Agent-based modelling of Stakeholder Interaction in Transport Decisions

Abstract

This is an abridged version of the paper presented at the conference. The full version is being submitted elsewhere. Details on the full paper can be obtained from the author. ABSTRACT Community Involvement, Public Engagement, Stakeholder Engagement, are all different ways to name the participation process of interested people to public decisions. In transport planning there are lots of decisions concerning several issues, with diverse stakeholders involved from organizations to citizens. Sometimes involvement is just a single, compulsory moment of the decision-making process and it lacks in its real purpose: engaging people to find the most shared solution in the shortest time, in order to make the process effective and (cost) efficient. The aim of this work is to improve the knowledge of the involvement process by building the network of relationships among stakeholders and analysing the opinion dynamics which leads to the final decision. The methodology proposed uses an agent-based simulation and a multi-state opinion dynamics and bounded confidence model as a basis to investigate the consensus formation phenomenon. It can be used as a tool both for a preventive analysis addressed to plan an effective participation process and to predict and foster the emergence of a coalition of stakeholders towards a shared decision.

Full text

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
AGENT-BASED MODELLING OF
STAKEHOLDER INTERACTION IN
TRANSPORT DECISIONS
Michela Le Pira, University of Catania, Italy, mlepira@dica.unict.it
Matteo Ignaccolo, University of Catania, Italy, matig@dica.unict.it
Giuseppe Inturri, University of Catania, Italy, ginturri@dica.unict.it
Cesare Garofalo, University of Catania, Italy, cesar egarofalo@yahoo.com
Alessandro Pluchino, University of Catania, Italy, alessandro.pluchino@ct.infn.it
Andrea Rapisarda, University of Catania, Italy, andrea.rapisarda@ct.infn.it
This is an abridged version of the paper presented at the conference. The full version is
being submitted elsewhere. Details on the full paper can be obtained from the
author.
ABSTRACT
Community Involvement, Public Engagement, Stakeholder Engagement, are all different
ways to name the participation process of interested people to public decisions. In transport
planning there are lots of decisions concerning several issues, with diverse stakeholders
involved from organizations to citizens. Sometimes involvement is just a single, compulsory
moment of the decision-making process and it lacks in its real purpose: engaging people to
find the most shared solution in the shortest time, in order to make the process effective and
(cost) efficient. The aim of this work is to improve the knowledge of the involvement process
by building the network of relationships among stakeholders and analysing the opinion
dynamics which leads to the final decision. The methodology proposed uses an agent-based
simulation and a multi-state opinion dynamics and bounded confidence model as a basis to
investigate the consensus formation phenomenon. It can be used as a tool both for a
preventive analysis addressed to plan an effective participation process and to predict and
foster the emergence of a coalition of stakeholders towards a shared decision.
Keywords: transport planning, stakeholder engagement, public engagement, agent-based
model, opinion dynamics, sustainable mobility
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
1

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
INTRODUCTION
Community Involvement has become a relevant part of a decision-making process. The five
Public Engagement (PE) levels described by Cascetta and Pagliara (2011) (stakeholder
identification, listening, information giving, consultation, participation) are all linked with the
different phases of the “bounded rationality transportation planning process” and they refer to
levels of growing involvement. Social interaction is a key of success in transport planning,
because it fosters the emergence of coalitions facilitating the convergence of different
stakeholders to a shared solution. Therefore, planning becomes the management of a bi-
directional communication process and it requires specific programs and skills, able to
coordinate many players, conflicting interests and variables and anticipate problems. In this
respect the use of Decision Support Systems, based on quantitative methods (Cascetta, 2009),
can help to assess the outcome of different alternatives to increase the transparency and the
reproducibility of the decision process.
Community Involvement is an important part of the decision-making process according to
sustainability principles, as confirmed by the EU transport policy tendency. The Sustainable
Urban Mobility Plan (Buhrmann et al., 2011) and the Sustainable Urban Transport Plan
(Wolfram and Buhrmann, 2007) have become a reference point for a new way of transport
planning. Sustainable Urban Mobility Plans mean “Planning for the People” (Buhrmann et al.,
2011). They are the result of an integrated planning approach, with the aim to create a
sustainable urban transport system, also through a participatory approach. In Italy, public
participation in transport planning is required by law only for the Strategic Environmental
Assessment (Directive 2001/42/EC), and it must be carried out all along the planning process
from the beginning to the end.
Stakeholder theory and engagement
The concept of “stakeholder” was introduced by Freeman (1984) and it derives from
Economy, where there is a well-established literature affirming that the power of a company
lies on its relationships with them. Mitchell et al. (1997) report a chronology of the concept of
stakeholder and the key constructs in their theory of stakeholder identification and salience.
In transport planning there are lots of different stakeholders to be involved, e.g. citizens,
policy makers, public institutions, local communities, governmental organizations, NGOs,
public transport operators, experts, retailers, the private sectors and the third sector. For
example the authors, as partners of the PORTA project (www.porta-project.eu), supported by
the European Regional Development Fund within the MED Programme, are experimenting
the relevance of public participation of the diverse stakeholders involved in port planning and
in particular the relationships between Port Authority and city/citizens. The complexity of the
task requires specific tools; the methodology proposed in this work can help the knowledge of
the information exchange among the diverse stakeholders involved in transport planning.
There are several tools that can be used to engage: Roden (1984) suggests to develop a
“Community Involvement Plan”, the GUIDEMAPS Handbook (Kelly et al., 2004) reports the
different tools coupled with the phases of the involvement process, Whitmarsh et al. (2007)
propose a methodology divided into two phases (expert focus groups and questionnaires,
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
2

