# A State-of-the-art Integrated Transportation Simulation Platform

Tiago Azevedo, Rosaldo J. F. Rossetti, Jorge G. Barbosa  Artificial
Intelligence and Computer Science Lab
Department of Informatics Engineering
Faculty of Engineering, University of Porto, Portugal
{tiago.manuel, rossetti<EMAIL_ADDRESS>
###### Abstract

Nowadays, universities and companies have a huge need for simulation and
modelling methodologies. In the particular case of traffic and transportation,
making physical modifications to the real traffic networks could be highly
expensive, dependent on political decisions and could be highly disruptive to
the environment. However, while studying a specific domain or problem,
analysing a problem through simulation may not be trivial and may need several
simulation tools, hence raising interoperability issues. To overcome these
problems, we propose an agent-directed transportation simulation platform,
through the cloud, by means of services. We intend to use the IEEE standard
HLA (High Level Architecture) for simulators interoperability and agents for
controlling and coordination. Our motivations are to allow multiresolution
analysis of complex domains, to allow experts to collaborate on the analysis
of a common problem and to allow co-simulation and synergy of different
application domains. This paper will start by presenting some preliminary
background concepts to help better understand the scope of this work. After
that, the results of a literature review is shown. Finally, the general
architecture of a transportation simulation platform is proposed.

###### Index Terms:

Agent-directed simulation, Agent-supported simulation, HLA, High Level
Architecture, Cloud, SimSaaS, Simulation Software-as-a-service.

##  I Introduction

Nowadays, universities and companies all around the world have a huge need for
simulation and modelling methodologies. The objectives are varied, but
simulation is widely used for decision making and what-if analysis, as well as
for performance optimisation, testing, training, and so forth. In the
particular case of traffic and transportation, making physical modifications
to the real traffic networks could be highly expensive, dependent on political
decisions and could be highly disruptive to the environment. Therefore,
simulation is broadly used in such scenarios.

However, while studying a specific domain or problem, analysing a problem
through simulation may not be trivial and very often requires several
simulation tools, with different resolutions and domain perspectives, hence
raising interoperability issues. Thus instead of helping, simulation could be
a headache! Transportation problems usually are complex and fall within this
category of problems.

To date, there are not many solutions for traffic that make full use of the
intelligent agent concept. However, the multi-agent system metaphor has become
recognised as a convenient approach for modelling and simulating complex
systems [1]. Also, it has grown enormously not only for being applied to
traffic but also to transportation in general terms [2].

With the recent evolutions in Cloud Computing and Software-as-a-Service
(SaaS), there is a new paradigm where simulation software is used in the form
of services. Indeed, such evolutions have been more significantly seen in the
business world with Information Technology solutions moving to the SaaS
paradigm [3]. So, Simulation Software-as-a-Service (SimSaaS) is very
beneficial to better exploit the huge amount of platforms and storage that
simulation needs per se \- and Cloud Computing is able to provide such
resources. This way, researchers do not need to have the simulation software
installed on their own computers or access servers which host this kind of
software. Besides, they have new computing environments and methodologies for
software development over the Internet.

To address issues arising in this novel perspective the main goal of this
paper is to propose an agent-directed transportation simulation platform,
through the cloud, by means of services. It is intended to use the IEEE
standard HLA (High Level Architecture) for simulators interoperability and
agents for controlling and coordination. To do so, it is necessary to build
the body of knowledge needed to develop such a platform. This paper’s
objective is to present the current state of the art in the field and, with
that, also to present the general architecture of the intended platform.

The motivations of the platform are to allow multiresolution analysis of
complex domains, to allow experts to collaborate on the analysis of a common
problem and to allow co-simulation and synergy of different application
domains. It is expected to fulfil three main contributions. Firstly, a
technological contribution because one will have a cloud-based simulation
platform for transportation using HLA and agents where simulations are offered
in the form of services. Secondly, a scientific contribution since it will
enable the collaboration among experts of the Modelling & Simulation (M&S)
field with the agent-directed paradigm. Finally, an applied contribution with
an agent-oriented platform for scientific simulation, through the cloud, by
means of services. Basically, it will be a virtual laboratory.

This paper will start to present some preliminary background concepts
regarding M&S, the agent-oriented paradigm, HLA and cloud for a better
understanding of the scope of this work. After that, the results of a
literature review concerning SimSaaS in the Cloud, HLA and services, and
agent-directed SimSaaS is shown. Finally, the general architecture of a
transportation simulation platform is proposed.

