fUel-SAVing trip plannEr (U-SAVE) : a product of the JRC PoC instrument : final report.
Date: 2018-02-02
Institutions: Joint Research Centre
Subjects: energy policy, energy research, energy technology, innovation, intelligent transport system, means of transport, motor fuel, price of energy, reduction of gas emissions, research report, travel

fUel-SAVing trip plannEr (U-SAVE): 
A product of the JRC PoC 
Instrument 

Final Report 

Arcidiacono, V., Maineri, L., 
Tsiakmakis, S., Fontaras, G., Thiel, C., 
Ciuffo, B. 

2017 

EUR 29099 EN

 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
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 that 
might be made of this publication. 

Contact information 
Name: Biagio Ciuffo 
Address: European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra (VA) - Italy 
Email: biagio.ciuffo@ec.europa.eu  
Tel.: +39 0332 789732  

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

JRC110130 

EUR 29099 

PDF 

ISBN 978-92-79-79359-2 

ISSN 1834-9424 

doi:10.2760/57939 

Luxembourg: Publications Office of the European Union, 2017  

© European Union, 2017 

Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents 
is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

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sought directly from the copyright holders. 

How to cite this report: Arcidiacono, V., Maineri, L., Tsiakmakis, S., Fontaras, G., Thiel, C. and Ciuffo, B., fUel-
SAVing trip plannEr (U-SAVE): A product of the JRC PoC Instrument - Final report, EUR 29099 EN, Publications 
Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-79359-2, doi:10.2760/57939, JRC110130. 

All images © European Union 2017 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Contents 

Acknowledgements .............................................................................................. 1 

Abstract ............................................................................................................. 2 

1  Introduction ................................................................................................... 3 

1.1  The Context .............................................................................................. 3 

1.2  The Approach ............................................................................................ 4 

2  Methodology ................................................................................................... 6 

2.1  Find the most fuel-efficient path .................................................................. 6 

2.2  Calibration of fuel consumption raster ........................................................... 7 

2.3  Calculate the fuel consumption on a route ..................................................... 7 

2.4  Optimization of the velocity profile ............................................................... 8 

3  Implementation ............................................................................................. 10 

3.1  U-SAVE Desktop Version ............................................................................ 10 

3.1.1  OSRM Server ................................................................................... 11 

3.1.2  U-SAVE Core Module ......................................................................... 11 

3.1.3  Web Interface .................................................................................. 11 

3.2  U-SAVE Navigation Application ................................................................... 11 

3.2.1  User Authentication & Vehicle Inputs ................................................... 11 

3.2.2  Route Selection ................................................................................ 12 

3.2.3  Navigation System ............................................................................ 13 

3.2.4  OBD-II Interface ............................................................................... 13 

3.2.5  Data Collection ................................................................................. 13 

4  Validation ...................................................................................................... 14 

4.1  Analytical Validation .................................................................................. 14 

4.2  Real Test Cases ........................................................................................ 16 

5  Exploitation ................................................................................................... 17 

5.1  Competition Analysis ................................................................................. 17 

5.2  Market Analysis ........................................................................................ 18 

5.3  Business & Financial Model Analysis ............................................................ 18 

5.3.1  Business Model ................................................................................. 18 

5.3.1.1  Revenue Model ........................................................................... 18 

5.3.1.2  Server Cost ................................................................................ 19 

5.3.2  Financial Estimates ........................................................................... 20 

6  Conclusions ................................................................................................... 22 

References ........................................................................................................ 24 

List of figures .................................................................................................... 26 

List of tables ...................................................................................................... 27 

i 

Annexes ............................................................................................................ 28 

Annex 1. Real Test Cases Material .................................................................... 28 

Annex 2. U-SAVE Business Plan ........................................................................ 34 

ii 

Acknowledgements 

The authors would like to acknowledge the help of all CO2MPAS and Green Driving team 
members, namely Kostis Anagnostopoulos, Victor Valverde, Iker Prado Rujas, Alessando 
Tansini, Dimitrios Komnos, Jelica Pavlovic, for all the discussions and the support related 
to this work. 

The authors are grateful to the staff of the Vehicle Emission Laboratory (VELA) of the JRC 
for having carried out all the tests used in this analysis. 

Authors 

Vincenzo Arcidiacono, Joint Research Centre 

Lorenzo Mainieri, Joint Research Centre 

Stefanos Tsiakmakis, Joint Research Centre 

George Fontaras, Joint Research Centre 

Christian Thiel, Joint Research Centre 

Biagio Ciuffo, Joint Research Centre 

1 

 
Abstract 

Available tools for trip planning mostly rely on travel time and travel distance. Fuel costs, 
when  taken  into  account,  are  based  on  simplified  fuel  consumption  models  and  are 
usually independent from vehicle type and technology.  

Building on the work carried out by the Sustainable Transport Unit of the Joint Research 
Centre,  European  Commission,  in  developing  (a.)  CO2MPAS,  the  official  tool  supporting 
the WLTP/NEDC Correlation Exercise and allowing the back-translation of a WLTP test to 
the  equivalent  NEDC  CO2  emission  value  during  the  type  approval,  and  (b.)  Green 
Driving,  an  interactive  web-based  tool  allowing  the  estimation  of  fuel  costs  and  CO2 
emissions  of  individual  car  journeys  on  the  basis  of  variables  such  as  car  segment, 
engine  power,  fuel  type  and  driving  style,  the  present  project  aimed  at  developing  and 
proving the concept of a routing machine to be used when fuel consumption minimization 
is considered.  

Throughout the project a stand-alone off-board trip planner has been developed, the U-
SAVE  Desktop  Version,  while  a  smartphone  application,  the  U-SAVE  Navigation 
Application,  is  currently  under  the  last  development  phase,  and  shall  be  used  once 
completed as a low cost in-board navigation system.  

The  tool  has  been  extensively  validated  internally  demonstrating  both  its  capability  to 
accurately  estimate  fuel  and  energy  consumption  via  alternative  trip  options,  and  its 
capacity to provide a more efficient route when different from the shortest and/or fastest 
options. 

An  open-access  version  of  the  tool  is  expected  to  become  a  reference  instrument  for 
private citizens who are concerned about their fuel consumption and a more efficient use 
of their vehicles, while a premium API-based commercial version of the tool can operate 
as a viable and scalable business model targeting, among others, established navigation 
software  providers  who  want  to  extend  their  offering  by  providing  an  alternative  route 
option  to  their  clients,  mainly  private  companies  managing  fleets  of  light-duty  vehicles, 
for whom saving fuel from the daily vehicle operations is of crucial financial importance. 

2 

 
1  Introduction 

1.1  The Context 

Transport  represents  almost  a  quarter  of Europe's  greenhouse  gas  emissions  and  is  the 
main  cause  of  air  pollution  in  cities.  Within  this  sector,  road  transport  is  by  far  the 
biggest  emitter  accounting  for  more  than  70%  of  all  GHG  emissions  from  transport  in 
2014 [1].  

Figure 1. Greenhouse Gas Emissions in Europe. 

Notes: * Transport includes international aviation but excludes international maritime; ** Other include fugitive 
emissions from fuels, waste management and indirect CO2 emissions. Source: EEA. 

Notes: Greenhouse gas emissions from transport by mode in 2014; Share of transport energy demand by mode 
in 2014 (%). Source: EEA. 

At the same time, intra-EU freight transport demand has increased by 2.8% per year on 
average  from  1995  till  2007,  with  a  corresponding  growth  rate  for  passenger  transport 
demand  of  1.7%  (in  passenger  km).  Road  transport  modes,  car  and  coaches,  have 
basically proved to be the most important modes for meeting that demand.  