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
citizen workshops and questionnaires), Mameli and Marletto (2009) propose a participate
procedure by involving experts, citizens and stakeholders to implicate in different ways with
“top-down” phases (results derived from the work of experts) and “bottom-up” phases (results
derived from the participation of citizens and stakeholders). It is clear that all the methods are
time-consuming and require money, so it is not easy to make a good involvement. Indeed
there are lots of negative examples where decisions failed because of lack of Community
Involvement (e.g. the High Speed Rail Turin-Lyon). In addition to the traditional tools,
having a clear insight of the actors who take part in the decision-making and predicting the
possible results of an interaction can be of great benefit for the planning process. In this
respect linking together stakeholders in a social network and simulating the communication
among them can help to improve the knowledge of the social interaction mechanisms.
Social network analysis and opinion dynamics models
The analysis of the network consists of finding properties which cannot be obtained by
visualization. Social Network Analysis (SNA) is a powerful instrument in doing so, because it
allows to measure the centrality of the different stakeholders and the potential problems due
to topology. The use of SNA in the field of Stakeholder Engagement can simply consists of
stakeholder mapping or it can include centrality measures.
Stakeholder engagement is a dynamic process and it is characterized by several reassessment
of the network. Together with the network analysis it can be helpful to simulate how the
opinions flow through the set of connections in order to improve the knowledge of the
involvement process at the earliest stage and to understand how to manage stakeholders. The
opinion dynamics which should lead to consensus can be reproduced through different
models. One of the most widely known is the Hegselmann and Krause (HK) compromise
model (2002), where the nodes form their actual opinion by taking an average opinion based
on their neighbours’ ones (i.e. the nodes connected with an edge). This leads to a dynamical
process which should flow into a consensus among all agents.
In general the opinion dynamics models consist of algorithms that can be analytically or
numerically solved; the dynamics is usually simulated by means of Monte Carlo algorithms.
Agent-based modelling is a powerful instrument in simulating the opinion dynamics for many
reasons, such as the relative easiness to represent a network of nodes (agents) linked together
with ties, the possibility to ask the agents (endowed with own properties) to have an opinion
and act according to simple behavioural laws, the power of visualization, that can help the
analysis, the opportunity to change the global variables, which makes generalization possible
and especially for the emergence of collective behavioural patterns which are not predictable
from the simple initial rules and that come out from simulations. For all these reasons, agent-
based modelling is suitable to represent the stakeholder network and to simulate the opinion
dynamics.
Therefore, in this work the focus is on a potential step of the participation process: the study
of how the network topology and the initial conditions can influence the final decision, by
simulating the opinion dynamics which takes place in the stakeholders’ network.
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
3