##  II Preliminary Background

###  II-A M&S in Traffic and Transportation

In the context of M&S, a system is defined as a collection of entities, for
example people or machines that act and interact together toward the
accomplishment of some logical end [4].

There no unique definition of M&S in the literature, depending on each domain
and scientific field. According to the Merriam-Webster Online Dictionary
(http://www.merriam-webster.com/), simulation is “the imitative representation
of the functioning of one system or process by means of the functioning of
another” or, in other words, the “examination of a problem often not subject
to direct experimentation by means of a simulating device”. So, in simulation
we have not only the idea of representation but also the idea of an
experimentation without the direct intervention of a human.

Modelling is basically an abstract and simplified representation of a system.
It is similar to the system, but simpler [5], as it should be an approximation
to the real system with the most relevant features, but simple enough to be
understandable. As [6] points, a good model is a judicious trade-off between
realism and simplicity.

As it can be seen, simulation of a system is mainly the execution of a model.
Indeed, simulation is widely used for decision making and what-if analysis, as
well as for performance optimisations, testing, training, and so forth.

Most of the times it is not possible to make direct experimentation with the
actual systems, and it is necessary to make simplifications using modelling.
In this case, there is the question of whether it actually reflects the
system, but analysing this issue is outside the scope of this paper. If the
model is simple enough, it is possible to study the system using an analytical
solution through, for example, calculus, algebra and probability theory.
Otherwise, if the system is highly complex, simulation is performed using
computational means.

In the particular case of traffic and transportation, making physical
modifications to the real traffic networks could be highly expensive,
dependent on political decisions and could be extremely disruptive to the
environment. Therefore, M&S is broadly used in such scenarios and related
tools can provide better and more concise data for analysis.

There are several ways to model traffic depending on the level of detail in
which they describe the traffic dynamics. Figure 1 illustrates the major four
categories of modelling in traffic, namely macroscopic, mesoscopic,
microscopic and nanoscopic.

Figure 1: The major four categories of modelling in traffic (from left to
right): macroscopic, microscopic, nanoscopic (mesoscopic within the circle)
[7]

Macroscopic models describe traffic in terms of flows (the number of vehicles
that pass through a certain road per hour) or densities, without considering
their entities such as vehicles. Therefore, these models are good to analyse
large or complex networks.

Unlikely macroscopic models, microscopic models detail both the behaviour of
each entity and their interactions, with each other as well as with the
network. For that, these models incorporate vehicle behaviour rules such as
acceleration, breaking, lane changing, and so forth.

Mesoscopic models fill the gap between macro and micro models. They normally
describe traffic entities at a high level of detail, whereas their behaviour
and interaction are briefly described. In these models, vehicles can be
grouped in packets, which are routed throughout the network and treated as one
single entity.

A new trend in traffic simulation though is the nanoscopic model which extends
the capabilities of three basic components of microscopic simulation: vehicle
modelling, vehicle movement modelling, and driver behaviour modelling [8]. For
example, in the case of traffic, it intends to describe the components of a
vehicle. Attempts at integrating these different perspective in a more
transparent way through modelling has already been reported in the literature
[9] [10].

###  II-B Agent-directed Simulation

The introduction of intelligent demons (called intelligent agents today) to
control simulation experiments was introduced by the MISS Hungarian Center
[11]. This notion of intelligent agent may not be consensual, mainly because
of the difficulty of defining what intelligence is. However, we consider an
agent as an autonomous and proactive computational entity whose rational
process is based on concepts such as Knowledge, Belief, Intention, Commitment,
Goal, Desire and Emotion.

The intelligent agent concept brings a genuine metaphor to represent
autonomous entities as it is equipped with sensors and effectors as well as
with reasoning and decision-making abilities. Thereby, we have entities with
high-level communication skills who also provide endless possibilities for
system coordination and controlling. Otherwise, system coordination and
controlling would be reduced to automated scripts, which do not bring so many
advantages.

To date, there are not many solutions for traffic that make full use of the
intelligent agent concept. However, the multi-agent system approach has become
recognised as a convenient approach for modelling and simulating complex
systems [1] and has grown enormously not only applied to traffic but also to
transportation in general terms [2]. Nevertheless, just a few simulation tools
truly support the concept of agents and multi-agent systems in traffic
simulation; MATSim-T [12] and ITSUMO [13] are good examples to be mentioned.
Also, besides agent-based traffic simulation, the MAS-Ter Lab platform
introduces the concept of expert agents in charge of the simulation analysis
process [14].

