

List of abbreviations and definitions ....................................................................... 30 

List of figures ...................................................................................................... 31 

List of tables ....................................................................................................... 32 

2 

 
 
 
 
 
 
Acknowledgements 

The  indicators  in  this  report  have  been  developed  after  fruitful  discussions  with  Lewis 
Dijkstra  from  the  European  Commission’s  DG  REGIO.  His  many  additions  to  this  work 
are  gratefully  acknowledged.  Early  prototypes  of  transport-related  energy  consumption 
indicator  have  profited  from  the  data  made  available  by  Marcin  Stępniak,  for  which  we 
are  also  thankful.  Finally,  we  would  like  to  thank  Jean-Philippe  Aurambout  for  his 
valuable  contributions  to  the  indicator  on  urban  form  efficiency  measured  by  ease  of 
access to potential public transport services. 

3 

 
 
 
 
 
 
Abstract 

Sustainable  urban  transport  is  a  key  objective  of  European  Commission  policies  and 
often an integral part of debates on  efficient and inefficient urban form. Because of the 
expected  relation  between  sustainable  transport  and  urban  form,  indicators  that 
communicate  potential  impacts  of  sustainable  transport  policies  have  repeatedly  been 
requested  for the LUISA  modelling platform. This report presents three novel indicators 
that  may  shed  some  light  on  potential  transport  sustainability  impacts  of  current  and 
future urbanization patterns. These indicators are: 

1)  A  proxy  of  demand  for  transport-related  energy  consumption,  which  is  a  
measure  that  indicates  for  every  inhabited  1  km  grid  cell in  Europe  the  average 
Euclidean distance travelled for social visits, given a number of assumptions and 
methodological limitations; 

2)  Road-link specific transport consumption, which is a measure that indicates how 
many  vehicle  kilometres  are  travelled  in  any  road-carrying  grid  cell  in  Europe; 
and 

3)  A measure of urban form efficiency by ease of access to potential public transport 
services,  which  is  a  measure  that  indicates  to  what  degree  the  population 
distribution in a city is supportive for an effective public transport system. 

Because  of  methodological  limitations  and  data  availability  the  indicators  are  based  on 
simulation  exercises  that  take  into  account  only  fine  resolution  population  distributions 
and in some cases also transport supply. Thus these indicators can give insight into the 
degree in which management of urban growth may encourage sustainable transport. The 
report  presents  the  indicators  at  hand  and  some  first  results  in  which  the  emphasis  is 
put  on  differences  between  European  cities.  The  report’s  conclusions  reflect  on  the 
usefulness  of  the  indicators  for  policy  evaluations,  and  on  research  that  might  be 
conducted in the future. 

4 

 
 
 
 
 
1. Introduction 

Sustainable  urban  transport,  characterised  by  reducing  energy  consumption,  CO2  and 
noxious  gas  emissions,  is  a  key  objective  of  the  European  Commission  (EC).  Various 
strategies  have  already  been  identified  to  reduce  the  energy  dependence  of 
transportation;  Gilbert  and  Dajani  (1)  broadly  categorized  those  strategies  into:  I) 
inducing  shifts  to  more  efficient  modes,  II)  increasing  the  efficiency  of  current  modes, 
and  III)  decreasing  travel  demand.  Because  of  its  expected  impact  on  travel  demand, 
urban  form  is  often  noted  to  affect  transport  energy  consumption  and  the  potential  for 
sustainable  transport.  Therefore  there  is  a  need  at  the  EC  for  the  indicators  that  show 
the relation between urban form, transport consumption and transport efficiency; for the 
current  status-quo  of  European  cities  as  well  as  for  their  potential  future  forms.  The 
LUISA  (‘Land  Use-based  Integrated  Sustainability  Assessment’)  modelling  platform  is 
specifically  set  up  to  provide  assessments  of  the  current  and  future  state  of  territorial 
sustainability in  an  integral  manner.  For  more  information  on  LUISA  we  refer  to  (2–4). 
Transport  sustainability  and  in  particular  the  relation  between  urban  form  and 
sustainable transport are often demanded as outputs of LUISA.  

This  report  will  introduce  three  novel  indicators  of  the  LUISA  modelling  platform  that 
deal  with  sustainable  transport,  energy  consumption  and  urban  form.  These  indicators 
have  been  used  to  inform  the  upcoming  2016  reports  on  cities  (5)  and  on  energy 
consumption  downscaling  (6).  Commonly,  compact  urban  development  is  considered 
favourable  for  sustainability.  Besides  other  advantages  of  compact  urban  development 
(7), there is growing evidence that compact cities favourably affect the  mode choice for 
walking,  cycling  and  public  transport  (8)  and  that  in  fact  such  cities  produce  less  car 
vehicle  kilometres  travelled  (9,10).  However,  many  aspects  of  transport  energy 
consumption  and  travel  demand  are  under  debate  in  the  academic  literature.  Most 
importantly,  there  is  no  consensus  that  urban  densities  unequivocally  affect  transport 
energy  consumption  or  trip  frequency  (11,12).  In  fact,  transport  energy  consumption 
might be related much more to fuel prices and family income (13).  Other issues raised 
in  the  debate  on  the  sustainability  of  compact  urban  development  are  related  to 
endogeneity  and  induced  demand  (14);  the  degree  in  which  people  commute  shorter 
distances  in  more  compact  cities  (15,16);  the  trade-off  between  energy-efficient 
transport technology and energy-efficient urban form (10,17); and the balance between 
compactness and overconcentration (18). Tackling the many reciprocities between urban 
form, travel demand, fuel prices, car ownership, mode choice and socio-economic status 
for the whole EU territory would go beyond the current capacity of the LUISA modelling 
platform and would require a substantial investment in data in the short-term.  

In order to satisfy urgent needs for policy analysis, three indicators have been developed 
in the LUISA modelling platform, which can be used to approximate the relation between 
urban  form,  road  networks,  travel  demand  and  modal  shift.  These  indicators  describe 
city-specific  potential  for  sustainable  transport,  estimate  the  geography  of  transport 
energy  consumed  on  Europe’s  road  and  measure  urban  form  efficiencies  based  on 
potential public transport service developments. Those use existing urban patterns, road 
networks  and  population  distributions  as  input  data,  but  furthermore  are  based  on  a 
number  of  assumptions  concerning  travel  behaviour,  transport  management,  and  the 
existence  of  a  relationship  between  distances  travelled,  mode  choice  and  energy 
efficiency.  

In section 2, a proxy of travel demand for each inhabited 1 x 1 km grid cell in Europe is 
introduced, along with the first findings from that method. This indicator can be used to 
understand the potential transport energy efficiency of cities, regions and countries; and 
it  can  be  used  to  evaluate  the  impacts  of  future  changes  in  land-use  patterns  and 
transport  networks  on  sustainable  transport  potential.  This  section  furthermore 
introduces an exercise in which the usage of roads is modelled using a transport model 
between  5  x  5  km  grid  cells  and  the  same  assumptions  used  in  section  2.  Preliminary 
results are provided in that section as well. This exercise is instrumental for downscaling 

5 

 
 
 
national energy consumption figures to local levels (6), and it can additionally be used to 
indicate changes in pressure on Europe’s existing road network. Section 3 introduces an 
indicator  in  which  fine-resolution  population  grids  are  used  to  measure  to  what  degree 
cities  can  support  an  effective  public  transport  system;  the  results  indicated  for  this 
analysis are based on the recently published EUROSTAT 2011 population grid data (19). 
Finally, section 4 offers some general conclusions arising from the presented indicators. 

2. A proxy of demand for transport-related energy consumption 

There is a considerable debate on the degree in which energy consumption for transport 
depends on the shape and level of compactness of urban settlements (8,12,13,17). This 
is  relevant  for  the  expansion  of  residential  areas  as  well  as  for  working,  shopping  and 
recreation  space.  Given  that  the  LUISA  modelling  platform  currently  only  simulates 
inhabitants  explicitly,  we  focus  on  the  transport  energy  efficiency  of  residential  areas 
here.  Aspects  that  are  relevant  to  understand  energy  use  for  transport  for  the 
inhabitants of a particular area include the following: 

1)  Available opportunities; 
2)  Choice of destination; 
3)  Choice of transport mode; 
4)  Fuel consumption of the used transport mode; 
5)  Socio-economic status. 