3 

 
 
 
In this context, the European Commission’s low-emission mobility strategy [2], adopted 
in  July  2016,  aims  to  ensure  Europe  stays  competitive  and  able  to  respond  to  the 
increasing mobility needs of people and goods, while fuel economy increasing measures 
should  be  adopted  both  at  a  higher-  and  at  an  individual-,  i.e.  drivers  and  distribution 
companies, level. Fuel consumption can indeed be reduced – when the traffic demand is 
considered  constant  –  by  increasing  the  efficiency  of:  (a)  the  vehicle,  and/or  (b)  the 
transport system.  

Main  policy  tools  have  so  far  focused  on  the  vehicle  efficiency.  The  new  strategy, 
however, calls for higher efforts in the overall transport sector. Two key elements in the 
efficiency  of  the  transport  sector  are  the  driver  behavior  and  optimal  routes/path 
planning.  Several  studies  have  highlighted  its  potential  for  saving  fuel  and  reducing 
emissions.  Literature  suggests  that  increase  of  drivers’  awareness  and  moving  towards 
more  eco-friendly  driving  styles  can  have  an  effect  of  5%  to  more  than  35%  of  fuel 
economy,  depending  on  the  trip  [3],  while  the  selection  of  fuel  efficient  trips  can 
decrease fuel consumption and thus emissions, by up to 10% on average. Tavares et al. 
[4] have performed a case study in the capital of Cape Verde, considering both the road 
slope and fuel consumption in selecting a suitable cost function when optimizing vehicle 
routing,  achieving  cost  savings  of  8%  as  compared  with  the  selection  of  the  shortest 
possible  travel  distance.  BOSCH’s  efforts  in  implementing  the  ECO2  satnav  software 
suggested  reductions  of  fuel  consumption  of  up  to  9%,  while  increasing  the  average 
journey time by 9% [5]. 

Even  if  several  software  incorporate  the  capability  of  taking  fuel  consumption  into 
account when specifying the best route, the intrinsic complexity of routing problems and 
the relevant requirements in computational power and speed, have led to simplifications 
in  the  road  network  geometry  (e.g.,  elevation,  filtering  by  road  type,  etc.)  and  the  fuel 
consumption model, significantly affecting the end accuracy and thus the value of those 
solutions.  Some  solutions  like  e-distance.com  [6]  don’t  calculate  fuel  consumption  but 
rather ask the user to provide an average value as an input, which is further used as the 
main indicator for calculating the fuel consumption over the various trips. ViaMichelin [7] 
provides the most fuel efficient option, however it has several limitations on the way fuel 
consumption  is  calculated,  i.e.  the  street  slope  is  not  accurately  taken  into  account  and 
many  car  and  trip  related  parameters,  are  missing.  Mappy  [8],  similar  to  ViaMichelin, 
does  not  consider  the  road  slope  and  has  a  limited  options  selection  for  vehicle  related 
parameters. Additionally, the fuel efficient routes plotted by existing satnav systems, are 
calculated according to the speed limits of particular roads and the number and type of 
intersections  along  the  journey.  However,  engine  performance  and  efficiency  are  also 
influenced  by  mass,  transmission,  tire  type,  gear-shifting  strategy,  driver-style,  traffic 
condition  (e.g.,  velocity  reduction  or  start  stop),  fuel  saving  technologies,  traffic  lights, 
etc.  Those  lead  to  a  variation  of  the  engine  power  demand  and,  thus,  a  different  fuel 
consumption and resulting emissions.  

1.2  The Approach  

Based on the previous, it would be advantageous to model correctly vehicle, driver, and 
traffic  condition  when  determining  the  fuel  consumption.  Such  an  approach  would 
provide  the  system  an  additional  degree  of  freedom,  which  would  generate  a  more 
realistic  cost  function  that  could  take  into  account  both  the  fuel  consumption  and  the 
associated  emissions.  The  problem  is  that  the  development  of  this  detailed  fuel 
consumption  map  is  expensive  and  complicated,  because  physical  tests  have  to  be 
carried out. 

The  present  proof-of-concept  aimed  at  designing  and  implementing  a  tool  capable  of 
using  all  vehicle,  driver,  and  traffic  related  data  to  calibrate  an  advanced  vehicle  model 
and,  thus,  to  accurately  predict  vehicle  fuel  consumption,  without  performing  extensive 
test  campaigns.  The  calibrated  vehicle  model  would  allow  the  determination  of  the  fuel 
consumption  under  different  driving  and  road  conditions.  When  going  from  A  to  B,  the 
algorithm  would  calculate  the  fuel  consumption  of  each  individual  route’s  sub-segment, 

4 

and  thus,  the  optimal  route  would  be  defined  as  the  one  with  the  minimum  total  fuel 
consumption,  not  necessarily  corresponding  to  the  shortest  travelled  distance.  Indeed, 
depending  on  the  road  geometry  and  status  (e.g.  slopes,  traffic  lights,  and  traffic 
conditions), the selected vehicle, and the driver style, it is possible for a longer route to 
become optimal in terms of fuel consumption. 

A  quick  screening  among  existing  solutions  (Table  1)  demonstrated  that  potential  key 
advantages of this solution would be: (1) the use of an advanced fuel consumption model 
without the need to perform physical tests; (2) the possibility to model any kind of light 
duty vehicle; (3) the possibility to use real data to optimize a specific vehicle model and 
driver;  (4)  the  possibility  to  consider  the  road  slope  and  geometric  features;  (5)  no 
limitations  due  to  the  road  type;  (6)  traffic  is  considered  in  order  to  evaluate  the  fuel 
consumption;  and  (7)  the  adoption  of  a  well  proven  and  validated  advanced  vehicle 
model (Green driving powered by CO2MPAS).  

Table 1. Initial Competitors Mapping. 

In  order  to  evaluate  the  potential  capabilities  of  the  tool,  and  using  the  Green  Driving 
tool as a reference, a case study (Table 2) demonstrated that when comparing the real 
consumption  of  a  Fiat  500X  on  the  route  Milano-Ispra-Milano  –  measured  with  the 
onboard  system  Uconnect  [9]  –  and  those  predicted  by  the  Green  Driving  tool  and 
ViaMichelin, an overall error of 0.54% is achieved with the first, as compared to 29.3% of 
the  second.  Moreover,  the  Green  driving  tool  captures  the  effect  of  the  road  slope, 
predicting  higher  fuel  consumption  in  the  trip  Milano-Ispra  (positive  avg.  slope)  in 
respect to the return trip. Both the previous provided a promising basis which supported 
a further in-depth analysis of the problem and the definition of a complete solution, as it 
is further explained in the rest of the text.  

Table 2. Case Study. 

5 

 
 
 
 
  
2  Methodology 

U-SAVE  is  designed  to  use  all  vehicle,  driver,  and  road  data  to  calibrate  an  advanced 
vehicle model (i.e., CO2MPAS physical model [10]) which is then used to predict the most 
fuel  efficient  path,  the  vehicle  fuel  consumption,  and  the  optimized  velocity  profile.  For 
simplicity and for computational efficiency reasons, the problem can be split in four sub-
tasks that are explained in the next four sections: (1.) Find the most fuel-efficient path, 
(2.) Calibration of fuel consumption raster, (3.) Calculate the fuel consumption on a route 
for a specific vehicle/driver, and (4.) Optimization of the velocity profile. 

