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ABSTUR: An Agent-based Simulator for Tourist Urban Routes
Iván García-Magariño ⇑
Department of Computer Science and Engineering of Systems, University of Zaragoza, Escuela Universitaria Politécnica de Teruel, Calle Ciudad Escolar s/n, Teruel 44003, Spain
a r t i c l e i n f o
Article history:
Available online 5 March 2015
Keywords:
Agent-oriented software engineering
Ingenias
Multi-agent system
Simulation
Tourism
a b s t r a c t
There are plenty of expert and intelligent systems related to tourism, either for (1) selecting appropriate
paths, (2) recommending routes or travel packages, (3) simulating certain implications in tourism, or (4)
virtually immersing tourists. However, to the best of author’s knowledge, none of these works provides asystem that simulates how many tourist people sign up for each tourist route considering the features of
some routes and tourists. This article presents an Agent-based Simulator (ABS) that covers this gap of the
literature and is called Agent-based Simulator for Tourist Urban Routes (ABSTUR). It receives input from a
set of routes and certain number of tourists with different types, and provides the number of tourist peo-
ple signed up for each route after the simulation. ABSTUR has been experienced by assisting a group of
tourism experts in designing a set of tourist routes in the historic center of Madrid. In this manner,
experts were able to avoid collections of routes with overcrowded or non-profitable routes. ABSTUR
has also proven to be efficient by comparing it with another ABS with the same specifications.
2015 Elsevier Ltd. All rights reserved.
1. Introduction
Nowadays, the popularity of recommender systems of tourist
routes is increasing, especially the ones installed in mobile devices,
as Gavalas, Konstantopoulos, Mastakas, and Pantziou (2014) show
in their analysis. By the same token, the economics of some cities
are usually influenced by their tourism activity, as indicated by
the review of Song, Dwyer, Li, and Cao (2012). Thus, the tourist
recommender systems can influence in the tourism of certain
cities, and consequently in their economics.
The present research is grounded on the assumption that the
cores of the tourist recommender systems are their underlying sets
of routes and their attached suitability recommendations for the
different kinds of tourists. For this reason, this work presents an
ABS, called ABSTUR, which guides domain experts in the creation
and distribution process of appropriate tourist route sets. In this
process, experts can (1) load a set of routes from a file, (2) simulatethe tourist behaviors in this set routes with several parameters, (3)
observe whether there is any route overcrowded or non-profitable
according to the people signed up for each route in the simulation,
(4) repeat the previous steps until the experts select an appropriate
set of routes, and (5) export the appropriate set of routes to a web
application publicly available. In this manner, this ABS assists
experts in determining and distributing appropriate sets of routes,
preventing these routes from being non-profitable or overcrowded.
This work belongs to the context of a research project about
tourist information systems for promoting cultural urban routes,
with Madrid historic center region as a pilot project, supported
by the Hergar foundation (see acknowledgments section for
further details). In this project, tourism experts are committed to
propose a set of routes that are useful for presenting routes for
all kinds of tourists. One of the goals of this project is to provide
a set of routes that distribute the tourists in a balanced way among
the routes, according to certain numbers of tourists of each type, in
order to avoid overcrowded routes and non-profitable routes. For
achieving this goal, the tourism experts require a simulator as
the one presented in this paper, so that they can properly assess
the different sets of routes. In particular, this work addresses this
need by means of an ABS.
Regarding the similar existing works in the literature, some
Multi-agent Systems (MASs) perform simulations in different
aspects of cities, like for example in Nguyen, Bouju, andEstraillier (2012), but these ABSs are not strictly related to tourism.
The existing works related to tourism simulations such as Balbi,
Giupponi, Perez, and Alberti (2012) are not aimed at improving
sets of urban routes as the current work is. The existing 3D tourism
simulation environments, e.g. (Hsu, 2012), neither address the goal
of the current work. Thus, to the best of author’s knowledge, the
presented ABS is novel in its objectives. In other words, ABSTUR
is the first ABS that simulates how many people sign up for certain
tourist routes given the features of routes and tourists. The
improvements of the current work over the related works are
further discussed later in this article.
http://dx.doi.org/10.1016/j.eswa.2015.02.023
0957-4174/ 2015 Elsevier Ltd. All rights reserved.