However,  it is  difficult  to  get  an  accurate  estimate  of  the  amount  of  energy  needed  for 
transportation.  There  are  several  reasons  for  this  complexity.  First  of  all,  there  are 
reciprocities between travel distance and trip frequency: e.g., one that lives farther away 
from  a  shop  presumably  goes  to  the  shop  less  frequently.  This  may  yield  unexpected 
results:  driving  eight  kilometres  once  by  car  presumably  yields  better  fuel  efficiency 
compared  to  driving  one  kilometre  eight  times,  because  cars  are  in  general  more  fuel 
efficient  on  longer  distances.  Furthermore,  there  are  also  reciprocities  between  mode 
availability,  mode  choice,  mode  preferences,  residence  choice  and  destination  choice: 
e.g.,  one  that  can  only  move  by  foot  will  choose  central  housing  or  is  restricted  to 
destinations in the immediate vicinity; one that has many transport modes available may 
still choose to pick destinations that are reachable by bicycle. Lastly, estimating the fuel 
consumption of motorized vehicles on a trip is not straightforward, e.g., a one-passenger 
trip  with  a  hybrid  car  is  presumably  more  fuel  efficient  than  a  bus.    All  in  all,  any 
practically achievable estimate of true fuel consumption will have to be nuanced in many 
ways, and will presumably face repeated challenges of its validity. 

To steer away from these threats and still have a straightforward indicator of the level of 
energy efficiency of residential areas, we propose a simple proxy indicator in which, for 
every inhabited 1 x 1 km grid cell in selected urban regions, the average Euclidean travel 
distance is computed. The key question answered by this indicator is: if every inhabitant 
makes the same amount of trips to destinations only within 30 minutes, what Euclidean 
distance  would  the  inhabitants  travel  on  average?  The  underlying  assumption  is  that 
longer travelled Euclidean distances are associated with higher energy consumption and 
reduced opportunities for energy efficient transport modes such as walking and cycling. 
The  limitation  that  every  inhabitant  makes  only  one  trip  removes  the  troublesome 
reciprocities  between  distance,  trip  frequency,  urban  form,  car  ownership,  fuel  prices 
and socio-economic status.  For calculations only trips to destinations within 30 minutes 
of  travel  time  are  concerned.  This  has  a  practical  reason:  to  compute  this  indicator,  a 
matrix with travel-times from all grid cells to all other grid cells has to be obtained, and 
this  can  only  be  achieved  if  the  number  of  destinations  is  held  reasonably  low. 
Restricting travel times has computational advantages in software such as GeoDMS (20), 
where shortest path algorithms have n-complexity, with n being the number  of  origins, 
and the memory and computation burden depends completely on the threshold distances 

6 

 
 
 
that  can  be  applied  to  the  shortest  path  algorithm.  Regardless  of  practical 
considerations, we expect that a threshold of 30 minutes already provides a feasible limit 
which  can  be  associated  with  short-distance  trips  such  as  commuting,  shopping  and 
social visits. In fact, a recent JRC survey shows that working day trips in most surveyed 
European countries are shorter than 30 minutes (21), and we therefore expect that this 
restriction does not severely affect the validity of the findings. 

2.1 Indicator definition 

The transport-related energy consumption indicator is computed as in (1): 

𝐷𝑖 =

1
𝑛

𝑛
∑ 𝑑𝑖𝑗𝑇𝑖𝑗
𝑗=1

, 

(1) 

so that averaged Euclidean distances D for origin grid cells i are obtained from a matrix 
of Euclidean distances d to destinations j given estimated number of trips T. Those trips 
are  again  computed  using  an  origin-constrained  spatial  interaction  model  (22).  For  the 
sake of elegance, we obtain this model as a special case from Alonso’s general theory of 
movements (23) as in (2): 

𝑇𝑖𝑗 = 𝐴𝑖

(1−𝛾)𝑃𝑖(𝑃𝑗 − 𝐼𝑖𝑗)𝑓(𝑐𝑖𝑗), 

(2) 

in  which  the  number  of  trips  depends  on  number  people  P  at  the  origin  and  the 
destination.  The  population  numbers  at  the  destination  are  reduced  with  1  in  the  case 
that the destination and the origin are identical. This is done using values of I (3): 

𝐼𝑖𝑗 =   {

1 𝑖𝑓 𝑖 = 𝑗
0 𝑖𝑓 𝑖 ≠ 𝑗

  , 

(3) 

which takes the value of 1 if the destination and the origin are the same. The reduction 
of people (and thus trip attractiveness) at the destination is done to impose the rule that 
people  cannot  visit  themselves.  This  is  relevant  in  particular  in  sparsely  populated  grid 
cells, where the inclusion of self-visits may have a significant effect on the grid cell’s trip 
distributions.  

The  definition  of  T  also  depends  on  the  distance-decayed  generalised  travel  costs  c.  In 
the  prototype  that  is  being  demonstrated  here,  the  distance  decay  function  is  set  as 
−1.5; the generalised travel costs are  obtained as  shortest-path travel times using the 
𝑐𝑖𝑗
car.  

First tests of the method used data generated and presented by Stępniak and Rosik (24) 
and applied here with explicit permission. The later European-wide implementation uses 
the  fully  available  European  coverage  of  roads  provided  by  commercial  road-data 
provider  TeleAtlas.  By  using  car  travel  times  to  obtain  destination  choices,  this  model 
imposes that inhabitants decide on their destinations based on car availability. If detailed 
travel  time  matrices  for  other  transport  modes  (walking,  cycling,  and  public  transport) 
may  be  obtained,  the  used  car-dependent  travel  times  can  be  replaced  by  travel  times 
from the fastest available transport mode per origin-to-destination relationship. 

Lastly the number of trips depends on the accessibility factor A (4): 

7 

 
 
 
 
 
 
 
−1

𝐴𝑖 =   {∑(𝑃𝑗 − 𝐼𝑖𝑗)𝑓(𝑐𝑖𝑗)

}

, 

𝑗

(4) 

which can in this case be interpreted as accessibility to people. It is, in Alonso’s theory of 
movement (23), used as a balancing factor that ensures that the production of trips at 
an  origin  does  not  increase  proportionally  with  changes  in  transport  costs.  This  is  done 
by keeping the parameter γ between 0 and 1; see equation (2). In this case, 𝛾 has been 
kept  at  1  to  ensure  that  the  production  of  trips  from  an  origin  equals  the  number  of 
inhabitants  at  the  origin,  but  may  be  changed  to  take  into  account  that  the  number  of 
trips again depends on value of travel costs or in fact the local level of A.  

2.2 Results 

2.2.1. Average travel distances: Masovia Region in Poland 

A  prototype  run  for  the  Masovia  region  in  Poland  that  contains  Warsaw  has  been 
computed  as  an  example  of  regional  differences  in  modelled  travel  distances.  If  the 
average  Euclidean  travel  distances  are  plotted  for  the  whole  region  it  is  immediately 
clear  that  city  centres  are,  as  can  be  expected,  associated  with  the  shortest  average 
distances  (see  Figure  1).  In  particular  peripheral  areas  with  low  population  densities 
stand  out  as  areas  with  higher  average  Euclidean  distances.  The  lack  of  attractive 
destinations  in  the  vicinity,  amplified  by  the  detraction  of  1  in  the  attractiveness  of 
intrazonal  trips  causes  substantial  increases  in  average  travel  distance.  Not  presented 
here are additional statistics on the computed  indicator that consider  the breakdown  of 
trips  per  origin.  Such  statistics  could  for  example  indicate  the  number  of  trips  within 
1000  meters  (where  walking  is  a  vital  substitute  for  the  car)  or  within  3000  meter 
(where cycling is a vital substitute). 