The agent-oriented paradigm has a panoply of associated concepts, being
important to define them and point out what is our perspective on those
definitions.

The agent metaphor encompasses some other concepts, that [15] unify in the
Agent-Directed Simulation paradigm. The authors indicate that the paradigm
consists of three distinct, yet related areas that can be grouped under two
categories: Simulation for Agents (agent simulation) and Agents for
Simulation. The first is about simulation of systems that can be modelled by
agents, that is, the simulation model is an agent or, in other words, we are
simulating agent systems. The latter can still be grouped under two
categories, namely agent-based simulation and agent-supported simulation, as
follows.

  * •

Agent-based simulation is the use of agent technology to generate model
behaviour or to monitor generation of model behaviour. The perception feature
of agents makes them pertinent for monitoring tasks. Agent-based simulation is
useful for having complex experiments and deliberative knowledge processing
such as planning, deciding, and reasoning.

  * •

Agent-supported simulation deals with the use of agents as a support facility
to enable computer assistance by enhancing cognitive capabilities in problem
specification and solving. Hence, agent-supported simulation involves the use
of intelligent agents to improve simulation.

A lot of researchers do not take into account the contribution of agents to
simulation. Thus, in such cases agent simulation and agent-based simulation
are seen as the same principle. In this work, it is adopted the same
perspective in which the two principles are seen indistinguishably.

###  II-C HLA

Parallel and distributed simulation (PADS) relies on partitioning the
simulation model across multiple execution units. Each execution unit manages
only a part of the model and handles its local event list, but locally
generated events may need to be delivered to remote execution units [16].

Distributed simulation facilitates the reuse of heterogeneous simulation
systems but has issues regarding interoperability of simulators. HLA is an
IEEE software standard developed to provide a common technical architecture
for distributed M&S, trying to provide the structural basis for
interoperability among simulators.

In HLA, every participator of the simulation is called federate, and these
federates can interact with each other within a federation. The baseline
components of HLA include (1) Federate Interface Specification, (2) Framework
and Rules, and (3) Object Model Template (OMT) Specification.

The Federate Interface Specification has the services which federates can use
for communication [17]. This communication between simulators is managed by a
Run-Time Infrastructure (RTI). In order to make possible the interaction
between federates and the RTI, there is the concept of ambassador. Basically,
federates communicate with the RTI using its ambassador as an interface.

The Framework and Rules of HLA are the set of rules which must be obeyed in
order to ensure the proper interaction within a federation. There are five
rules for federates and also five rules for federations, detailed in [18].

The OMT describes the format and syntax of the data exchanged among federates.
Thus, it defines the object template data that all simulation unit needs to
use in order to exchange data with each other [19].

Figure 2 depicts all referred concepts instantiated for the work described in
[20]. In this work it was presented a distributed architecture for electric
bus powertrain simulation within a realistic urban mobility context. There,
the communication between Federate A ambassador and SUMO simulator [21] is
performed through SUMO’s API, TraCI. In this sense, whenever simulation data
is required, the RTI ambassador performs calls to federate ambassador that
communicate with SUMO through TraCI. In a similar way, the communication
between Federate B ambassador and Simulink [22] model is performed through
MATLAB, the Simulink’s API.

Figure 2:  HLA’s Functional Architecture with two different simulators [23]

###  II-D The Cloud Computing Paradigm

In 1969, Leonard Kleinrock [24] said: “As of now, computer networks are still
in their infancy, but as they grow up and become sophisticated, we will
probably see the spread of ‘computer utilities’ which, like present electric
and telephone utilities, will service individual homes and offices across the
country”. After so many years, this is becoming true, and computing is even
more managed and delivered in the form of traditional utilities such as water,
electricity, gas and telephony. Cloud Computing is helping to leverage this
utility vision.

Consequently, Cloud Computing has become another buzzword and more and more
work is being done in the field. Nevertheless, there are dozens of different
definitions for Cloud Computing and there is little consensus on that [25].

Despite this vision, it is possible to differentiate cloud computing from grid
computing. In this research, we adopt the definition of Cloud Computing
provided by The National Institute of Standards and Technology [26], as it is
an Institute responsible for standards and we did not see until now any other
standard definition:

Cloud computing is a model for enabling ubiquitous, convenient, on-demand
network access to a shared pool of configurable computing resources (e.g.,
networks, servers, storage, applications, and services) that can be rapidly
provisioned and released with minimal management effort or service provider
interaction.