2.1  Find the most fuel-efficient path  

The  most  fuel-efficient  path  from  an  origin  to  a  destination  is  defined  as  the  route  with 
the  minimum  total  fuel  consumption,  which  does  not  necessarily  correspond  to  the 
shortest  travelled  distance.  Indeed,  depending  on  the  road  geometry  and  limits  (e.g., 
slopes, speed limits, and turns), the selected vehicle, and the driver style, it is possible 
for a longer route to become optimal in terms of fuel consumption.  

This problem is also sometimes called the single-pair shortest path problem. To solve it, 
the  Open  Source  Routing  Machine  (OSRM)  [11]  has  been  chosen.  It  is  an  open-source 
high-performance  routing  HTTP  server  designed  to  be  used  with  data  from  the 
OpenStreetMap  (OSM)  project  [12]  and  customizable  edge  weights.  OSRM  takes  into 
account  turn  restrictions  and  other  “costs”  like  waiting  at  traffic  lights,  braking  and 
accelerating  at  sharp  turns.  To  compute  the  shortest  path  it  uses  a  multi-level  Dijkstra 
algorithm [13]. 

Figure  2  shows  how  to  setup  a  U-SAVE  routing  HTTP  server  for  finding  the  most  fuel 
efficient  route.  OSRM  converts  OSM  data  to  an  edge-expanded  graph.  It  extracts  some 
useful  information  like:  average  road  velocities,  road  geometry  coordinates,  and  turn 
angles.  The  elevations  and  edges  slopes  are  computed  using  the  Shuttle  Radar 
Topography Mission data [14].  

Figure 2. Flowchart: Start U-SAVE Routing HTTP Server for Finding the Most Fuel Efficient Route. 

6 

 
 
The  multi-level  Dijkstra  graph  –  i.e.,  the  routing  map  –  is  compiled  by  OSRM  using  as 
edge  and  turn  weights  the  fuel  consumption  predicted  by  a  light  computational  model. 
This  is  composed  by  two  raster  functions  of  velocity,  slope,  and  wheel  power.  Edge 
weights  are  computed  with  the  slope-velocity  raster,  while  turn  weights  are  computed 
with the power-velocity raster. After the compilation of the MLD graph, U-SAVE initializes 
a  standard  OSRM  routing  HTTP  server  that  will  wait  for  requests.  Next  section  explains 
how to calibrate the fuel consumption raster for the map compilation. 

2.2  Calibration of fuel consumption raster 

The raster is a light computational model that is extracted from a cloud of data, acquired 
from physical tests or simulated with advanced analytical models. The size of the raster 
is generally defined by the application domain, in this case we need data points sampled 
when the engine reached the thermostat temperature (i.e., hot condition) with ±20% of 
slope  and  a  velocity  range  of  0-130  km/h.  The  WLTP  test  cannot  satisfy  the  application 
requirements.  Hence,  we  are  using  a  more  advanced  model,  i.e.,  CO2MPAS  physical 
model,  to  simulate  four  WLTC  cycles  with  variable  slope  in  hot  condition.  The  CO2MPAS 
physical model is calibrated using a full CO2MPAS input file. The data are sampled with a 
moving average window of 60 seconds. The figure below shows an indicative example of 
calibrated fuel consumption raster. 

Figure 3. Fuel Consumption Raster Function of: (left) Velocity and Slope, (right) Velocity and 
Power. 

2.3  Calculate the fuel consumption on a route 

The fuel consumption calculation starts when a user queries the U-SAVE server with the 
vehicle  data  and  a  route  request  (i.e.  origin  and  destination).  Figure  4  shows  how  U-
SAVE handles a user query and computes the fuel consumption over a route. 

The route request is forwarded to the routing HTTP server that replies with the most fuel-
efficient  path.  From  this,  U-SAVE  determines  elevation  and  slope  from  the  SRTM  data. 
Rasters  are  calibrated  on  the  fly,  from  the  vehicle  data  and  the  Green  Driving  meta-
model.  This  is  a  multivariate-kriging  model  that  has  been  calibrated  using  the  EU  light-
duty  vehicles  fleet  data  processed  by  CO2MPAS,  simulating  the  WLTC  cycle.  The  two 
rasters  are  then  used  to  compute  the  fuel  consumption  of  each  individual  route’s  sub-
segment  and  to  calculate  the  velocity  suggestions.  Then,  all  results  are  added  to  the 
standard  OSRM  response  and  given  to  the  user  that  is  awaiting  the  response.  Next 
section  explains  how  to  compute  the  velocity  suggestions  by  optimizing  the  velocity 
profile. 

7 

     
 
 
Figure 4. Flowchart: Handle a User Query and Compute the Fuel Consumption over a Route. 

2.4  Optimization of the velocity profile 

The  scope  of  the  velocity  suggestions  is  to  reduce  the  fuel  consumption.  Hence,  the 
velocity of each individual route’s sub-segment is modified within some velocity margins. 
The margins for modifying the velocity profile – i.e. the upper and lower velocity bound – 
are function of the velocity and are shown in Figure 5.  

Figure 5. Speed Margins for the Velocity Suggestions. 

These margins are needed for ensuring a feasible velocity – for example avoid suggesting 
a speed of 50 km/h on a road where the average velocity is 100 km/h – or do not exceed 
the  speed  limits.  However,  this  is  not  sufficient,  because  the  brute  optimization  of  the 

8 

 
 
 
 
fuel consumption can lead to unacceptable variation of the total trip duration. Therefore, 
the  optimization  of  the  velocity  profile  consists  in  minimizing  the  trip  fuel  consumption 
respecting some duration constrains (e.g., to not exceed a threshold). The velocity Vi of 
each ith segment is defined as follows: 

(cid:1848)(cid:3036) (cid:3404) (cid:1848)(cid:3013),(cid:3036) (cid:3397) (cid:2012)(cid:3036) (cid:3400) (cid:3435)(cid:1848)(cid:3009),(cid:3036) (cid:3398) (cid:1848)(cid:3013),(cid:3036)(cid:3439) 
where:  VL,i  and  VH,i  are,  respectively,  the  lower  and  the  upper  velocity  limit  of  the  ith 
segment; and i are the multiplication factors [0, 1] to be optimized. 

From  the  derivative  of  fuel  consumption  with  respect  to  the  velocity  margins,  we  have 
identified three sub-groups of road segments (see Figure 6). These are classified by the 
sign of the derivative and they have the following characteristics:  

1.  Negative: increasing the average velocity, we reduce trip fuel and duration;  

2.  Almost zero: the velocity is not affecting the fuel consumption; 

3.  Positive: reducing the average velocity, we have less fuel consumption but higher 

duration. 

Figure 6. Road segments sub-groups. 

By grouping the segments in these three sub-groups, the velocity Vi of each ith segment 
can be rewritten as follows: 

(cid:1848)(cid:3036) (cid:3404) (cid:1848)(cid:3013),(cid:3036) (cid:3397) (cid:2016)(cid:3037) (cid:3400) (cid:3435)(cid:1848)(cid:3009),(cid:3036) (cid:3398) (cid:1848)(cid:3013),(cid:3036)(cid:3439) 

where: j is the multiplication factor of the jth sub-group. The multiplication factor of the 
first sub-group is set to 1, because increasing the average velocity, we minimise trip fuel 
and  duration.  Hence,  we  have  simplified  the  problem  to  two  unknowns  and  thus 
improved the optimization performances. 

9 

 
 
3  Implementation 

3.1  U-SAVE Desktop Version 

U-SAVE desktop version was created using HTML5 [15], CSS3 [16], JavaScript [17] and 
uses the following external JavaScript libraries: Bootstrap [18], JQuery [19] and Leaflet 
[20]. 