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
METHODOLOGY
The need to include Public Engagement in transport decision-making process leads to the
effort to understand how to design and speed the process of taking a public decision and to
find out if the communication among stakeholders can influence the process of governance.
Social network analysis and opinion dynamics models can allow to know how the actors
involved in the planning process are linked together, how the network structure can enable or
limit a joint action and how the social and spatial architecture of the community network can
influence the outcome of the planning process. It is worth to make a distinction between the
two techniques:
•SNA can be used to make static measures of the network, improving the knowledge
of the actors involved and helping to understand how a modified topology can foster
the emergence of coalitions towards a shared solution;
•opinion dynamics models allow to make dynamic measures which can help to make
prediction about the final decision that might derive from interaction.
The methodology proposed is based on an agent-based simulation of the opinion dynamics on
a stakeholders’ network, through the implementation of a multi-state opinion dynamics and
bounded confidence model. It is not intended as an operative participative decision-making
tool, but as a strategic and preventive mean to plan an effective participation process and to
predict and foster the emergence of a coalition of stakeholders towards a shared decision.
We used NetLogo (Wilensky, 1999), a multi-agent programmable modelling environment
which can reproduce lots of characteristics of complex systems, following the time evolution
and the significant parameters real-time. NetLogo was previously used in transport modelling,
e.g. for the simulation of pedestrian behaviour (Camillen et al., 2009) and the impact of real
time information on transport network routing (Buscema et al., 2009).
Implementation of the multi-state opinion dynamics and bounded confidence
model
The implemented model is inspired to the majority rule (MR) model (Galam, 2002), where all
the agents at time t are endowed with binary opinions (+1, −1) and they can communicate
with each other. At each interaction, a group of agents is selected at random (discussion
group): as a consequence of the interaction, all agents take the majority opinion inside the
group. Our model can be considered a multi-state opinion model where agents are endowed
with one opinion among approval, disapproval or neutral, denoted by +1, −1 and 0
respectively. The neutral opinion is considered less significant and “contagious” than the two
others, so the latter were assigned with a double weight. Each node can change its opinion at
time t+1 based on its neighbours’ ones with a probability related to their influence. It is also a
bounded confidence model, because of the definition of a confidence bound which limits the
way a node can change its opinion: a node with +1 cannot directly change its opinion in −1
(and vice versa), but it must pass through the opinion 0 before. The activation of the
confidence bound depends on the node property influenceability, a random real number in the
range [0,1], which represents the probability that a node directly changes its opinion without
any confidence bound. If the parameter has a value close to 1, the probability to directly
change its opinion without passing through the neutral stance is high and vice versa when the
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
4

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
value is around 0. In conclusion, each node is characterised by a certain influence (which
affects the neighbours’ opinions) and by a certain “influenceability” (which expresses to what
extent a node can be influenced by its neighbours).
The implemented algorithm consists of the creation, for each node, of a vector filled with the
weighted opinions of all the neighbours. Let xi (t) be the opinion of the node i at time t; the
opinion at time t + 1 will be:
where vi (t) is the vector of the neighbours’ opinions, which are repeated, for each neighbour,
a number of time related to the opinion weight, the influence and according to a belonging
factor, considering that there are more possibilities to interact within the same group:
with k = −1, +1, 0.
At each time an element of the vector will be randomly chosen, therefore the most numerous
opinion will be the most likely to be selected. At this point it is useful to distinguish “strong
ties” from “weak ties”, a standard description in community structure analysis for indicating,
respectively, links between nodes belonging to the same group and links between nodes
belonging to different groups. We call “degree” the total number of links (strong + weak) of a
given node and “z-out” the number of weak links of the same node.
In order to reproduce potential external influences to the opinions, we assumed that the
dynamics can be modified by means of Changing-Mind-Rate (CMR), a factor we introduced
to represent the probability that a given node would randomly change its opinion at a given
time. We considered a single event when, starting from a given distribution of opinions
among the agents, it ends with all agents converging towards the same opinion. We also
considered a multi-event version, with different (random) results related to the same initial
distribution of opinions.
The dynamics starts from a positive initial group, that is to say a group of nodes that initially
have the +1 opinion. Therefore, for what concerns the simulations, there are three main
elements that can be modified:
1. Topology (average degree, average z-out)
2. Initial conditions (positive initial group)
3. Opinion dynamics (CMR)
Considering N events for each simulation, we are interested into the following simulations’
results: the number of events ended with a complete consensus (all the opinions equal to +1)
or complete dissent (all the opinions equal to −1) and the average time for reaching consensus
or dissent. In order to convert the final outcome of the events into a unique index we
calculated the parameter W as the weighted average of the final network state, i.e. the net
frequency of the events which end with +1:
where Nk is the number of events ended with consensus (k = +1) or dissent (k = −1) and N is
the total number of events. W is included in the interval [−1,+1], where the extreme values −1
and +1 represent, respectively, 100% of events ended with dissent or consensus. It represents
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
5