As also shown in [27], there are three aspects that are new in cloud
computing: (1) the appearance of infinite computing resources available on
demand, (2) the elimination of an up-front commitment by cloud users, and (3)
the ability to pay for the use of computing resources on a short-term basis as
needed.

Cloud Computing is a fresh and on-going hot topic not only for the industry
but also among academics. Indeed, even recently new buzzwords emerged from
Cloud Computing, trying to extend it even further: Fog Computing [28] and
Cloud 2.0 [29] are examples. For a wider state-of-the-art perspective on Cloud
Computing with important research directions there are several recent sources
to analyse [30] [31] [32] [33] [34] [35].

##  III Literature Review of Cloud SimSaaS

###  III-A SimSaaS in the Cloud

Simulation Software-as-a-service (SimSaaS) is a relatively new paradigm where
simulation software is used in the form of services. Thanks to the significant
attention on the Cloud computing paradigm and taking advantage of the several
resources provided, it is possible to set simulations into the cloud. This
way, it is possible to offer simulations through the cloud by means of
services.

In our research, it turned clearly evident that SimSaaS is a very recent
topic. Just five papers are prior to 2011 and the first three do not
specifically use the term SimSaaS, vaguely mentioning simulation and web.
Besides, there is an increase number of work over the years.

Although the amount of papers found about SimSaaS is not quite big, it is wide
in which concerns application domains. For example, in the biomedical domain
there is a system devoted to simulations of electromagnetic field inside the
human body [36]. In crowd and pedestrian M&S there is also one work proposing
a method based on a distributed architecture with simulation in the cloud,
with the authors indicating that there is a lack of automation and integration
of tools for crowd M&S [37]. As [38] suggest, research dissemination methods
suffer from a major drawback as they do not allow publishing simulation code
and scripts along with the published papers. So, the authors demonstrate an
ongoing project in which scientists can openly share their underlying code and
data.

A lot of other specific application domains use SimSaaS, mostly in the cloud,
and more specifically work regarding ontology learning [39], traffic and
transportation [40],scheduling parallel discrete event simulation jobs [41]
and a cloud simulation in manufacturing [42], just to mention a few.

In addition to these specific domain works on SimSaaS, there are also generic
ones. For example, [43] proposed a SimSaaS framework incorporating multi-
tenancy architecture and scalability for simulation, also presenting a
simulation run-time infrastructure.

As [44] indicate, pioneers such as Richard M.Fujimoto, Bohu Li, Gabriele
D’Angelo and so forth, made considerations on the Cloud Simulation but none
has given an overall picture of Cloud Simulation to its full extent.
Consequently, the authors propose the general architecture of a Cloud
Simulation, which is a SaaS type cloud. This architecture is illustrated in
Figure 3.

Figure 3:  The architecture of the Cloud Simulation [44]

In general terms, it is possible to verify the infrastructure in the bottom of
the architecture, where virtualisation plays a vital role. Before reaching the
end users, which can be very diverse, the architecture shows a great detail in
the specification of the offered services. Authors divide services into three
self-explanatory groups: Modeling as a Service, Execution as a Service and
Analysis as a Service. During the provision of these services, available
simulation resource can be reused with the aid of the Simulation Resource as a
Service. Managing and connecting the baseline infrastructure and services
there is the so-called Cloud Operating System.

###  III-B HLA and services

While specifically discussing SimSaaS, there is almost no clearly references
about interoperability among simulators. However, HLA, the current standard
for simulator interoperability, has been used in many different works, such as
in agent-based simulations. In the context of this paper, it is important to
show how it is possible to extend HLA to enable simulations offered in the
form of services.

A first approach can be seen in [45]. In this paper, the authors propose a
framework to extend the HLA to support Grid-wide distributed simulation. Here,
a remote proxy acts on behalf of the federate in interacting with the RTI. It
hides the heterogeneity of the simulators, simulators’ execution platforms,
and how the simulators communicate with the RTI.

In a work done by [46], it is introduced HLAcloud, a model-driven and cloud-
based framework to support both the implementation of a Distributed simulation
system from a SysML (Systems Modelling Language dialect of UML) specification
of the system under study and its execution over a public cloud
infrastructure.

Maybe, the most relevant paper found is the one that presents a possible way
for HLA to be integrated with a Service Oriented Architecture (SOA) in the
context of a smart building project [47]. The paper discusses the design of an
HLA federate for the inclusion of a service oriented smart building controller
in the simulation loop. It is important to refer that the Simulation Manager
module is a service wrapper on top of the RTI that exposes access to the RTI’s
federation management via a RESTful API. It deals with the creation,
initialisation, deletion, starting, stopping, and execution of simulations.