8 

 
 
 
 
Figure 1: Average Euclidean travel distances in the Masovia region, Poland. 

The introduced indicator might be used to explore the potential for sustainable transport 
in alternative urban growth scenarios. As an example, the case of Warsaw from Poland is 
examined in detail. This is done using the previously computed travel time matrices (24) 
and the population distribution outcomes of the Compact Development and Business-As-
Usual (BAU) scenarios that have previously been run in LUISA for a previous project (4). 
In  the  BAU  scenario,  the  current  urbanization  process  is  assumed  to  continue  without 
inhibition; in the Compact scenario, urbanization is only allowed in the immediate vicinity 
of existing urban areas. These different scenarios were created specifically to understand 
the  impact  of  local  spatial  policies  on  urban  expansion  patterns  and  associated 
sustainability impacts. In this context they provide a useful example to show the effects 
of different urban development cases on the developed indicator.  

The modelled average travel distances according to the two LUISA  scenarios taken into 
account  are  shown  in  Figure  2.  Although  the  central  areas  with  low  average  travel 
distances are visually dominant in both scenarios, in the BAU scenario (right-side of the 
figure) there is slightly more urban development in Warsaw’s periphery as well as more 
fully isolated urban development patches far from the city. Although the visible impacts 
of the two scenarios are limited, the more abundant scattered urban development in the 
BAU scenario causes considerably higher average travel distances. Thus, in the Compact 
scenario  (left-side  of  the  figure)  an  inhabitant  of  the  Warsaw  region  travels  10.1 
kilometres  on  average,  while  in  the  BAU  scenario  the  region’s  inhabitants  would  travel 
500 metres more on average. It is highly conceivable that a 500 meter increase in travel 
distance will sway many of the trips in the city from slow transport modes to car-based 
trips.  Thus,  the  implications  of  urban  development  for  the  potential  for  sustainable 
transport may be substantial.   

9 

 
 
 
 
Figure 2: An application of the average Euclidean travel distances indicator in Warsaw, 
Poland: Compact vs BAU future urban development. 

2.2.1 Average travel distances: A Europe-wide grid map  

To obtain Europe-wide average travel distances, the method has been applied using the 
EUROSTAT  2011  population  grid  data  (19).  Unfortunately,  due  to  network  data 
unavailability, results are not available for the islands of Denmark, Greece and Spain, as 
well  as  for  the  most  Northeastern  region  of  Poland.  For  the  sake  of  computational 
simplicity, the analysis has been done separately with subsets of origins and destinations 
in  large  regions.  Thus,  for  each  large  region  (a  slightly  adapted  version  of  the  NUTS1 
regional  division  to  be  precise),  average  travel  distances  are  computed  between  the 
origins and destinations within that region. This causes some bias in border pixels where 
travel distances may be under or overestimated; our estimation is that this bias is fairly 
limited  due  to  the  fact  that  NUTS1  regions  generally  have  typical  centre-periphery 
patters in which border pixels are mostly oriented on the region’s centre. In this exercise 
the  general  patterns  of  the  Masovia  region  are  repeated  throughout  all  Europe;  see 
Figure  3.  Noteworthy  is  the  dominance  of  long  travel  distances  around  the  major 
metropolises  of  North-western  Europe  such  as  Brussels,  Paris  and  the  Randstad  and 
Ruhr  areas.  If  we  take  Barcelona  and  Stockholm  as  cases,  cities  that  stand  out  as  two 
cities with very contrasting urban patterns. The common pattern in both of the two cities 
is that average travelled distance is lower in the city centres and higher in the suburbs 
and rural areas. However, it becomes immediately clear that the city of Stockholm has a 
much  more  scattered  urban  development  pattern  with  relatively  low  average  travel 
distances.  This  is  no  doubt  caused  by  the  different  urban  structures  of  those  cities. 
Barcelona has a very dominant city centre  in  which  a large portion  of the population is 
concentrated  and  most  trips  are  terminated.  In  contrast,  Stockholm  has  a  number  of 
sub-centres that draw a considerable amount of trips locally. 

10 

 
 
 
 
Figure 3: Average travel distances (km) in Europe, with particular focus on Stockholm 
and Barcelona. EUROSTAT 2011 population grids have been used to describe population 
distribution. 

2.2.2. A comparison of Functional Urban Areas 

In  order  to  obtain  a  comparison  of  the  relative  performance  of  urban  areas,  travel 
distances  have  been  averaged  for  Europe’s  Functional  Urban  Areas.  The  results  are 
shown  in  Figure  4.  In  that  map  the  dominance  of  long  travel  distances  in  Europe’s 
Northwestern cities is immediately clear.  In contrast,  cities in the Mediterranean  region 
and  in  Europe’s  newest  member  states  have  substantially  lower  average  travel 
distances. The areas of Madrid and Milan make surprising exceptions, with considerably 
higher average travel distances.  

11 

 
 
 
 
Figure 4: Average travel distances in Europe's functional urban areas. 

Table  1  shows  the  10  best  and  10  worst  performing  functional urban  areas  in  terms  of 
modelled  travel  distances  according  to  the  indicator  at  hand.  It  includes  average 
potential accessibility levels A as computed in (4) and the population-weighted densities 
of Europe’s FUAs. Population-weighted density is a method to compute urban densities in 
such  a  way  that  effects  of  the  shape  and  size  of  areal  units  are  less  dominant  by 
indicating the average density in which each resident lives  (25). For a recent discussion 
of  issues  related  to  areal  unit  size  and  shape,  commonly  identified  as  aspects  of  the 
Modifiable Areal Unit Problem, we refer to Jacobs-Crisioni et al. (26). If the expectations 
of  the  advocates  of  compact  urban  development  hold  true,  higher  population-weighted 
densities may be expected to profoundly lower average travel distances.  

From the table of city  performance it becomes immediately clear that urban areas with 
high population-weighted densities (Zaragoza, Thessaloniki, Bucarest) or cities with very 
low  accessibility  values  (Bournemouth-Poole,  Tallinn)  yield  very  low  average  travel 
distances;  while  urban  areas  with  relatively  low  population-densities  and  high 
accessibility values perform much worse. Browsing the table, it becomes  clear that one 
relevant  factor  is  missing  from  this  table,  namely  a  city’s  greater  spatial  context.  All  of 
the  best  performing  urban  areas  are  relatively  isolated,  with  no  attractive  destinations 
outside  of  the  observed  urban  area.  In  contrast,  most  of  the  poorly  performing  urban 
areas  are  part  of  a  larger  urban  network  where  adjacent  urban  areas  exchange  travel 
flows.  This  is  for  example  the  case  for  Amsterdam  and  Utrecht,  as  well  as  for  the 
Ruhrgebiet,  Düsseldorf,  Köln  and  Bonn  urban  areas.  Clearly,  such  overlaps  in  zones  of 

12 

 
 
 
 
influence  and  the  modelled  competition  for  trips  has  a  substantial  impact  on  modelled 
average travel distances. 

Table 1: Large European functional urban areas ranked by estimated average travel 
distance, including potential accessibility levels and population-weighted densities. 

Cityname 

Ran
k 

Countr
y 

Averag
e 
distanc
e 

Potential 
accessibilit
y 

Population-
weighted 
density 

1  Zaragoza 

2  Thessaloniki 

3  Riga 

4  Granada 

5  Bucuresti 

6  Bournemouth-Poole 

7  Bilbao 

8  Palermo 

9  Catania 

10  Tallinn 

.... 

106  Stuttgart 

Braunschweig-Salzgitter-
Wolfsburg 

107 

108  Amsterdam 

109  Münster 

110  Frankfurt am Main 

111  Gent 

112  Bonn 

113  Köln 

114  Utrecht 

115  Ruhrgebiet 

116  Düsseldorf 

ES 

GR 

LV 

ES 

RO 

UK 

ES 

IT 

IT 

EE 

... 

DE 

DE 

NL 

DE 

DE 

BE 

DE 

DE 

NL 

DE 

DE 

3.8 

4.2 

4.6 

5.1 

5.2 

5.4 

5.5 

5.6 

5.6 

5.7 

... 