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
a statistics of the events and it does not indicate the rate of agents which have the opinion +1
at a certain step of the simulation or the degree of sharing of a project. On the other hand, it is
an index which measures the tendency of the final state of the system towards the full
consensus or the full dissent, so it represents the final configuration of the opinions.
A time threshold was defined in order to exclude the cases in which the process took too long
time (t > 500) before reaching consensus (or dissent). Therefore, when time exceeds the
threshold without reaching any convergence of opinions, we say that the simulation outcome
is “no consensus/dissent”.
CASE STUDY
The decision-making process regarding transport planning is characterized by a high level of
complexity and it is not simple to be described with a model. Therefore, in order to apply our
methodology to a case study, we decided to represent a simple, real situation of a decision-
making process regarding transport issues. In particular we depicted a well-known situation of
a narrow and homogeneous community of people with the same interest, i.e. easy access to
the workplace. In particular, the case study of this work is about the idea of adopting parking
pricing inside the Campus of the University of Catania as one of the main transport policy for
sustainable mobility proposed by the mobility management office of the University. The topic
involves all the University staff, including full professors, associate professors and assistant
professors, while students are excluded because they cannot access those parking spaces.
Some observations carried out during several meetings on these issues, though not systematic
and statistically significant, were useful for the construction of the model. The network was
created according to relationships derived by roles and by department organization
(institutional relationships). Thanks to the knowledge of all the elements it was possible to
build the network and simulate the opinion dynamics which should lead to a
consensus/dissent.
Simulations and results
Taking into consideration topology, in order to reproduce realistic situations, two cases were
considered:
1. average degree 10, i.e. on average each node is connected with other 10 nodes;
2. average degree 20, i.e. on average each node is connected with other 20 nodes.
The simulations were performed by varying the number of weak ties, i.e. with a parameter z-
out ranging, in average, from 1 to 5 for degree 10 and from 5 to 10 for degree 20 (both degree
and z-out are extracted from normal distributions). We considered 10 different (random)
realizations of the initial distribution of opinions (multi-event version with N = 10). To
understand the impact of external influences on the final decision, a series of simulations was
made with average degree = 20, CMR = 0.5% and z-out varying from 5 to 10. The next tables
show some results in terms of the parameter W, as above defined.
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
6

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
Table I - Parameter W with random initially positive nodes (av. degree = 10, CMR 0.0%).
number of
random
positive
1 2 3 4 5
0 -1.0 -1.0 -1.0 -1.0 -1.0
50 no consensus/dissent -1.0 -1.0 -1.0 -1.0
100 no consensus/dissent no consensus/dissent -1.0 -0.8 -0.8
150 no consensus/dissent no consensus/dissent 0.4 -0.6 0.2
200 no consensus/dissent no consensus/dissent 0.8 0.8 0.6
250 no consensus/dissent 1.0 1.0 1.0 1.0
300 no consensus/dissent 1.0 1.0 1.0 1.0
350 no consensus/dissent 1.0 1.0 1.0 1.0
400 1.0 1.0 1.0 1.0 1.0
W
average degree = 10, CM R = 0.0
average z-out
Table II - Parameter W with initially positive groups (av. degree = 10, CMR = 0.0%).
average
influence
number
of nodes
role
10 8
head of
department
no consensus/dissent -1.0 -1.0 -1.0 -1.0
8 149
full
professors
no consensus/dissent no consensus/dissent 0.2 1.0 0.4
6 156
associate
professors
no consensus/dissent no consensus/dissent -0.6 0.0 -0.8
4 149
assistant
professors
no consensus/dissent -1.0 -1.0 -1.0 -1.0
no consensus/dissent no consensus/dissent 0.4 -0.2 0.4
no consensus/dissent no consensus/dissent 0.2 0.4 0.6
no consensus/dissent no consensus/dissent 0.4 0.2 0.6
no consensus/dissent no consensus/dissent 1.0 1.0 1.0
no consensus/dissent 1.0 1.0 1.0 1.0
5
W
random nodes
1 department
2 departments
3 departments
positive initial group
4 departments
1
2
3
4
average z-out
average degree = 10, CMR = 0.0
Whatever the initial conditions are, it is clear that a too small number of weak ties critically
slows down the information exchange; actually, when a node has on average 10 links, it is
evident that we need more than 2 weak ties in order to reach convergence of opinions.
Furthermore, the parameter W is minimum when the positive initial nodes are heads of
departments (a minority, but very much influent) or assistant professors (more numerous, but
less influent), that is to say that it is very difficult to reach consensus when only one of these
groups is originally positive about the given topic (in our case the parking pricing). On the
other hand, higher W values are achieved with entire positive departments. In Table I it is
useful to make comparisons by column, in order to notice the change from total dissent (i.e.
100% of events ended with dissent) to total consensus (i.e. 100% of events ended with
consensus) as the number of initially positive nodes increases. Analysing the results by row in
Table I and Table II it appears that, in the transition phase (and in particular in proximity of
the critical threshold), which is an area of “turbulence”, there are fluctuations in the results
(e.g. for 150 random positive nodes) also due to the limited number of simulations with the
same starting conditions. This result is also visible if we study the behaviour of the parameter
W versus an increasing number of randomly chosen initially positive nodes (ranging from 0 to
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
7