Still in that paper, the authors cite [48] who presented a comparison between
HLA and SOA concluding that:

  * •

HLA has good interoperability, synchronisation and effective and uniform
information exchange mechanism between the communicating components
(federates), but lacks several features of web services, such as the
integration of heterogeneous resources, web-wide accessibility across firewall
boundaries;

  * •

SOA benefits from loose coupling, component reuse and scalability but lacks a
uniform data exchange format and time synchronisation mechanisms;

  * •

The combination of HLA and SOA can extend the capabilities of the two
technologies and thus enable integrated simulated and real services.

###  III-C Agent-directed Simulation-as-a-service

Agent-directed simulation and, more specifically, agent-supported simulation
is used in a huge variety of fields. Nevertheless, in the field of Simulation-
as-a-service, just a few examples exist, and even fewer concern the cloud.

Albeit there is such a lack of work regarding agents and cloud, work such as
[49] already mentioned the importance of agents to simulation (even in gaming)
by exploring the relationship of software agents with simulation and games.

An agent-supported simulation is seen in [50], where handling agents were used
for composition of simulation services with different time and event
granularity. In this case, the composition consisted of wildfire and weather
simulation services, but no cloud was used.

Elsewhere [51] and [52] suggest that most current simulation interoperability
standards are insufficient as they focus exclusively on information exchange
to support the federation of solutions without providing the necessary
introspection. HLA is a bit more flexible, as the information to be exchanged
itself is not standardised (it only says how to structure the data), but the
focus remains on information that can be exchanged with a system. So, the
formal approach to simulation interoperability (using agent-supported
simulation) tries to solve this problem.

The only work found that truly implements agents in the cloud indicates that
cloud computing can speed up significantly agent-based M&S to facilitate more
accurate and faster results, timely experimentation, and optimisation [53].
However, the many different clouds, cloud middleware and service approaches
make the development of agent-based M&S in the cloud highly complex.

Already in 2004 [49] pointed that there were limitations of existing federated
simulation environments in supporting dynamic model and simulation updating.
HLA federation development, for instance, requires complete specification of
object models and information exchanges before the simulation run begins. They
also observed and argued that the lack of machine processable formal
annotations describing the behaviour, assumptions and obligations of federates
is a fundamental roadblock. After a decade, it seems that this problems still
remain almost the same.

###  III-D Summary

SimSaaS is a trendy term with high potential to grow more. However, there are
problems with the term: there is a lack of automation and integration of tools
in M&S [37], and research dissemination methods suffer as they do not allow
publishing simulation code and scripts along with the published papers [38].
But not everything is bad! Although the amount of papers found about SimSaaS
is not quite big, it is wide in what concerns application domains.

Simulator interoperability is a very explored subject in general, but when it
comes to SimSaaS, almost nothing focus on this. Indeed, [54], in their panel
about the future of research in M&S, authors refer to the distribution of
SimSaaS in the cloud as a future research topic.

HLA is another term referenced a lot in the literature since the first
complete version (HLA 1.3) was published in 1998. However, again, there is a
few works regarding extension of HLA to allow simulation services in general
and in the cloud. Nevertheless, HLA solely has some disadvantages [49][51].

Although cloud computing can speed up significantly agent-based M&S to
facilitate more accurate and faster results [53], agents are not an exception
and there is an absence of work putting agents in the cloud to support
SimSaaS. Nonetheless, an example was shown where it was possible to develop an
agent-supported simulation to bear out SimSaaS [55].

Summing up, it is possible to see lots of gaps in the literature concerning
SimSaaS, SimSaaS in traffic and transportation, SimSaaS in the cloud, HLA in
the cloud, solutions to HLA restrictions, agents to support SimSaaS and agents
in the cloud. So, these will be the front lines of this work! Knowing that
there are yet a few works regarding SimSaaS and synergies of all these gaps,
this paper aims to bridge them by proposing an architecture of a
transportation simulation platform in the cloud.

##  IV A seamlessly integrated transportation simulation platform

According to the gaps previously found in the literature review, we propose an
integrated transportation simulation platform regarding every term (SimSaaS,
HLA and Agent-directed simulation), as it will offer simulation in the form of
services, using HLA for interoperability of simulators and agents for
collaboration. The general architecture of such platform will be similar to
the one described in Figure 3. The main differences to such architecture is
that it will be adapted in order to support HLA in the Virtual Resources tier
and Agents in the Cloud Management tier. Figure 4 shows the generic
architecture of the proposed platform considering the three main tiers that
differ from the generic cloud simulation.