25438 

21695 

24106 

18312 

49040 

10434 

21204 

16691 

21289 

3756 

... 

11.6 

24326 

11.7 

11.9 

12.0 

12.8 

13.2 

13.9 

13.9 

14.2 

14.9 

15.9 

10369 

32291 

15893 

25976 

21857 

27044 

51575 

41620 

50923 

63933 

197 

151 

72 

82 

164 

34 

148 

86 

59 

57 

... 

34 

23 

59 

31 

39 

27 

28 

48 

50 

38 

47 

Table note: only functional urban areas with at least 500,000 inhabitants are considered. 
Some cities may be missing because the estimated data is missing. 

2.3. Analysis of factors causing travel distance disparities 

The last section’s results make clear that the often presumed relation between average 
travelled  distances  and  urban  density  is  not  as  straightforward  as  it  seems.  In  fact, 
modelled  average  travelled  distances  are  at  least  the  result  of  a  complex  interaction 
between  urban  densities,  accessibility  and  greater  spatial  context.  To  show  this 
interaction,  travel  distances  have  been  made  relative  to  the  average  of  large  urban 
areas. This indicator of relative distances is defined as 𝑅𝐷𝑖 = 𝐷𝑖
 and is computed for 
the  116  Functional  Urban  Areas  in  Europe  with  at  least  500,000  inhabitants.  The 

∑ 𝐷

⁄

𝑛

1

13 

 
 
 
 
included  urban  areas  have  been  categorized  into  areas  with  low  ( 𝑅𝐷𝑖 < 0.8),  medium 
(0.8  ≤ 𝑅𝐷𝑖 ≥ 1.2) and high (𝑅𝐷𝑖 > 1.2) average travel distances. Subsequently, the average 
accessibility levels and population-weighted densities are plotted for those three groups 
of functional urban areas in Figure 5.  

The trend lines of the groups in that graph clearly demonstrate the interaction between 
densities and accessibility. In all cases, accessibility and densities are proportional; this 
is logical, as both variables may be expected to rise with increase population numbers. 
However,  in  low  travel  distance  cities,  minimum  population-weighted  densities  are 
higher  while  average  accessibility  values  are  lower  and  increase  less  with  increases  in 
population-weighted  densities  than  in  cities  with  medium  travel  distances.  In  contrast, 
cities  with  high  average  travel  distances  are  characterized  by  generally  low  population-
weighted  densities  and  high  accessibility  values  that  increase  substantially  with  higher 
population-weighted densities.  

)
s
d
n
a
s
u
o
h
t
n
i
(

y
t
i
l
i

b
i
s
s
e
c
c
a

l

a
i
t
n
e
t
o
P

75

50

25

0

0

50

100

150

200

Population-weighted density of functional urban area 

Low average distance

High average distance

Medium average distance

Linear (Low average distance)

Linear (Medium average distance)

Linear (High average distance)

Figure 5: Population weighted densities versus potential accessibility levels in Europe's 
most populous functional urban areas for different average travel distance classes 

This yields the hypothesis that average travel distances in cities are generally subject to 
two  opposing forces: on the one hand, high potential accessibility values where a well-
developed  transport  system  offer  many  attractive  destinations  within  a  wide 
geographical  range;  and  on  the  other  hand,  high  densities  that  offer  many  attractive 
destinations  within  a limited  distance.  To  test  this  hypothesis  a  regression  analysis  has 
been executed of 663 functional urban areas for which data was available. The following 
equation has been fitted:  

𝐷𝑖 = 𝛽0 + 𝛽1𝑅𝐴𝑖 + 𝛽2𝑅𝑃𝐷𝑖 + 𝜀, 

(5) 

1

⁄

in  which  relative  potential  accessibility  𝑅𝐴𝑖 = 𝐴𝑖
∑ 𝑃𝐷 
densities 𝑅𝑃𝐷𝑖 = 𝑃𝐷𝑖
population-weighted  densities  PD.  A  straightforward  Ordinary  Least  Squares  regression 
method  has  been  applied  to  uncover  the  factors  behind  the  cross-sectional  variety  in 
average  travel  distances.    The  results  of  this  regression  analysis  are  in  Table  2.  The 
results clearly confirm the interaction between travelled distances, potential accessibility 

have been fitted on average travel distances. RPD is based on 

 and  relative  population-weighted 

∑ 𝐴 

⁄

𝑛

𝑛

1

14 

 
 
 
 
 
 
 
 
and  urban  densities:  higher  levels  of  potential  accessibility  increase  average  travel 
distances, while higher densities decrease those distances.  

Table 2: Results regression analysis of the effects of potential accessibility and 
population weighted densities on average travel distances (in kilometers of Euclidean 
distance) in Europe’s Functional Urban Areas. 

Variable 

Coefficient (t-
value) 

Potential accessibility 

2.18** (26.41) 

Population-weighted 
density 

Constant 

N 

R2 

* p<0.05, ** p<0.01 

-2.86** (-26.04) 

8.57** (61.28) 

663 

0.62 

2.4. Network implications 

The  definition  of  fine  resolution  transport  flows  enables  a  further  breakdown  of  the 
distribution of trips over the transport network. This has been done in order to support 
the so-called European Regional Energy Balance and Innovation Landscape (EREBILAND) 
project  undertaken  by  the  JRC  (6).  That  project  aims  at  supporting  innovative  efficient 
patterns  of  regional  energy  supply  and  demand  in  Europe.  For  this  project  a  suite  of 
modelling  tools  is  being  deployed  to  assess  the  efficiency  of  measures  on  energy 
production and sectoral final consumption, and related regional development strategies. 
The  EREBILAND  approach  is  based  on  the  granular  territorial  disaggregation  of 
information,  and  the  development  of  optimisation  scenarios  at  regional  scale  based  on 
the concept of dynamic land functions.  

2.4.1. A proxy of transport consumption at the network link level 

Formerly,  emissions  of  transport  in  Europe  at  the  fine  geographical  level  have  been 
estimated by a number of straightforward proxies based on road categories and distance 
to populations. To improve those estimates, a transport model has been developed that 
is  roughly  based  on  the  assumptions  of  the  previously  introduced  proxy  for  transport 
energy  consumption.  In  this  transport  model,  origin-destination  flows  are  computed 
between 5 x 5 km grid cells using equation (2). Subsequently those flows are allocated 
to  the  very  detailed  TeleAtlas  network  by  means  of  the  principle  that  the  entire  flow  is 
allocated  to  the  route  that  yields  the  shortest  travel  time,  thus  assuming  that  the 
modelled  drivers  have  perfect  knowledge  of  travel  options  and  have  a  choice  behavior 
that is purely determined by travel time considerations. This is the most straightforward 
method  of  flow  allocation  in  transport  modelling;  for  an  overview  of  methods  to  model 
route choice behavior we refer to (27). 

The  allocated  flows  are  subsequently  summed  into  vehicle  kilometers  travelled  by 
multiplying the summed flow per link with the length of the link. Subsequently, the start 
and  end  points  of  every  link  are  related  to  an  underlying  100  m  raster.  Subsequently 
half the vehicle kilometers travelled for each link are associated with the raster through 
the  start  and  end  point.  The  final  raster  indicates  summed  vehicle  kilometers  travelled 
for each point, which is then used as a proxy for transport energy consumption. Because 
the  used  road  network  is  sufficiently  detailed,  this  yields  a  map  in  which  roads  are 
accurately represented as line-like structures.  

15 

 
 
 
2.4.2. Results 

The  results  of  this  exercise  are  demonstrated  in  Figure  6,  Figure  7  and  Figure  8.  The 
produced  data  demonstrates  vehicle  (passenger  car)  kilometers  travelled  for  all  of 
Europe, except for a number of Spanish, Greek, British and Danish islands as well as the 
region surrounding Bialystok in Poland; for those cases, results are not available because 
of data availability issues. In total, consumed vehicle kilometers have been allocated to 
1,092,767 grid cells (roughly 0.05% of the surface mapped in LUISA). Values range from 
1,800 to 457,489 vehicle kilometers travelled. The mean value for grid cells with traffic 
is 5,698; roughly 93% of those grid cells have a value of 31,500 or lower. All in all, the 
modelled distribution of vehicle kilometers travelled is considerably skewed, reminiscent 
of well-known network scaling laws (28).  