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
400), where a transition from dissent (W = −1) to consensus (W = +1) clearly appears in
correspondence of around 150 positive nodes and can be appreciated plotting the parameter W
within a scatter diagram ( Figure 1). Indeed, all the events end with dissent up to 50, then
there is a transition phase with some events ended with dissent and some others with
consensus (from 50 to 250 nodes) and where the lines for different z-out can intersect, whilst
all the events end with consensus when there are more than 250 (randomly chosen) initially
positive nodes.
Figure 1 - The parameter W as a function of the number of random positive nodes on varying z-out (av. degree =
10, CMR = 0.0%).
For what concerns the average time to reach the final decision, it is possible to plot it as a
function of the number of random positive nodes and for several values of z-out (Figure 2).
It results that the convergence time presents a peak exactly in correspondence of the transition
from total dissent to total consensus. Such a peak is much more pronounced for smaller values
of the average z-out, i.e. when the small number of weak ties does not allow the positive
opinions to spread over the entire networks.
Figure 2 - Average convergence time as a function of the number of random positive nodes on varying z-out (av.
degree = 10, CMR = 0.0 %).
If we increase the number of links (average degree = 20) the results are similar. The greater
number of links improves the communication among nodes, so consensus/dissent is always
reached, even when the number of weak ties is small. If we consider the presence of external
influences, represented by non zero values of the CMR indicator (CMR = 0.5%) in general it
produces an increase in convergence time but does not significantly affect the transition from
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
8

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
dissent to consensus, which occurs between 150 and 200 initially positive (randomly chosen)
nodes.
CONCLUSIONS AND DISCUSSIONS
Transport planning is mainly a complex decision-making process, with many actors involved
and different conflicting objectives and opinions. In this paper we propose an agent-based
model that can simulate the opinion dynamics on a network of stakeholders involved in
transport planning, in order to support the decision-making process. The presented model is a
multi-state opinion model with 3 different opinions. It is also a bounded confidence model
because of the presence of a confidence bound which limits the opinion changing from
approval to disapproval (and vice versa) by means of the neutral opinion. We applied our
model in a very simple case study, both to test the model and to capture the intrinsic essence
of the complex phenomena of social interaction. The decision-making process regards the
adoption of a new parking pricing system inside a University Campus, where a well-known
situation of a narrow and homogeneous community of people (professors) with the same
interest, made quite reasonable the opinion dynamics model we implemented. For what
concerns topology, many links help the communication among nodes and it takes few time to
reach the final decision, while few links slow down the process and sometimes it requires too
much time to reach consensus or dissent. Choosing random initial positive nodes, there is a
transition from dissent to consensus within which the time required for the convergence of
opinions has a peak. Introducing external influences which affect the dynamics, the process
slows down and it requires more time to reach a decision.
Further research will tend to modify the opinion dynamics, for instance increasing the number
of possible opinions or changing the model from a discrete choice model to a continuum one,
or including the possibility that the stakeholders could change their mind by policy persuasion
or awareness raising. Indeed, our model considers that some people can have a greater weight
than others through the parameter influence, but we are neutral about the result. For what
concerns the stakeholder network, it would be useful to see how the geographical distance and
the department affinity influence the topological distance of the nodes, affecting the
information exchange. Moreover, in order to calibrate the model, it would be helpful to
compare the results of the simulations with a real situation with systematic observations to see
if the model results are in agreement with reality.
In conclusion, Stakeholder Engagement is an integral part of the transport planning process. It
involves all the stakeholders from the very beginning of the planning process, with different
levels of involvement during the planning phases. Its aim is to foster the emergence of
coalitions among stakeholders towards a shared solution.
Our model can be useful to the design of the stakeholder involvement at an early stage of the
planning process, because it can predict and, therefore, raise the awareness of the possible
results of interaction; consequently it allows to set up the priority for information and it helps
to understand how to improve the linkages among stakeholders in order to facilitate the
involvement process; moreover it investigates the probability that external influences can
modify the convergence towards a shared solution. Therefore, studying the stakeholder
network and the opinion dynamics can help to understand how to make a good involvement
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
9