The  user  interface  (UI),  built  based  on  the  Leaflet  library,  shows  and  manages  user 
changes  in  the  vehicle  data  (car  segment,  fuel  type,  fuel  price,  electricity  price,  engine 
capacity, engine power, car weight, gearbox, EURO standard, traction, Start/Stop, BERS, 
driving  style,  tyres  class,  number  of  passengers,  internal  luggage,  air  conditioning, 
roofbox)  and  the  route  data  (start  location  and  destination  plus  all  intermediate 
waypoints, if available).  U-SAVE uses a custom built OSRM server to provide the routing 
and then, the Green Driving Tool to calculate fuel consumption.  

The information regarding the routing (start location and destination plus all intermediate 
waypoints, if available) are sent by the UI to the OSRM server, that provides a response 
in JSON format. The response contains all information used by the UI to draw the routes 
on the map and provides also route duration, distance, directions and elevations.  

After having received the results from the OSRM server, the UI sends the route data and 
the  vehicle  data  to  the  Green  Driving  Tool  that  calculates  the  fuel  consumption  of  the 
submitted  vehicle  on  the  submitted  route.  The  request  to  the  Green  Driving  Tool  is 
managed by a PHP file that queries a MySQL database [21]. The database contains a set 
of raster data extrapolated by CO2MPAS and through a set of kriging operations the fuel 
consumption and CO2 emission is sent to U-SAVE UI. 

The  OSRM  server  uses  the  tiles  (images  of  the  shown  map)  and  the  geocoding 
(translation  of  location  to  geographic  coordinates)  of  Mapbox  [22],  a  provider  of  map 
services.  

U-SAVE  consists  mainly  of  3  different  elements/modules,  as  demonstrated  in  Figure  5, 
which  provides  the  workflow  of  the  U-SAVE  module  calculating  fuel  consumption  and 
route from the data received from the OSRM server and the UI. 

Figure 7. U-SAVE Structure. 

10 

 
3.1.1  OSRM Server 

The OSRM server [11] will receive HTTP JSON requests from the U-SAVE module and will 
reply  through  the  same  HTTP  JSON.  To  install  and  update  the  OSRM  server  a  C++ 
compiler  (gcc  4.9.2  or  higher)  must  be  installed  on  the  machine  together  with  other 
software.  The  OSRM  installation  is  enclosed  in  a  Docker  [23]  that  facilitates  the 
installation and updating. 

3.1.2  U-SAVE Core Module 

The  U-SAVE  module  (built  in  Python  3.6)  manages  the  requests  from  the  UI, 
communicates  with  the  OSRM  server  and  elaborates  the  data  using  the  integrated 
CO2MPASS software. All requests from and to the U-SAVE module are made in JSON on 
HTTP protocol.  

3.1.3  Web Interface 

The  UI  will  be  publicly  accessible  (world  wide  web)  and  will  communicate  with  the  U-
SAVE  module  through  HTTP  JSON  requests.  The  public  version  of  the  U-SAVE  Desktop 
version is available here: https://usave.1kb.it:8443/ (accessed 18 December 2017). Log 
in using the following username: testing and password: testing. A prototype homepage 
is also present, set up to explain the functionality of the Desktop version.  

3.2  U-SAVE Navigation Application 

The  U-SAVE  Application  is  an  online  navigator  system  that  provides  route  alternatives 
(fastest, shortest, and most fuel efficient) and in addition to them velocity suggestions to 
optimize the fuel consumption over the trip.  

The App will be available for 2 operating systems, Android and iOs and will be available in 
multiple languages: English, German, French and Italian to begin. 

The  App  has  to  collect  and  send  to  a  the  U-SAVE  predefined  server  the  user  provided 
information:  vehicle  data,  route  information  (start  and  destination  and  intermediate 
waypoints, if available), and other minor elements. The navigation system will be based 
on  to  the  native  Sdk  of  Mapbox,  to  maintain  full  compatibility  with  the  U-SAVE  server 
that is responding  using the Mapbox and OSRM [11] standards. The App will follow the 
EC design rules [24,25]. 

The application will have five main blocks: (a.) user authentication & vehicle inputs, (b.) 
route  selection,  (c.)  navigation  system,  (d.)  interface  with  OBD-II,  and  (e.)  data 
collection. 

3.2.1  User Authentication & Vehicle Inputs 

The user shall be able to login to the App using accounts like Facebook, Google+, Twitter, 
to  maintain  the  portability  of  the  vehicle  settings  between  devices.  The  App  shall  store 
locally  and  remotely  the  vehicle  parameters  and  the  user  will  be  able  to  check,  modify 
and  eventually  delete  some  or  all  data.  These  data  are  used  to  customize  the  fuel 
consumption output. 

Figure 8. U-SAVE Mobile Application Inputs. 

11 

 
 
 
The user shall be in the position to share his results in CO2 Emissions, Fuel Consumption 
or  fuel  price  with  social  networks  and  compare  the  results  of  other  users  with  his/her 
own. He shall also be able to see his own results from previous trips. 

3.2.2  Route Selection 

The  map  will  show  and  allow  the  user  to  select  one  of  the  three  route  alternatives 
calculated between the current location and destination, including the various waypoints, 
if available. 

Figure 9. Route Selection. 

The  routes  are  printed  on  an  interactive  map,  which  can  be  modified  from  the  user  by 
dragging the route paths or waypoints. 

Figure 10. Routes Visualization. 

All  calculations,  routing,  velocity  suggestion  and  fuel  consumption,  will  be  done  by  the 
server  that  will  receive  and  reply  to  the  inputs  in  HTTP  JSON  format.  The  server,  using 
the  Green  Driving  Tool  and  the  OSRM  service,  will  provide  to  the  App  the  route 
parameters and the fuel consumption, exactly like in the Desktop version. This uniformity 
in  communications  standards  between  the  Desktop  version  and  the  App  is  important  to 
facilitate upgrades and updates of the server.  

For  each  route  the  following  parameters  have  to  be  shown:  distance,  duration, 
fuel/energy  consumption,  and  CO2  emission.  It  will  also  show  the  list  of  directions  to 
follow. Hence, the users can choose their route. 

12 

 
 
 
 
  
Figure 11. Selected Route Details. 

3.2.3  Navigation System 

The Navigation System shall guide the users to reach their destination from their actual 
location, providing turn by turn directions and a suggested vehicle speed value (see 44 in 
the figure below). Indications and suggestions have to be provided with visual and most 
importantly with audio/spoken advices. A safety advice will be shown at the beginning of 
the navigation process to remember the user always to follow road rules and legislations. 

Figure 12. Navigation System. 

3.2.4  OBD-II Interface 

It  will  be  investigated  the  option  for  the  App  to  be  in  the  position  to  communicate  with 
On  Board  Diagnostics  system  (OBD)  according  to  the  current  European  standard  EOBD 
(SAE J1979) [26] using wireless bluetooth. 

3.2.5  Data Collection 

The  App  shall  be  in  the  position  to  communicate  information,  like  position  at  each 
second, time and route selected to the server database. The data will be collected from 
the  smartphone  and  from  an  OBD  reader  if  available.  The  App  will  have  the  option  to 
connect to an OBD II device via bluetooth and will be able to read selected vehicle data 
while  travelling  and  save  them  on  the  device.  The  same  data  will  be  uploaded  to  the 
server when a Wi-Fi connection is available. 

All user data are going to be processed following the EU Regulations [27]. 

13 

 
 
  
 
 
 
 
4  Validation 

4.1  Analytical Validation 

The  scope  is  to  compare  the  performances,  in  terms  of  fuel  saving  and  cost,  of  four 
different route options, with and without velocity suggestions. The chosen routing options 
are: the fastest, the shortest, the most fuel efficient (i.e. eco), and the most economical 
(i.e.  cost).  Each  option  optimizes  a  different  metric,  which  is  respectively:  duration, 
distance,  fuel  consumption,  and  cost.  The  latter  is  calculated  considering  driver,  vehicle 
maintenance, tyre wear, and fuel costs. The table below shows the multiplication factors 
used in the simulation to compute the route cost. 