Figure 6: Modelled vehicle kilometers travelled in the region surrounding Milan, Italy. 

The  modelled  vehicle  kilometers  travelled  have  been  used  in  the  EREBILAND  project  to 
disaggregate  national-level  passenger  car  data  to  a  fine  geographical  resolution. 
However,  this  method  provides  ample  opportunity  for  other  applications.  For  example, 
this  method  could  be  used  to  uncover  the  impacts  of  modelled  LUISA  outputs  on  road 
demand; and when linked to existing data on travel time losses due to congestion, this 
method  could  be  further  developed  to  more  closely  link  LUISA  projections  with  an 
European-scale transport model such as TRANS-TOOLS (29). 

16 

 
 
 
 
 
Figure 7: Modelled vehicle kilometers travelled in the region surrounding Brussels, 
Belgium. 

17 

 
 
 
 
Figure 8: Modelled vehicle kilometers travelled on roads surrounding four European 
capitals. Lighter values indicate higher values. Different visualization schemes based on 
standard deviations have been applied to uncover variation in the mapped values. 

18 

 
 
 
 
2.5. Conclusions and further work 

This section presents a new indicator that serves as a proxy of energy consumption for 
transport  by  computing  the  Euclidean  distance  travelled  by  an  urban  system’s 
inhabitants  given  specific  limitations.  This  indicator  can  be  computed  for  any  urban 
system  and  is  meant  to  contribute  to  the  sustainability  assessment  of  modelled  future 
city  configurations.  The  presented  indicator  communicates  the  average  travel  distances 
in case all EU inhabitants would have perfectly identical travel choice behaviours. It thus 
only  captures  the  effect  of  spatial  context  on  the  potential  for  sustainable  transport, 
given  that  everybody’s  destination  choices  are  exclusively  determined  by  the  travelling 
options  provided  by  passenger  cars  on  an  uncongested  network.  Contemporary 
availability  and  affordability  of  alternative  transport  modes,  as  well  as  contemporary 
attitudes  towards  travel  are  deliberately  ignored  in  this  study.  Given  that  urban  form 
may  long  outlast  those  contemporary  attitude  and  affordability  considerations,  we 
suspect  this  is  a  fair  exclusion  here  when  evaluating  long  term  impacts  of  land-use 
changes. The only assumptions made on travel behaviour are that the likeliness of a trip 
decreases with travel time, and that people are more likely to make trips to destinations 
with  more  inhabitants.  These  assumptions  are  all  firmly  grounded  in  spatial  interaction 
modelling practice; a practice that has repeatedly been proven to be theoretically sound 
(23,30) and empirically useful (22,31). 

A  comparison  of  LUISA  outcomes  for  Warsaw  shows  that  compact  urban  development 
may affect average travel distances: development according to the LUISA scenario may 
reduce average travel distances with 500 meters by 2030. However, we show that urban 
densities  only  partially  affect  average  travel  distances;  accessibility  to  distant 
destinations acts as a counterweight to urban densities. These results thus demonstrate 
the  real  challenge  of  planning 
for  sustainable  transport:  whenever  transport 
infrastructure is improved, the need for policies to retain high urban densities becomes 
more  stringent.  Unfortunately,  the  results  of  this  section  may  show  that  current  urban 
planning instruments do not seem to be able to rise up and meet this challenge. Even in 
the  Netherlands,  where  compact  urban  development  has  been  the  mainstay  of  spatial 
planning  policies  since  the  1980s  (32),  high  accessibility  values  have  greatly  offset 
higher urban densities, and the country’s major cities are among the worst-performing in 
terms of modelled average travel distances. Of course, these results are for cases where 
only road transport is considered. The environmental results would surely be better if the 
share  of  other  transport  modes  such  as  bicycles  and  public  transport  are  taken  into 
consideration  in  Netherlands.  In  any  case,  these  findings  possibly  demonstrate  that 
those policies that foster compact urban development may have a very limited effect on 
curtailing  transport  demand,  if  not  supported  by  complementary  sustainable  transport 
policy  measures  such  as  public  transport  development,  bicycle  infrastructure  incentives 
or  stringent  parking  limits.  Clearly  more  research  is  needed  to  get  a  full  picture:  for 
example to understand the costs of not doing anything? Furthermore, the usefulness of 
potential  complementary  policies  is  yet  to  be  discovered  –  for  example,  policies  to 
promote public transport ridership or use of slow transport modes.  

A number of improvements to the average travel distances indicator may be considered. 
Most  importantly,  currently  continuous  trips  are  being  allocated.  Thus,  although  any 
inhabitant makes the sum of one trip, that value of one trip can be divided over a large 
number  of  destinations  so  that  in  fact  fractions  of  trips  from  an  origin  are  allocated  to 
various destinations. This is consistent with the concept that every inhabitant is allowed 
an  equal  number  of  trips,  but  the  simulated  travel  pattern  will  only  hold  if  each 
inhabitant  makes  an  equally  very  large  number  of  trips.  Alternatively,  the  computation 
method  could  be  set  to  allocate  trips  discretely.  This  could  be  done  by  estimating  trip 
utility  and  possibly  by  introducing  a  random  utility  component.  That  way  the  method 
essentially  answers  what  the  pattern  would  be  if  every  inhabitant  makes  only  one  trip. 
Another  potential  improvement  concerns  transport  modes.  Currently  the  relationship 
between trips and transport modes is left unexplored. If additional information on travel 

19 

 
 
 
times for other transport mode becomes available, a simple parameterized choice model 
may be used to obtain potential percentage mode usage for each origin.  

Lastly, the indicator allows for a further disaggregation of the modelled trips to Europe’s 
transport  networks.  Such  disaggregation  can  yield  useful  information  on  where  energy 
for transport is being consumed. This is useful for disaggregation works such as done in 
the EREBILAND project; it may also be useful to map environmental problems related to 
traffic such as air and noise pollution. This  section shows the first results of a transport 
modelling  attempt  in  which  vehicle  kilometres  travelled  on  the  network  are  eventually 
disaggregated  to  100  meter  grid  cells.  Further  work  will  be  needed  to  verify  the 
empirical validity of this method and integrate it with interregional traffic flows, such as 
modelled by the TRANS-TOOLS model.   

20 

 
 
 
 
 
3.  A  measure  of  urban  form  efficiency  with  the  ease  of  access  to 
potential public transport services 

It is repeatedly noted that urban form can have a substantial impact on public transport 
ridership  (8).  Underneath  this  are  the  facts  that  public  transport  (PT)  investments 
depend very much on land use characteristics and that PT stops generally have a limited 
geographical  reach.  Walking  distance  is  an  important  limiting  factor  in  particular  for 
transport  modes  that  are  meant  to  serve  the  direct  urban  area,  such  as  buses.  Recent 
evidence from Australia for instance shows that the majority of bus users live within 500 
meter of a bus stop (33). PT is assumed to be a more sustainable option when compared 
to  passenger  cars.  Given  the  dependence  of  PT  ridership  on  urban  form,  this  begs  the 
question of how efficient Europe’s cities are for supporting PT services.  

Today,  promotion  of  PT  in  urban  areas  and  concentration  of  urban  development  at  PT 
nodes  are  given  high  importance.  Such  policies  serve  to  foster  sustainable  urban 
development and transport policies, together with complementary policies that strive for 
compact  city  development.  Such  compact  city  policies  aim  at  facilitating  walking  and 
cycling,  easing  access  to  PT  services  and  increasing  the  share  of  PT  usage.  Such 
compact cities are expected to have several benefits. The most straightforward ones are 
I)  less  car  dependency  and  lower  emissions,  II)  less  energy  consumption,  III)  higher 
overall accessibility, and finally IV) preserved green infrastructure in the peripheries. The 
indicator introduced here is designed to explore how compact European cities are, or, in 
other  words,  how  efficient  European  cities  are  in  terms  of  potential  for  PT  service 
development  and  ease  of  access  to  PT  services.  A  spatial  simulation  approach  with  a 
number of critical assumptions has been applied for this measure.  