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
process and can be helpful to make the planning process transparent, effective and (cost)
efficient.
REFERENCES
Buhrmann, S., F. Wefering and S. Rupprecht (2011). Guidelines. Developing and
Implementing a Sustainable Urban Mobility Plan. ELTISplus, Working document.
Rupprecht Consult, Germany. In:
http://www.sustainable-urban-mobility-plans.org/docs/SUMP_guidelines.doc
Buscema, D., M. Ignaccolo, G. Inturri, A. Pluchino, A. Rapisarda, C. Santoro and S. Tudisco
(2009). The impact of real time information on transport network routing through
Intelligent Based Simulation. Proceedings to TIC-STH Symposium on Human Factors
and Ergonomics. IEEE Xplore, ISBN: 9781424490943.
Camillen, F., S. Caprì, C. Garofalo, M. Ignaccolo, G. Inturri, A. Pluchino, A. Rapisarda and S.
Tudisco (2009). Multi agent simulation of pedestrian behavior in closed spatial
environment. Proceedings to TIC-SENCS Symposium on Engineered and Natural
Complex Systems - Modeling, Simulation and Analysis. IEEE Xplore, ISBN :
9781424438785.
Cascetta, E. (2009). Transportation System Analysis: Models and Applications. Springer,
New York.
Cascetta, E. and F. Pagliara (2011). Public Engagement and Transportation Planning: Some
evidences from Italy. SIDT, National Conference and International Seminar, Venice.
In: http://www.sidt.org/sidt2011/wp-content/uploads/SIDT-2011_PL2_02_Public-
Engagement-and-Transportation-Planning.pdf
Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman, Boston.
Hegselmann, R. and U. Krause (2002). Opinion dynamics and bounded confidence: models,
analysis and simulation. Journal of Artificial Societies and Social Simulation, 5(3).
Kelly, J., P. Jones, F. Barta, R. Hossinger, A. Witte and A. C. Wolf (2004). Successful
transport decision-making. A project management and stakeholder engagement
handbook. In: http://www.civitas-initiative.org/docs1/GUIDEMAPSHandbook_web.pdf
Mameli, F. and G. E. Marletto (2009). A participative procedure to select indicators of
sustainable urban mobility policies. CRENoS-CUEC, Cagliari.
Mitchell, R. K., B. R. Agle and D. J. Wood (1997). Towards a theory of Stakeholder
Identification and Salience: Defining the Principle of Who and What really counts.
Academy of Management Review, Vol. 22, No. 4, 853-886.
Roden, D. B. (1984). Community Involvement in Transportation Planning. Technical Report
Series 37. North Central Texas Council of Governments, Arlington, TX.
Whitmarsh, L., A. Haxeltine and M. Wietschel (2007). Sustainable transport visions: expert
and non-expert stakeholder perspectives on sustainable transport. International
Conference on Whole Life Urban Sustainability and its Assessment, Glasgow. In:
http://download.sue-mot.org/Conference-2007/Papers/Whitmarsh.pdf
Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer Based
Modeling. Northwestern University, Evanston, IL. In:
http://www.ccl.northwestern.edu/netlogo
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
10

Agent-based modelling of Stakeholder Interaction in Transport Decisions
LE PIRA Michela; IGNACCOLO Matteo; INTURRI Giuseppe; GAROFALO Cesare; Pluchino
Alessandro; RAPISARDA Andrea
Wolfram, M. and S. Buhrmann (2007). Sustainable Urban Transport Planning SUTP Manual.
Guidance for stakeholders. Rupprecht Consult, Germany. In:
http://www.ccl.northwestern.edu/netlogo
13th WCTR, July 15-18, 2013 – Rio de Janeiro, Brazil
11