Table 3. Cost’s Multiplication Factors used in the Simulation. 

Metric 

Value  Description 

Fuel [€/L] 

1.45 

Diesel cost. 

Duration [€/h] 

16 

Driver cost. 

Distance [€/km]  0.08992  Maintenance and tyres. 

The  routing  machines  are  compiled  changing  edge  and  turn  weights  in  the  multi-level 
Dijkstra graph, according to the route metric.  

Routings are performed over a sample of 2,000 random pairs of origin/destination points, 
selected inside the Lombardy region. The routing results without velocity suggestions are 
shown in the table below.  

Table 4. Routing Results without Velocity Suggestions. 

Without Velocity Suggestions 

Routing optimization 

Fastest  Shortest 

Eco 

Economical 

Duration [min] 

164 

214 

214 

Distance [km] 

180 

163 

165 

170 

170 

Fuel [L] 

8.65 

7.92 

7.87 

8.12 

Cost [€] 

72.4 

83.3 

83.4 

72.3 

Fuel Consumption 
[L/100km] 

4.8 

4.9 

4.8 

4.8 

Average Velocity [km/h] 

66 

46 

46 

60 

Fuel Saving [-] 

Cost Saving [-] 

- 

- 

8.4% 

9.1% 

6.1% 

-15.0% 

-15.2% 

0.1% 

Without velocity suggestions, the shortest and the most fuel efficient routes are the best 
in fuel consumption, however they are the most expensive. In comparison to the fastest 

14 

 
 
route, they have a reduction of 8.4% and 9.1% in fuel consumption and an increase of 
15.0% and 15.2% in trip cost. This increase is driven by the driver cost and the higher 
trip  duration. Consequentially,  the  most  economical  route  has  a  low  duration,  similar  to 
the  fastest  one.  It  has  an  overall  saving  of  0.1%  in  trip  cost.  However,  it  has  a  more 
ecological  impact,  with  fuel  consumption  being  6.1%  lower  than  the  fastest  route. 
Moreover,  it  has  a  shorter  travelled  distance  that  reduces  maintenance  and  tyres 
expenses.  Despite  the  different  average  velocities,  all  routes  have  approximately  the 
same  fuel  consumption  ratio  of  4.8-4.9  L/100km,  because  the  calculation  of  the  fuel 
consumption considers also the road slope.  

Table 5 shows the routing results with velocity suggestions. This leads to a modification 
of  the  base  velocity  profile  and  the  travelled  time.  Higher  is  the  velocity,  larger  are  the 
margins,  and  therefore  higher  could  be  the  fuel  saving.  Indeed,  the  fastest  route  with 
suggestions has the highest delta of 7.5% in fuel saving. However, the shortest and the 
most economical routes have the best fuel consumption. 

Table 5. Routing Results with Velocity Suggestions. 

With Velocity Suggestions 

Routing optimization 

Fastest Shortest

Eco 

Economical 

Duration [min] 

166 

197 

195 

Distance [km] 

180 

163 

165 

166 

170 

Fuel [L] 

Cost [€] 

8.00 

7.69 

7.75 

7.69 

72.0 

78.4 

78.0 

70.6 

Fuel Consumption [L/100km] 

Average Velocity [km/h] 

4.5 

65 

4.7 

50 

4.7 

51 

4.5 

62 

Fuel Saving [-] 

7.5% 

11.1% 

10.4%

11.1% 

Cost Saving [-] 

0.6% 

-8.3% 

-7.8% 

2.5% 

Unexpectedly, the most fuel-efficient route – with a fuel consumption of 7.75L – does not 
have the highest fuel saving. This can be explained by the fact that each route has been 
selected  to  minimize  the  fuel  consumption.  Thus,  the  selected  path  corresponds  to  the 
minimum of the fuel consumption function, so a small variation of the velocity profile has 
a lower effect. 

Despite  the  better  fuel  consumption  and  a  lower  travelled  time,  the  shortest  and  the 
most fuel-efficient routes with suggestions are still the most expensive routes. They have 
a  trip  cost  8.3%  and  7.8%  higher  respect  to  the  fastest  route  without  velocity 
suggestions.  In  the  future,  with  autonomous  vehicles,  the  driver  expenses  will  be 
reduced. Thus, the shortest and eco routings will become the cheapest options, but they 
will not be a viable product for the market, because the travel time is too high.  

With  velocity  suggestions,  the  fastest  route  is  now  slower,  while  the  other  routes  are 
faster  respect  to  those  without  suggestions.  In  particular,  the  fastest  and  most  fuel 
efficiency  routes  have  the  same  travelled  time  of  166  min. The  higher  travelled  time  of 
the  fastest  route  leads  to  a  small  improvement  of  0.6%  in  trip  cost.  While,  the  most 
economical  route  with  suggestions  has  the  lowest  cost  –  that  correspond  to  2.5%  less 

15 

 
trip  cost  –  and  the  lowest  environmental  impact  –  i.e.,  a  fuel  consumption  of  7.69L. 
Hence, this is the best routing option to be delivered to customers.   

4.2  Real Test Cases 

Once the analytical validation of the tool has been completed, a real test cases campaign 
has  been  designed.  The  aim  of  this  validation  step  was  to  validate  the  tool  as  a  whole 
regarding  both  its  usability,  i.e.  users’  experience,  and  the  provided  results,  i.e.  route 
suggestions and estimates of fuel  consumption, trip duration, etc. versus reality. At the 
same time, sharing the tool with third parties would provide a solid and expanded basis 
of real world datasets that could be used to further calibrate the tool and test additional 
features. 

In order to perform this step, and not having concluded the implementation of a mobile 
application, a two-steps approach has been decided: 

1.  As a first step, a mobile extension of the desktop version has been utilized, mainly 
focusing on obtaining feedback regarding the overall applicability of the idea, the 
quality  of  the  information  provided,  i.e.  realistic  route  suggestions,  applicable 
velocity  suggestions,  etc.,  while  in  parallel,  this  first  step  would  allow  gathering 
data for a further quantitative evaluation of the tool. 

2.  As  a  second  step,  and  at  a  later  stage,  the  mobile  application  should  be  used, 
mainly  to  validate  the  overall  user  experience  and  allow  additional  optimizations 
and refinements of the tool before its full scale deployment. 

A fully detailed test protocol / test guide has been designed and circulated internally for 
feedback before being forwarded to external parties. The complete test folder is provided 
in the Annex, Real Test Cases Material. 

The material has been initially shared with the University of Belgrade under the context 
of JRC’s collaboration with the Faculty of Transport and Traffic Engineering. Students are 
asked to use the tool and report back the results and their feedback. Once the first round 
of  real  tests  is  complete  and  the  test  protocol  validated,  a  more  expanded  real  tests 
campaign  will  be  performed,  with  the  participation  of  third  parties  from  different  places 
around Europe, different users’ profiles, driving patters, environmental conditions, etc.  

It shall be highlighted that this process is currently on-going, thus no concrete results are 
yet available. 

16 

 
5  Exploitation 

After  reviewing  all  the  previous  and  having  confirmed  the  capability  of  the  tool  to  both 
(a.) accurately estimate the fuel and energy consumption of a vehicle over an individual 
trip,  and  (b.)  predict  the  most  fuel  efficient  route  option  for  a  specific  pair  of  start  and 
end points, a full in-depth market research and viability assessment has been performed 
to  analyse  the  potential  of  the  approach  as  the  basis  for  a  viable  and  scalable  business 
venture.  