This  exercise  attempts  to  explore  urban  form  efficiency  by  recreating  minimal  public 
transport  system  coverage  in  Europe’s  cities,  in  a  method  governed  by  three  major 
assumptions: 

  The  first assumption is that a public transport  system with a smaller  geographic 
span  is  more  efficient  both  from  the  costs  and  transport  service  point  of  view; 
thus,  a  transport  system  that  reaches  more  people  with  less  PT  stops  will  have 
higher ridership at lower costs.  

  The  second  assumption  is  that  any  public  transport  system  should  cover  a 
substantial  proportion  of  a  city’s  population.  Preferably,  the  majority  of  the 
population for a more sustainable system. 

  The  third  assumption  is  that  the  coverage  of  public  transport  stops  is  limited  to 

easily walkable distances, set at maximum 500 meters. 

Given  these  main  assumptions,  the  indicator  introduced  here  counts  the  minimum 
number of possible PT stops that serve at least 80% of a city’s population; measures the 
average  distance  between  these  PT  stops;  and  counts  the  inhabitants  that  those 
minimum number of PT stops serve. Hence, cities that require less PT stops, cities with 
PT  stops  that  are  closer  to  each  other,  and  cities  with  PT  stops  that  separately  serve 
more  inhabitants  are  considered  as  more  efficient  for  the  potential  development  of  PT 
services. On the contrary, cities that require more PT stops, or cities with PT stops with 
greater  distances  in  between,  or  PT  stops  that  separately  serve  less  inhabitants  are 
considered  as  less  efficient  in  terms  of  urban  form  and  ease  of  access  to  potential  PT 
services.  

3.1. Definition of the indicator 

The method for the indicator is based on a 100 m population grid. Each city, in this case 
Functional Urban Areas (FUAs), is a separate subset of 100 m grids. In each iteration the 
method allocates one public transport stop to the optimal place (the central pixel of the 
most populated catchment area) in a city. To do so it searches the whole city’s surface 
and attempts to find the pixel with maximum number  of inhabitants within 500 meters 
radius. An already covered inhabitant by a PT stop is not counted again for the next PT 

21 

 
 
 
stop  allocations,  so  that  the  optimal  location  for  the  next  PT  stop  is  different  from  the 
previous ones. The only input to the indicator is  a population grid which can be defined 
as a matrix at iteration zero as, Pij

0. 

The procedure starts from the initial cell, say  origin,  0mn, m being the  row and n being 
the column numbers of the cell like the i and j in the population matrix. Then determining 
the  catchment  area  for  the  origin  cell  using  the  500  meters  distance  criterion,  with  a 
spatial weight matrix, Wmn

ij, which in turn is defined as: 

𝑊𝑚𝑛

𝑖𝑗 =   {

1 𝑖𝑓 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑂𝑚𝑛 𝑎𝑛𝑑 𝐷𝑖𝑗) ≤ 500𝑚
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(6) 

It is static, not changes in each iteration and measures the distance between the origin 
cell  0mn  and  all  other  destination  cells  Dij.  The  catchment  area  populations,  CPmn
r,  and 
ij
the total population, CPmnr, at iteration r, for the origin cell then can be defined using the 
distance and population matrixes as: 
𝑟 =   𝑊𝑚𝑛

𝐶𝑃𝑚𝑛

(7) 

𝑟  

𝑖𝑗

𝐶𝑃𝑚𝑛𝑟 =   ∑ 𝐶𝑃𝑚𝑛

𝑖𝑗𝑃𝑖𝑗
𝑟  

𝑖𝑗

(8) 

r, 
If the equations 6, 7 and 8 repeated for all cells taking them as origins, a matrix,  CPij
that includes the total populations, within 500 meters for each cell as an origin, can be 
achieved via: 

𝐶𝑃𝑖=𝑚,𝑗=𝑛

𝑟 =   𝐶𝑃𝑚𝑛𝑟  

(9) 

r matrix gives us the best PT stop allocation as selected cell 
Finding the maximum of CPij
at  iteration  r.  Then  the  coordinates  of  the  selection(s)  can  be  recorded  to  the  vectors, 
SELr1 and SELr2 as following: 

𝑆𝐸𝐿𝑟,1:2 = 𝑤ℎ𝑖𝑙𝑒 𝐶𝑃𝑖𝑗

𝑟 = max(𝐶𝑃𝑖𝑗

𝑟)  

𝑆𝐸𝐿𝑟1 = 𝑖
𝑆𝐸𝐿𝑟2 = 𝑗

(10) 

The  procedure  continues  up  until  to  a  threshold.  The  80%  of  population  should  be 
covered  with  the  selected  PT  stops.  This  can  be  achieved  via  calculating  the  covered 
population,  Popr,  and  the  share  of  covered  population  by  the  selected  PT  stops,  Spopr, 
which are increasing in each iteration: 

𝑚𝑎𝑥𝐶𝑃𝑟 =   max (𝐶𝑃𝑖𝑗

𝑟)    

𝑃𝑜𝑝0 = 𝑚𝑎𝑥𝐶𝑃0  

𝑃𝑜𝑝𝑟 = (𝑃𝑜𝑝𝑟−1 + 𝑚𝑎𝑥𝐶𝑃𝑟)  

𝑆𝑃𝑜𝑝𝑟 = (𝑃𝑜𝑝𝑟) (∑ 𝑃𝑖𝑗

⁄

0)

(11) 

(12) 

(13) 

(14) 

The procedures from equations 6 to 14 are repeated with increasing r until Spopr<0.80, 
at  which  point  the  procedure  stops.  Finally,  in  order  to  remove  the  already  counted 
population in any  r+1  allocation, the population  of cells within the catchment area  of a 
selected PT stop, CPmn

ij, should be subtracted from the initial population matrix, Pij

r: 

𝑃𝑖𝑗

𝑟+1 = 𝑃𝑖𝑗

𝑟 − 𝐶𝑃𝑚𝑛

𝑟   

𝑖𝑗

(15) 

Here  m  is  the  row  and  n  is  the  column  of  the  selected  PT  stop  in  iteration  r,  achieved 
respectively  via  SELr1  and  SELr2 in  equation  10.  The  subtracted  population  matrix  then 
serves in equation 7 to calculate new catchment areas population and ensures that the 
best option for a PT stop is different than the previous ones. 

After  the  search  for  new  public  stop  sites  is  ended  with  a  final  iteration  r,  the  average 
distance, AvrD, between the public transport stops, where the coordinates were recorded 
to the columns of SEL, is computed as: 

𝐴𝑣𝑟𝐷 =  

∑ 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑆𝐸𝐿𝑖. 𝑎𝑛𝑑 𝑆𝐸𝐿𝑗.)
(𝑟2−𝑟)

(16) 

22 

 
 
 
    
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
    
 
 
 
 
 
 
Finally the urban efficiency indicator (UFE) for a city is computed in a normalized way so 
that: 

𝑈𝐹𝐸 =  

(𝑃𝑜𝑝)

(𝑃𝑇𝑆)(𝐴𝑣𝑟𝐷)

(17) 

Where PTS is the number of PT Stops that serve 80% of the population, it is equal to the 
number of iterations, r; AvrD stands for the average distance between the PT Stops; and 
finally, the variable Pop stands for the total population that the allocated PT Stops serve 
(80%  of  overall  total)  as  achieved  with  the  equation  13.  The  consequence  is  that  the 
urban  efficiency  indicator  computes  number  of  public  transport  stops  and  their 
underlying distance relative to city population. Thus, if population is held equal, a higher 
number  of  transport  stops  or  a  higher  average  distance  between  those  stops  leads  to 
lower efficiency. If transport stops and average distance between stops is held equal, a 
lower population number will lead to a lower efficiency. 