Figure 4: Generic architecture of the proposed platform

The scientific community needs collaboration in its pursuit of
multidisciplinary achievements. This way, scientific community started to make
a first sharing approach by creating public repositories of datasets, which
are sustainable, shared and ever-evolving. However, there are other
roles/stakeholders that would also benefit from such datasets, such as public
decision-makers and the industry alike. Nevertheless, while they are all
interested in testing their own algorithms/calculations, just the scientific
community generally adopts the philosophy of data sharing. Besides data, we
also have processes, methods and plans, which are even less commonly shared.

In this multi-stakeholder and multi-resource philosophy, HLA already supports
the inclusion of resources because a federate is sufficiently general to
consider not only simulators but also databases, data loggers, and other
resources. Therefore, the so-acclaimed agent-directed approach could be used
to enable collaboration between experts, sharing not only data but also
processes. But, why is this interest in sharing? What is the interest in
creating this shared community above the cloud? We believe the answer is
simple and consists in generating knowledge and innovation.

Simple sharing methodologies are, for example, personal pages in the platform
for each researcher with the created models, similar suggested models,
integration of models of other researchers and performed simulations with
obtained results. Nevertheless, the agent-directed paradigm could bring better
options. For example, some researchers have their own legacy tools, which run
some complex and less optimised simulations, producing outputs. It would be
much better if these tools were implemented in the platform, and it also had
the possibility to deploy agents that act on behalf of the researcher, that
initialise these tools and even that generate graphics from the resulting
output. It would be like an avatar. In practice, these are services that exist
in agents: possibility to start and stop everything, make data collection,
make data analysis and even pick the output to serve as the input to other
tools.

How the researcher can implement and deploy their own agents? The platform
itself in the cloud allows that! As well as there is a methodology like HLA to
interoperate simulators, there would exist also a design methodology for each
researcher to implement his or her own agent/avatar. Each avatar is
characterised by the perceptions, actions and operators that could feature.
The cognition is decided by the researcher and is expected to implement his or
her own expertise. In summary, this is more than just a scientific environment
for empirical science. There is the need to be more than that in order to
value stakeholders like public decision makers and industry in general, as
they could implement their own simulations in the platform.

There are many fields where such a platform can enhance simulations. For
instance, cloud computing is becoming increasingly deep-seated in our lives,
and Mark Weiser once said “The most profound technologies are those that
disappear. They weave themselves into the fabric of everyday life until they
are indistinguishable from it” [56]. Indeed, living labs can be supported by
simulation, as the people in these cases would be receiving stimuli from other
virtual realities, supported by the platform.

This platform may seem too futuristic but nowadays technological advances have
all the proper means that allow its implementation. Firstly, the ones
concerning the management and control of the cloud itself. The amount of
existing tools in the field is huge, but they are mostly commercial. While
looking for alternatives, decision comes mostly between OpenStack
(http://www.openstack.org/) and OpenNebula (http://opennebula.org/).

The implementation of HLA’s RTI in the cloud is also possible. Again, there
are several tools but most of them do not implement the full HLA
specification, or have limitations for free use. Here, the choice is easier
and falls in the only two tools that seem to be still active, namely the
PoRTIco project (http://www.porticoproject.org/) and Pitch pRTI
(http://www.pitch.se/).

##  V Conclusion

This paper started to explain its context, motivation and objectives,
following clarification of important concepts for a better understanding of
the approach herein proposed. Results of the literature review were broadly
presented, focusing on the current (and a few) synergies among concepts such
as Cloud, HLA and agents in the cloud. It was thus possible to verify the
current research level regarding SimSaaS, Cloud Computing, HLA and Agent-
directed simulation.

The lack of published research on coupling these terms suggests an important
area needing further investigation, which is explored in our approach. As a
result, we shared our vision and general architecture of an agent-directed
transportation simulation platform, through the cloud, by means of services.
Next steps in our research include the implementation of the specified general
architecture, and further investigation of ways to use agents in the cloud,
over the RTI tier. It will then be possible to leverage the technological,
scientific and applied contributions as pursued in this research. As a first
approach to implement such a platform, the example previously mentioned [23]
should be executed in the cloud. If someone would be capable to execute such a
concrete application for the platform without installing anything and yet
being able to parametrise it, the platform will immediately prove its
benefits.

## Acknowledgement

This work has been partially supported by MIEIC, Faculty of Engineering,
University of Porto.

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