The  viability  assessment  has  been  performed  targeting  mainly  the  following  three  key 
areas: 

1.  Competition  Analysis:  An  in-depth  competition  analysis  has  been  performed  to 
analyse  alternative  approaches  of  calculating  fuel  and/or  energy  consumption  of 
light-duty  vehicles  over  specific  mission  profiles  (i.e.  velocity  and  acceleration 
profile, vehicle load, road slope, etc.); 

2.  Market Analysis: Several alternative markets have been brainstormed and initially 
evaluated  as  of  their  attractiveness  for  an  initial  market  entry  and  potential 
expansion.  The  Navigation  Software  Providers  was  selected  as  the  first  target 
market and constituted the main focus of this analysis; 

3.  Business  &  Financial  Model:  Several  potential  business  models  have  been 
analysed  as  of  their  applicability  and  potential  financial  returns  to  the  selected 
target market segment. 

5.1  Competition Analysis 

Several  approaches  exist  and  are  currently  used  for  estimating  fuel  consumption  of  a 
specific vehicle over a specific mission profile. Those can be roughly divided in two main 
categories: (a.) emission factors-based models, and (b.) fully detailed vehicle simulation 
models. 

Definition of Competitive Approaches 

—  Emission Factors-based Models 

Emission factors are empirical functional relations that predict the quantity of a pollutant 
that  is  emitted  per  distance  driven,  energy  consumed  as  a  function  of  vehicle  activity 
parameters,  e.g.  average  velocity  or  traffic  situation.  Those  constitute  the  basis  for  the 
emission models which can be directly used to estimate fuel and/or energy consumption 
of a vehicle for a constant, i.e. average, velocity profile, etc. Some correction factors are 
usually applied to take into account the effect of specific factors such as cold start, new 
technologies, age of vehicles and so on. 

—  Vehicle Simulation Models 

Vehicle simulation models are longitudinal vehicle dynamics simulation models that deal 
with  the  explicit  simulation  of  the  behaviour  of  a  vehicle  and  its  technological 
components  over  a  well-defined  mission  profile.  Very  detailed  information  is  usually 
required  as  these  tools  have  been  developed  with  the  intention  to  support  the  entire 
vehicle design phase. 

Both  approaches  have  advantages  and  disadvantages  which  can  be  summarized  in  two 
main  axes,  simplicity  of  usage  (mainly  in  terms  of  required  input  parameters)  and 
accuracy of the end result, as depicted in Table 6 below. 

17 

 
Table 6. Competitive Approaches. 

5.2  Market Analysis 

The  focus  of  the  market  analysis  has  been  on  identifying  potential  market  segments 
where the use of the tool and the choice of a more fuel efficient route option could be of 
high  added  value,  prioritize  them  in  terms  of  perceived  value,  entry  barriers, 
competition,  and  market  size,  and,  lastly,  based  on  the  previous,  select  the  most 
attractive market segment which could operate as the main “beachhead” market.  

Among  others,  the  following  potential  market  segments  have  been  brainstormed  and 
analysed: fleet management companies, transportation management solution providers, 
vehicle manufacturers, maps providers, navigation software providers, etc. The last, i.e. 
navigation  software  providers,  has  been  selected  mainly  due  to  the  direct  benefits  of 
implementing such a service to their products (e.g. potential competitive advantages that 
such a feature could provide), the “simplicity” of implementing a business model for this 
specific  market  segment  (more  details  can  be  found  in  the  next  paragraph),  and  the 
potential  size  both  in  terms  of  financial  returns  and  environmental  impact  of  the  tool’s 
application in such a market. 

An in-depth market research has been performed on the top players of such a market, a 
snapshot of which is provided in Table 7. More details can be found in the Annex, U-SAVE 
Business Plan. 

5.3  Business & Financial Model Analysis 

5.3.1  Business Model  

The  business  model  consists  of  selling  access  to  the  tool’s  services  via  dedicated  APIs 
charged per call or per package, while the main cost is linked with the use of the server 
that hosts the software back-end. It is assumed that dedicated APIs will be accessible to 
the  main  customers  who  will  be  able  to  call  the  server  with  specific  inputs  and  get  a 
specific response that can be then directly utilized and incorporated to their services.   

5.3.1.1  Revenue Model 

The revenue model is structured around a credits plan which provides credits to the users 
–  equivalent  to  API  calls  –  for  a  monthly  fee.  Up  to  500  credits  per  day  the  service  is 
offered  for  free,  while  the  monthly  charge  increases  as  the  required  credits  per  day 
increase.  The  same  pricing  model  employed  by  Graphhopper  [28]  is  applied,  due  to  its 
similarities with U-SAVE’s business model and value proposition.  

18 

 
 
 
Table 7. Feature Comparison of Top Navigation Software Providers. 

Feature comparison of commercial GPS software

Name of Application

Maps Source

Operating Platform Software license

Cost

Maps can be preloaded 
(and stored)

Apple Maps

TomTom, others

iOS

BlackBerry Maps

Unknown source

BlackBerry 10

Non‐free 
proprietary
Varies per device

Free

Free

No

No

Google Maps

Zenrin, AutoNavi, Tele 
Atlas, Google Map 
Maker

Android, iOS, 
Windows Phone

Non‐free 
proprietary

Free

Yes (expire after 30 days)

GraphHopper

OpenStreetMap

Android, iOS, 
Raspberry Pi

Apache 2.0

Free, Paid 
subscription 
fee for hosted 

Here WeGo

HERE Maps B.V.

Android, iOS, 
Windows Phone

Varies per device

Free

Top‐Map, Tele Atlas, 
Navteq

Android, iOS, 
Windows Mobile

Varies per device

Android, iOS

Non‐free 

Android

Non‐free 
proprietary

Android

Non‐free 
proprietary

Free

Free

Free

Paid

iGO

Karta GPS

Locus Map Free

Locus Map Pro

MapFactor

OpenStreetMap
OpenStreetMap, 
Swisstopo, IGN, 
Outdooractive, 
Freytag&Berndt, 
SHOcart, others
OpenStreetMap, 
Swisstopo, IGN, 
Outdooractive, 
Freytag&Berndt, 
SHOcart, others
OpenStreetMap, 
TomTom

Android, Windows, 
Windows Phone

Non‐free 
proprietary

Freemium

Maps.me

OpenStreetMap

Android, iOS, 
BlackBerry

Apache 2.0 (except 
some 3rd party libs 
and resources)

Moovit

OpenStreetMap

Android, iOS

Non‐free 
proprietary

Free

Free

Yes

Yes

In‐App purchase

Yes

Yes

Yes

Yes

Yes

No

3D navigation mode Voice‐guidance Live Traffic

Speed Traps

Speed Limits Other features and remarks

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

No

No

No

No

No

No

CarPlay

‐

Lane guidance, Android Auto

No

Yes

Yes

Vehicle tracking

Yes

No

Large roads 
only

Indoor Maps, Lane Guidance

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

No

No

No

Green Routing, Lane 
Guidance

Lane Guidance, 

Focused on outdoor 
navigation ‐ hiking, biking, 
geocaching

Focused on outdoor 
navigation ‐ hiking, biking, 
geocaching

In‐App 
purchase

Yes

Yes

No

Yes

No

Lane Guidance

‐

No

No

No

No

Focused on pedestrians and 
public transportation

Navigon

Navteq

Android, iOS, 
Windows Phone

Varies per device

Paid

Yes

Yes

Yes

In‐App 
purchase

Yes

Yes

Lane Guidance
Apple Watch
Pedestrian Navigation
Drive & ETA sharing

Navmii

OpenStreetMap

Android, iOS

OsmAnd

OpenStreetMap

Android, iOS

Non‐free 
proprietary

GNU GPLv2 
(except some 3rd 
party libs and 
resources)