3.2.  Results 

The  indicator  has  been  computed  for  all  FUAs  in  Europe  as  a  pilot  study,  using  the 
EUROSTAT Population Grid for 2011 (19). The grid population counts at 1 km resolution. 
Since the algorithm runs at 100 m, the EUROSTAT grid was equally resampled to 100 m 
grids.  Table  3 introduces  the  results,  a  classification  of  Europe’s  cities  (FUAs)  based  on 
the  Urban  Form  Efficiency  (UFE)  indicator  measured  via  ease  of  access  to  potential  PT 
services.  As  mentioned  earlier,  the  method  first  identifies  the  minimum  number  of  PT 
stops  that  could  serve  80%  of  a  city’s  total  population,  and  second  computes  the 
average  distance  between  the  identified  PT  stops.  It  finally  normalizes  multiplication  of 
these two values with the population that is served within the city. If the majority (80% 
of the population) of a city can be served with few PT stops and these stops are close to 
each other, this city has a more efficient urban form. On the contrary, if a city is served 
with a large number of PT stops and the average distance among these PT stops is high, 
the city has a less efficient urban form. 

A further analysis has classified FUAs into five groups according to a novel classification 
method that takes into account a FUA’s geographical size and its population distribution. 
In  this  classification  method  circles  are  drawn  around  a  FUA’s  centre  and  subsequently 
the  population  in  the  underlying  population  grid  is  summed  to  that  circle.  This  is  done 
repeatedly  with  different  circle  sizes  ranging  from  10  to  50  kilometers.  The  circle  that 
contains  at  least  as  many  people  as  are  recorded  in  the  FUA  is  chosen;  the  FUA  is 
subsequently  classified  into  the  group  of  FUAs  with  similar  circle  sizes.  The  indicator  is 
designed  to  make  comparisons  within  groups  of  similar  cities,  like  Paris  vs  London  or 
Rotterdam  vs  Porto  and  is  a  useful  tool  to  group  heterogeneous  units  into  comparable 
groups,  regardless  of  geography,  and  relatively  independent  of  modifiable  areal  unit 
problems  caused  by  the  delineation  of  areal  units  (34).  It  can  be  used  with  different 
units of measurement, such as FUAs or LAU2 zones.  Which city belongs to which group 
can be seen in the Table 3. The 20th percentiles are taken as the changing point for each 
group of UFE. Table 3 indicates FUAs in a descending order from high to low urban form 
efficiency. 

As  indicated  in  the  table  and  in  Figure  9,  the  cities  (FUAs)  in  Spain,  Greece,  Bulgaria, 
Romania and Lithuania are mostly with very high urban form efficiency compared to the 
others.  In  other  words,  the  cities  in  these  countries  are  more  compact  considering  the 
distribution  of  population  and  ease  of  access  to  potential  PT  services.  The  countries 
including the United Kingdom, Croatia, the Netherlands and Italy have cities with mostly 
very  high  and  high  urban  efficiencies.  On  the  other  hand,  majority  of  the  cities  in 
countries  like  Denmark,  Belgium,  France,  Germany  and  Poland  have  low  or  very  low 
urban form efficiency if the distribution of population across the territory and the ease of 
access to potential PT services are taken into consideration.  

23 

 
 
 
  
 
 
 
 
 
 
 
 
 
 
Table 3: Urban form efficiency with the ease of access to potential public transport 
services – classification by FUAs. 

Size of Cities (Functional Urban Areas) 

Very Small 

Small 

Moderate 

Large 

Very Large 

s
e
c
i
v
r
e
S
T
P

l
a
i
t
n
e
t
o
P
o
t

s
s
e
c
c
A
f
o
e
s
a
E
e
h
t
h
t
i

w
y
c
n
e
i
c
i
f
f
E
m
r
o
F
n
a
b
r
U

Very 
High 

High 

Moderate 

Low 

Very Low 

Talavera de la 
Reina, Melilla, 
Ceuta, Narva, Lugo, 
Brăila, Botosani, 
Cáceres, Ciudad 
Real, Craiova, …, 
Cádiz, Alytus, 
Buzau, Avilés, 
Girona, Elda, 
Annemasse, 
Algeciras, Ploiesti, 
Manresa, Puerto de 
Santa María 
Guadalajara, Ferrol, 
Satu Mare, Bacău, 
Inowrocław, Luton, 
Irun, Cǎlǎraşi, 
Târgu Mureş, 
Katwijk, Delft, 
Kalamata, Setúbal, 
Plymouth, …, Vidin, 
Darlington, Caserta, 
Swindon, Frankfurt,  
Piatra Neamţ, 
Leiden 

Bârlad, 
Northampton, 
Peterborough, 
Veliko Tarnovo, 
Gloucester, 
Cheltenham, 
Pescara, Benidorm, 
Oxford, 
Southampton, …, 
Bradford, Savona, 
Reading, Milton 
Keynes, Warwick, 
Alkmaar, Crawley, 
Prato, Nijmegen, 
Worcester 
Pabianice, 
Deventer, 
Hilversum, Gouda, 
Torbay, Krefeld, 
Torrevieja, 
Bergamo, 
Remscheid, 
Sunderland, …,  
Chemnitz, Redditch, 
Solingen, Bielefeld, 
Viareggio, 
Brandenburg an der 
Havel, Acireale 
Heerlen, Almelo, 
Alphen aan den 
Rijn, Martigues, 
Roman, Wycombe, 
Lecco, …, 
Chesterfield, Como, 
Varese, Avellino, 
Kortrijk, Póvoa de 
Varzim, Biella, 
Treviso, Pordenone, 
Guimarães, Viana 
do Castelo, 

Burgos, Bucureşti, 
Valencia, 
Thessaloniki, 
Logroño, Salamanca 
… 
Rotterdam, 
Siracusa, Trieste, 
Livorno, Bristol, 
Liverpool 
… 

North East 
Lincolnshire, 
Middlesbrough, 
Bournemouth, 
Newcastle, Lille, 
Sliven 
… 
Porto, Köln, 
Portsmouth, Utrecht, 
Nottingham, Leeds, 
Düsseldorf, Kirklees, 
Lecce, Eindhoven, 
Derby, 
Shumen, Most, 
Kassel, La Spezia, 
Kavala, Darmstadt 
... 
Middelburg, Pisa, 
Verona, Metz, 
Helsingborg 
... 
Reutlingen, Modena, 
Liège, Roosendaal, 
Salzburg, Koblenz 

Zaragoza, Albacete, Pamplona, 
Valladolid, Córdoba, Bilbao, 
Vitoria/Gasteiz, Sevilla, Varna, 
Palermo, Málaga, Genova, 
Granada, Napoli, Białystok, 
Torino, León, Cagliari, 
Marbella, Taranto, 
Amsterdam, Milano, 
Daugavpils, Plovdiv, 
Panevėžys, Malmö, West 
Midlands urban rea, Nice, 
Katowice, Manchester, 
Wrocław 
Santander, Burgas, Edinburgh, 
Bydgoszcz, Bratislava, Toulon, 
Frankfurt am Main, Kingston 
upon Hull, Leicester, Cardiff, 
Sassari, Tampere, Montpellier, 
Brussels, Norrköping, 
Hannover, Nürnberg, Olsztyn, 
Bologna, Grenoble, Västerås, 
Szeged, Turku, Innsbruck, 
Perpignan, Debrecen, Leipzig, 
Ioannina, Košice, Nancy 

Exeter, Lahti, Münster, 
Karlsruhe, Clermont-Ferrand, 
Toledo, Ajaccio, Reims, 
Strasbourg, Pleven, 
Magdeburg, Groningen, 
Venezia, Ostrava, Freiburg im 
Breisgau, Rouen, Augsburg, 
Tours, Ljubljana, Plovdiv, Halle 
an der Saale, Koszalin, Plzeň, 
Miskolc, Lübeck, Erfurt, 
Jelgava, Kecskemét, Brest, 
Dunkerque, Saarbrücken 

