R. MANZINI ET AL. 87
nalities, follow [11-13].
CO2 emissions. During 1990-2004 the emissions of
CO2 increased by 27%, 26.079 million tonnes (Mt
CO2) in 2004, while the energy demand from the
transport sector increased by 37%. In particular, USA
and China respectively increased by 19% and 108%
in term of CO2 emissions, and by 28% and 168% in
term of energy demand.
For the 27 EU Member States (EU-27) the green-
house gas emission 2020 projections are 1091 Mt
CO2—eq. (767 Mt CO2—eq. reported in 1990), as-
suming a 15% growth in transport volume between
2010 and 2020 and no further reduction measures,
and excluding air and maritime transport.
Passenger car use. It grew by 18% between 1995 and
2004, and it was responsible for 74% of all passenger
transport in 2004. In 2005 the average car ownership
level is 777 in the USA per 1000 inhabitants, while it
is 460 in the 32 EEA members, including 27 EU
states, Turkey, Norway, Iceland, Liechtenstein and
Switzerland. The increase of car ownership rates re-
duces the average number of passenger per car and
does not contribute to cut greenhouse gas emissions.
High levels of ownership rate do not improve vehicle
In 2003 the road network, including motorways, rep-
resented about 95% of the total, including also rail-
way lines, oil pipelines and inland waterways. In par-
ticular, Italy presents the following key indicators re-
lated to railways in 2003: 28 km per 100,000 inhabi-
tants and 54 km per 1000 squared km.
Responding to a questionnaire, only 22% of EU citi-
zens stated that they would not consider reducing car
usage under any circumstances .
In UK there was a total membership of car sharing
clubs of 23,000 in 2006 .
Strategies and policies to ensure better capacity utili-
sation within each transportation mode may result in sub-
stantial additional reductions of CO2 emissions and other
externalities . Moreover, the increase of transport
demand and car usage, and the reduction of the number
of passengers per car negate the improvements gained
from the improvements in vehicle efficiency, such as 120
g CO2/km target for passenger cars by 2012 as stated by
the European Council—EC . In particular, assuming
this target met in 2012 and the cars are replaced at the
same rate as today there will be an efficiency gain of 30
g CO2 per km which corresponds to –125 Mt CO2—eq.
. Different targets, e.g. that band in the Bali roadmap
in 2020, can guarantee additional emission reductions.
Consequently the main keyword as the way forward to
a sustainability development is: integra tion. Integration
of transport and environmental strategies; integration of
vehicle efficiency targets, technologies, energy efficient
transportation modes, construction and maintenance in-
frastructures, behavioural changes, reduction of transport
demand. In particular, all EEA reports state “it will not
be possible to achieve ambitious targets without limiting
transport demand” : modal shift and influence on
user behaviours may reduce the need for demand that is
the so-called transport volume.
This paper presents a set of original cost-based models
and a DSS to solve the CPP. The proposed models and
methods mainly refer to the clustering analysis (CA),
which can be efficiently supported by commercial statis-
tical tools. Therefore, these supporting decision models
can be quickly adopted by mobility managers of indus-
trial and service production systems implementing CP
and other transportation modes to reduce traffic and
emissions or by expertise offering and supplying mobil-
Literature studies on CA as a way to group items, e.g.
car poolers, is presented by  and  for respectively
cellular manufacturing and group technology, by  for
the analysis of enterprises network and by  for freight
transportation and vehicle routing adopting the groupage
There are not recent studies on CP. Previous signifi-
cant contributions are:  on daily CPP,  and fi-
nally .  presents a case study in Strasbourg
 presents an exact and a heuristic method for the
CPP, based on two integer programming formulations.
This manuscript is not an Operations Research contribu-
tion focused on the mathematical formulation of the
problem and the development of optimal approaches to
solve it, but it is focused on effective models and meth-
ods to face the generic problem instance and support the
decision making efficiently. To this, a DSS is proposed
and applied to a set of real instances.
3. Cluster Based Approach to the CPP
The adopted approach to solve the CPP is 2-phases:
cluster first and route second. Nevertheless, it is based on
three main activities, called steps, as illustrated in Figure
3.1. Step 1
The first step deals with data collection about:
Users, e.g. geographical locations, availability/non
availability of a car to be shared, capacity of the car
(i.e. maximum admissible number of passengers) and
eventually maximum admissible extra time to reach
the destination point, etc.;
Destination point, i.e. geographical location;
Transportation network by the availability of a geo-
graphical map, routes and related performance, e.g.
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