Freemium

Yes

Yes

In‐App purchase

In‐App 
purchase

In‐App 
purchase

Yes

‐

Free

Yes

No

Yes

No

No

Yes

Lane guidance

Ovi Maps

Nokia / Navteq

Symbian OS S60, 
Maemo

Varies per device

Sygic: GPS navigation

TomTom, Navteq, 
HERE Maps, others

Android, 
iOS,Windows Phone

Non‐free 
proprietary

TomTom

TomTom

Android, iOS

Non‐free 
proprietary

Free: Last 
Nokia & 
Navigator 
phones
Paid: Other 
phones

Freemium

Free for 
75km/month
Paid 
subscription

Yes

Yes

Yes

Waze

open basemaps
drives of the users
Community additions

Android, iOS, 
Windows Phone

Non‐free 
proprietary

Free

Limited control over 
stored data
can be used for off‐line 
navigation

Windows Maps

HERE Maps B.V.

Windows 10 Mobile, 
Windows 10, Xbox 
One Microsoft 

Non‐free 
proprietary

Free

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Pedestrian Navigation

In‐App purchase

In‐App 
purchase

In‐App 
purchase

In‐App 
purchase

MirrorLink (Paid Version)
Lane Guidance

Yes

Paid 
subscription

Paid 
subscription

Yes

Lane Guidance

Yes

Yes

Yes

Yes

Gas‐stations & prices
Drive & ETA sharing
Report hazards
Temporary Closures 
(roadwork/events/...)

Yes

Yes

No

Yes

Cortana integration

5.3.1.2  Server Cost 

The server cost is based on Aruba’s Private Cloud services [29] which charges a monthly 
fee as a function of the peak server usage per second (this option may introduce a non-
negligible  cost  initially  where  the  number  of  users  and  API  calls  is  expected  to  be  low, 
however, it guarantees a smooth transition towards higher demand and scalability).  

Figures 12 and 13 provide the yearly revenues and server costs and the ratio of revenues 
to  server  costs  as  a  function  of  the  daily  API  calls,  respectively.  It  is  assumed  that  on 
average,  one  user  uses  the  app  once  per  day,  making  4  API  calls  per  app  use  (three 
route  options,  plus  selection  and  navigation).  From  Figure  13,  it  can  be  concluded  that 
with  the  previous  assumptions  on  the  revenue  and  server  cost  models,  without 
the  plan  starts  becoming  viable 
considering  additional  operational  expenses, 

19 

 
 
 
(Revenue/Server  Cost  >  1)  with  approximately  more  than  36,000  API  calls  per  day, 
equivalent to approximately 9,000 users per day.  

Figure 13. Yearly Revenues & Server Costs as a Function of the Number of API Calls. 

Figure 14. Ratio of Revenues & Server Costs as a Function of the Number of API Calls. 

5.3.2  Financial Estimates  

Combining  all  the  previous  together  the  financial  estimates  are  calculated  as  shown  in 
Table  8  and  Figure  14.  Additional  assumptions  that  are  considered  in  the  financial  plan 
include: 

1.  The total “market size” / number of users is assumed to be equal to 10,000,000 
potential  users,  which  could  be  captured  either  directly  or  via  partnerships  with 
small  navigation  software  providers;  it  shall  be  highlighted  that  the  main 
navigation software providers target a far bigger market, i.e. TomTom claims that 
more  than  100  million  people  use  its  services,  Waze  has  more  than  65  million 
users, Here’s mobile app is used by more than 30 million users, while Google Apps 
is installed to more than 1 billion mobile phones alone. 

2.  Each user is assumed to use the app once per day (for 365 days per year), thus 

making 4 API calls on average per day. 

3.  The number of the peak API calls, which is crucial for the calculation of the server 
cost,  is  calculated  assuming  a  normal  distribution  of  the  calls  per  day  with  a 
standard deviation of 2 hours. 

4.  For  the  first  year,  one  engineer  is  working  part  time  on  the  project,  while  for 
future  projections  it  is  assumed  that  one  additional  engineer  is  needed  for  every 

20 

 
 
 
½  million  additional  API  calls  per  day,  with  an  average  cost  of  50,000  euro  per 
year. 

5.  For  simplicity  and  since  their  effect  is  considered  minor,  administrative  and 
additional  /  other  expenses  are  accounted  as  equal  to  3%  and  5%  of  the 
revenues, respectively. 

Table 8. Financial Planning. 

Figure 15. Financial Estimates & Expected Market Penetration. 

As  it  can  be  seen,  the  cash  flow  becomes  positive  during  the  second  year  of  operation, 
while  it  could  be  potentially  improved  considering  more  adequate,  i.e.  cheaper,  server 
alternatives for the first years of operations, when the server demand is expected to be 
low. A more in depth analysis of all the previous, along with some case studies and more 
explicit financial scenarios can be found in the Annex, U-SAVE business plan.  

21 

 
 
 
 
 
6  Conclusions 

The present report summarizes the work performed and the main results of the U-SAVE 
project, financed under the JRC Proof-of-Concept Instrument.  

The main target of the project was the design and implementation, proof of concept, and 
viability  assessment  of  a  tool  allowing  the  optimization  of  light-duty  vehicles  routing 
based on fuel consumption. The project builds on the results of previous work carried out 
by  the  Sustainable  Transport  Unit  of  the  Joint  Research  Center,  European  Commission, 
and  more  specifically  on  (a.)  the  CO2MPAS  tool,  the  official  tool  supporting  the  back-
translation  of  a  WLTP  measurement  to  its  equivalent  NEDC  value  during  the  type-
approval of passenger cars in Europe, and (b.) the Green Driving tool an interactive web-
based  tool  allowing  the  estimation  of  fuel  costs  and  CO2  emissions  of  individual  car 
journeys based on various vehicle, trip, and driver related data. An open-access version 
of the U-SAVE tool is planned to be merged with the existing Green Driving tool, adding 
the features of route selection (shortest, fastest, most fuel-efficient) and navigation, thus 
making the combined solution a comprehensive service offered by the Commission to EU 
citizens, regarding the use of their vehicles and their trips planning. 

The report is structured as follows: 

  Chapter  1  provides  a  short  introduction  regarding  the  background  and  the 

project’s overall context; 

  Chapter  2  presents  the  applied  methodology  for  developing  the  required  models 

for the tool’s implementation; 

  Chapter  3  describes  the  various  implementation  steps  for  both  the  desktop 

version and the navigation app; 

  Chapter 4 presents the validation methodology and some initial validation results 

of the tool; 

  Chapter  5  analyses  the  various  key  factors  affecting  the  tool’s  potential 
exploitation,  namely  the  competition,  the  market,  and  the  business  and  financial 
models. 

Regarding the tool’s validation, running the tool to a number of random pairs of start-end 
points  and  calculating  the  various  trips’  parameters  for  the  shortest,  fastest,  and  more 
fuel-efficient route options, it is demonstrated that a fuel saving of 9.1% can be achieved 
as compared to the fastest option. It is demonstrated that optimizing the driving style - 
and  more  specifically  the  velocity  profile,  by  slightly  adjusting  the  driven  speed  -  an 
additional  1-2%  of  fuel  economy  can  be  expected,  to  the  already  optimized  fuel 
consumption.  Additionally,  optimizing  the  route  and  the  driving  style  based  on  a  “cost 
function” accounting not only for the fuel consumption, but for the driver’s time cost and 
the  vehicle’s  wear  and  maintenance  cost,  demonstrated  fuel  savings  of  11.1%  and  cost 
savings  of  2.5%,  as  compared  with  the  initial  fastest  route  option.  Those  last  points  do 
indeed validate the actual concept behind the tool’s ideation and the potential impact of 
implementing such an approach at full-scale. 