Jastrzębie-Zdrój, 
Charleroi, Derry, 
Głogów, Cosenza, 
Trento 
… 
Parma, Trenčín, 
Aschaffenburg, 
Lubin, Avignon, 
Pardubice 
… 
Gent, Lorient, 
Brugge, Pavia, Sant. 
De Compostela 
Colmar, Braga, 
Belfort, Hildesheim, 
Jelenia Góra, 
Lüneburg 
… 
Ferrara, 
Friedrichshafen, 
Leuven, Namur, 
Quimper, Kalisz 
… 
Evreux, Zamość, 
Asti, Nowy Sącz, 
Viseu 

Norwich, Caen, Saint-Etienne, 
Angers, Pécs, Oldenburg, 
Bremerhaven, Ulm, Limoges, 
Troyes, Le Mans, Linz, 
Osnabrück, Konstanz, La 
Rochelle, České Budějovice, 
Regensburg, Amiens, 
Würzburg, Székesfehérvár, 
Osijek, Gyõr, Luxembourg, 
Ingolstadt, Potenza, 
Göttingen, Częstochowa, 
Châteauroux, Bourges, 
Nyíregyháza 
Cottbus, Pau, Besançon, 
Flensburg, Opole, 
Kaiserslautern, Siegen, 
Poitiers, Schweinfurt, 
Cherbourg, Rosenheim, Kielce, 
Szombathely, Kempten 
(Allgäu), Plauen, Slavonski 
Brod, Stalowa Wola, Görlitz, 
Fulda, Rzeszów, Saint-Brieuc, 
Klagenfurt, Bayreuth, Chalon-
sur-Saône, Coimbra, Płock, 
Angoulème, Niort, Tarnów, 
Maribor, Passau 

København 
Athina 
Mallorca 
Marseille 
Vilnius 
Gdańsk 
Lisboa 
Glasgow 

Madrid 
Barcelona 
Paris 
Sofia 
Berlin 
Tallinn 

Stuttgart 
Tartu 
Lyon 
Toulouse 
Grad Zagreb 
Split 
Poznań 

Stockholm 
London 
Roma 
Wien 
Dublin 
Warszawa 

Budapest 
Helsinki 
Liepāja 
Aberdeen 
Hamburg 

Bremen 
Rostock 
Oulu 
Bordeaux 
Jönköping 
Uppsala 
Linköping 
Örebro 

Ruhrgebiet 
München 
Praha 
Umeå 
Göteborg 
Kuopio 

Brno 
Kiel 
Braunschwei
g-Salz.-
Wolfs.  
Nantes 
Orléans 
Dijon 
Cork 

Graz 
Schwerin 
Stralsund 
Greifswald 
Rennes 
Galway 
Kraków 
Lublin 

Århus 
Jyväskylä 
Dresden 
Odense 
Aalborg 
Neubrande
nburg 

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 9: Urban form efficiency with the ease of access to public transport services in 
Europe's Functional Urban Areas. 

25 

 
 
 
 
 
 
4. Conclusions 

Sustainable transport  for  Europe’s cities is a key priority for the  European Commission. 
This  report  has  introduced  three  novel  indicators  that  report  on  various  aspects  of 
sustainable  transport.  Two  of  those  indicators  link  urban  form  with  its  potential  to 
support sustainable transport, and one indicator builds on the presented simulation work 
to  provide  a  proxy  of  the  amount  of  energy  consumed  on  Europe’s  roads.  In  all  cases, 
these  indicators  are  based  on  closed-environment  simulation  exercises,  in  which  many 
empirically relevant aspects of travel behaviour are excluded. The choice for such closed-
environment simulations is a necessary compromise given, on the one hand, the need to 
report on highly policy-relevant aspects of transport with a local basis; and on the other 
hand, a distinct lack of Europe-wide data on travel behaviour on a sufficiently fine spatial 
resolution. A number of sensitivity analyses will be needed to test the sensitivity of the 
used indicators for specific parameter settings, and an additional empirical validation will 
be  needed  to  find  to  what  degree  the  presented  indicators  reflect  real-world  situations. 
The nature of the presented indicators makes them a useful addition in particular when 
multiple  scenarios  or  time  intervals  are  compared,  so  that  any  systematic  bias  in  the 
results  is  cancelled  out.  Despite  the  closed-environment  nature  of  the  presented 
indicators, these indicators still add considerably to the body of knowledge on European 
cities,  and  will  provide  useful  additions  to  the  toolbox  of  policy  makers  that  occupy 
themselves with sustainable transport.   

26 

 
 
 
 
 
 
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29 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of abbreviations and definitions 

BAU scenario 

Business-as-usual scenario run in LUISA model 

Compact scenario  Compact scenario run in LUISA model 

EC 

EREBILAND 

FUA 

grid cell 

JRC 

LUISA 

NUTS1 

PT 

European Commission 

“European Regional Energy Balance and Innovation LANDscape” 
project 
Functional urban area data reporting system 

One unit in a fine resolution regularly latticed data reporting system 

Joint Research Centre 

"Land Use-based Integrated Sustainability Assessment" model 

1st level of "Nomenclature of Territorial Units for Statistics" 

Public Transport 

TeleAtlas 

Network data provider 

TRANS-TOOLS 

"TOOLS for TRansport Forecasting ANd Scenario testing" model 

30 

 
 
 
 
 
 
List of figures 

Figure 1: Average Euclidean travel distances in the Masovia region, Poland. ................. 9 

Figure  2:  An  application  of  the  average  Euclidean  travel  distances  indicator  in  Warsaw, 
Poland: Compact vs BAU future urban development. ............................................... 10 

Figure  3:  Average  travel  distances  (km)  in  Europe,  with  particular  focus  on  Stockholm 
and Barcelona. EUROSTAT 2011 population grids have been used to describe population 
distribution. ........................................................................................................ 11 

Figure 4: Average travel distances in Europe's functional urban areas. ....................... 12 

Figure  5:  Population  weighted  densities  versus  potential  accessibility  levels  in  Europe's 
most populous functional urban areas for different average travel distance classes ...... 14 

Figure 6: Modelled vehicle kilometers travelled in the region surrounding Milan, Italy. . 16 

Figure  7:  Modelled  vehicle  kilometers  travelled  in  the  region  surrounding  Brussels, 
Belgium. ............................................................................................................ 17 

Figure  8:  Modelled  vehicle  kilometers  travelled  on  roads  surrounding  four  European 
capitals. Lighter values indicate higher values. Different visualization schemes based on 
standard deviations have been applied to uncover variation in the mapped values. ..... 18 

Figure  9:  Urban  form  efficiency  with  the  ease  of  access  to  public  transport  services  in 
Europe's Functional Urban Areas. .......................................................................... 25 

31 

 
 
 
 
 
 
List of tables  

Table  1:  Large  European  functional  urban  areas  ranked  by  estimated  average  travel 
distance, including potential accessibility levels and population-weighted densities. ..... 13 

Table  2:  Results  regression  analysis  of  the  effects  of  potential  accessibility  and 
population  weighted  densities  on  average  travel  distances  (in  kilometers  of  Euclidean 
distance) in Europe’s Functional Urban Areas. ......................................................... 15 

Table  3:  Urban  form  efficiency  with  the  ease  of  access  to  potential  public  transport 
services – classification by FUAs. ........................................................................... 24 

32 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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33 

 
 
 
 
 
 
 
 
 
 
 
 
 
L
B
-
N
A
-
2
7
7
0
8
-
E
N
-
N

JRC Mission 

As the Commission’s  
in-house science service,  
the Joint Research Centre’s  
mission is to provide EU  
policies with independent,  
evidence-based scientific  
and technical support  
throughout the whole  
policy cycle. 

Working in close  
cooperation with policy  
Directorates-General,  
the JRC addresses key  
societal challenges while  
stimulating innovation  
through developing  
new methods, tools  
and standards, and sharing  
its know-how with  
the Member States,  
the scientific community  
and international partners. 

Serving society  
Stimulating innovation  
Supporting legislation 

doi:10.2788/59611 

ISBN 978-92-79-54593-1 

34