Regarding  the  tool’s  exploitation,  the  competition  analysis  demonstrates  the  clear 
competitive  advantage  of  the  tool  as  compared  with  alternative  existing  approaches  for 
calculating  fuel  consumption  and  CO2  emissions,  and  is  based  around  the  speed, 
flexibility,  and  accuracy  of  the  tool.  The  market  analysis  refers  to  several  potential 
market  segments  and  focuses  on  what  is  expected  to  be  the  most  appropriate  and 
promising  one,  the  navigation  software  providers  market.  The  business  and  financial 
model analysis summarize the main assumptions regarding the revenue model, the cost 
structure,  and  the  overall  business  plan,  and  provides  some  initial  estimates  regarding 
the financials of such a venture. It is demonstrated that for reaching financial viability, a 
non-negligible  number  of  users,  i.e.  market  penetration,  shall  be  achieved.  For  this 
reason,  it  is  concluded  that  even  if  a  stand-alone  venture  based  on  a  commercial 
exploitation of U-SAVE would be of high risk, the option of partnering with an established 

22 

navigation  software  provider  who  would  want  to  extend  his  offering  by  providing  an 
alternative route option to his clients, who has established operations, infrustructure, and 
market access, would potentially be a truly viable and scalable model. 

23 

References 

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https://ec.europa.eu/clima/policies/transport_en (accessed December 13, 2017). 

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content/EN/TXT/HTML/?uri=CELEX:52016DC0501&from=en  (accessed  December 
13, 2017). 

[3]  The  Effects  of  Driving  Style  and  Vehicle  Performanc  e  on  the  Real-World  Fuel  

Consumption of U.S. Light-Duty Vehicles n.d. 

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booking n.d. https://www.viamichelin.com/ (accessed December 13, 2017). 

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https://fr.mappy.com/#/0/M2/THome/N151.12061,6.11309,8.62181,45.79872/Z11
/ (accessed December 13, 2017). 

[9]  Uconnect  Systems  for  Chrysler,  FIAT,  Jeep,  Dodge,  and  Ram  Trucks  n.d. 

https://www.driveuconnect.com/ (accessed December 13, 2017). 

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CO2MPAS  1.4.1  documentation  n.d.  https://co2mpas.io/  (accessed  November  28, 
2016). 

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[12] OpenStreetMap.  OpenStreetMap  n.d.  https://www.openstreetmap.org/  (accessed 

December 14, 2017). 

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(accessed December 18, 2017). 

24 

[24] The EC Visual Identity Manual n.d. 

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cloud/listino-prezzi.aspx (accessed December 14, 2017). 

25 

 
 
List of figures 

Figure 1. Greenhouse Gas Emissions in Europe. ...................................................... 3 

Figure 2. Flowchart: Start U-SAVE Routing HTTP Server for Finding the Most Fuel 
Efficient Route. .................................................................................................... 6 

Figure 3. Fuel Consumption Raster Function of: (left) Velocity and Slope, (right) Velocity 
and Power. .......................................................................................................... 7 

Figure 4. Flowchart: Handle a User Query and Compute the Fuel Consumption over a 
Route. ................................................................................................................ 8 

Figure 5. Speed Margins for the Velocity Suggestions. ............................................... 8 

Figure 6. Road segments sub-groups. ..................................................................... 9 

Figure 7. U-SAVE Structure. ............................................................................... 10 

Figure 8. U-SAVE Mobile Application Inputs........................................................... 11 

Figure 9. Route Selection. ................................................................................... 12 

Figure 10. Routes Visualization. ............................................................................ 12 

Figure 11. Selected Route Details. ........................................................................ 13 

Figure 12. Navigation System. ............................................................................. 13 

Figure 13. Yearly Revenues & Server Costs as a Function of the Number of API Calls. .. 20 

Figure 14. Ratio of Revenues & Server Costs as a Function of the Number of API Calls.  20 

Figure 15. Financial Estimates & Expected Market Penetration. ................................. 21 

26 

 
List of tables 

Table 1. Initial Competitors Mapping. ...................................................................... 5 

Table 2. Case Study. ............................................................................................. 5 

Table 3. Cost’s Multiplication Factors used in the Simulation. .................................... 14 

Table 4. Routing Results without Velocity Suggestions. ............................................ 14 

Table 5. Routing Results with Velocity Suggestions. ................................................. 15 

Table 6. Competitive Approaches. ......................................................................... 18 

Table 7. Feature Comparison of Top Navigation Software Providers. .......................... 19 

Table 8. Financial Planning. ................................................................................. 21 

27 

 
Annexes 

Annex 1. Real Test Cases Material 

Test Campaign Introduction 

28 

 
  
User Guide 

29 

30 

31 

 
 
 
Vehicle Data Inputs Template 

Parameter
Vehicle Plate
Car brand
Car model
Car model year
Tyre dimensions 

Roof‐box

Fuel type

Body Type

Vehicle height
Vehicle width
Vehicle mass

Value

Unit Comment

‐
‐
‐
‐
‐

‐

‐

‐

Vehicle registration plate of the car used for the test.

e.g., Fiat, Mercedes, Opel, etc.

e.g., 500X, E‐class, Corsa, etc.

e.g, 2015, 2000, 1996, etc.

Tyre code (e.g., P195/55R16 85H).
0 = No | 1 = Yes ‐ Is the vehilce equipped with a roof box? 
(default: 1)
Type of fuel used in the test: diesel, gasoline, LPG, NG or 
biomethane, ethanol(E85), biodiesel
Vehicle body: cabriolet, sedan, hatchback, stationwagon, 
suv/crossover, mpv, coupé, bus, bestelwagen, pick‐up

m Vehicle height (e.g., 1.48)
m Vehicle width (e.g., 1.76)
kg Vehicle mass without passengers and loads.

Turbo or supercharger

‐

0 = No | 1 = Yes ‐ Is the engine equipped with any kind of 
charging system? (default: 1)
Engine capacity in cubic centimeters

Engine capacity
Engine stroke
Engine nominal power
Rated engine speed
Engine nominal torque
Wheel driving 
Gearbox type
Number of gears
Final drive ratio
Top gear ratio

Trip Data Inputs Template 

cc
mm Engine stroke in mm
kW Engine nominal power
rpm Rated engine speed
N*m Engine nominal torque

‐
‐
‐
‐
‐

Specify 2‐wheel driving or 4‐wheel driving.

Gearbox type: automatic/manual/CVT

Number of gears of the gearbox.

Final drive ratio.

Gearbox ration of the hihgest gear.

Trip ID

Route 
optimization

Trip 
Group

Velocity 
suggestion

No of 
passengers

Cargo Rain Snow Windy

External 
Temp.

Engine 
is hot

YYYYMMDDhhmm

 ‐

 ‐

 ‐

 ‐

kg

bool

bool

bool

°C

bool

Air 
condition 
is ON
bool

Air 
condition 
temp.
°C

Windscreen 
wiper is ON

Fuel 
Rate

Distance

Average 
Velocity

Duration

bool

L/100km km

km/h

min

32 

 
 
 
 
Usability Report 

33 

 
Annex 2. U-SAVE Business Plan 

34 

 
35 

36 

37 

38 

39 

40 

41 

42 

43 

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