⇑ Tel.: +34 978645348; fax: +34 978618104.
E-mail address: [email protected]
Expert Systems with Applications 42 (2015) 5287–5302
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ABSTUR has been designed according to the Ingenias methodol-
ogy (Pavón & Gómez-Sanz, 2003) using the Ingenias modeling
language. The programming code of ABSTUR uses a framework that
is aimed at having a high efficiency, which is necessary in sim-
ulations with large amounts of agents, data and iterations. For
instance, this framework avoids high time-consuming messages
through agent platforms such as the Java Agent DEvelopment
Framework (JADE) platform (Bellifemine, Poggi, & Rimassa,2001), and gathers groups of people with the same behavior.
ABSTUR includes five main types of tourists, which are singles,
couples, families with babies, families without babies, and groups
of friends. Each of these tourist types is represented with a differ-
ent agent type. In addition, there is a simulator agent that guides
all the simulation and presents the analysis of the results to the
user. Furthermore, a route manager agent is in charge of managing
the access to the different tourist routes.
As a proof of concept, this article presents the importation
process for loading routes from files, the execution of a simulation,
its analysis, and the exportation of a set of routes to a web applica-
tion. The execution of the simulation represented more than nine
millions of tourist people, and was performed with 2350 agents
and 1200 iterations over 32 Madrid routes, with an elapsed time
around only three seconds. Furthermore, ABSTUR was compared
to another ABS recently developed with the same specifications
but without the adaptation framework. The results show that
ABSTUR is about nine times faster than the other ABS.
This work enhances our previous work (García-Magariño, 2014)
in several ways. To begin with, the simulator now incorporates a
route loader, so that experts can easily introduce route data from
files, managing and configuring the route sets for simulations.
This also allows other practitioners to use this application for sets
of routes in other urban areas. The simulator now takes more infor-
mation of tourist routes into account such as their start location,
duration and types, getting this information from a database. The
experimentation of the simulator has been enhanced, increasing
the number of agents from 92 to 2350, the number of routes from
10 to 32, and the number of iterations from 50 to 1200. In addition,the elapsed time is now measured to show the high performance of
the presented ABS. The performance of ABSTUR is now compared
with another ABS with the same specifications in twelve different
configurations. Furthermore, the current system now allows expert
domains to easily export a set of routes to a web application, once
they obtain a relevant set of routes.
The remaining of this paper is organized as follows: the next
section presents the related works indicating the improvement of
the current work over the literature; Section 3 describes ABSTUR
including its definition with Ingenias, the presentation of the adap-
tation framework for increasing performance, and a description of
its simulation tool and web application; Section 4 shows ABSTUR
running for simulating the tourists in Madrid routes, presenting
the Graphical User Interface (GUI) of the simulator and analyzingthe obtained results; Section 4 presents a comparison of perfor-
mance between ABSTUR and another ABS with the same speci-
fications; finally, Section 5 mentions the conclusions of this work
and the future lines of research.
2. Related works
The related works have been classified into different categories
for its presentation. In particular, Section 2.1 introduces some
expert and intelligent systems related to the current work.
Section 2.2 discusses existing MASs in tourism alongside some
recommender systems for tourists. Section 2.3 presents sim-
ulations that are related with tourism, including the simulationsin 3D environments. Finally, Section 2.4 summarizes all the
findings in the literature, and discusses the contribution of the cur-
rent work over the literature.
2.1. Expert and intelligent systems
There are many expert and intelligent systems aimed at obtain-
ing routes or predicting these. For instance, Nasir, Lim, Nahavandi,
and Creighton (2014) present an intelligent system for predictingpedestrian routes. They introduce an algorithm that simulates
how people select a path for getting to a certain location from
another. Its system mainly focuses on obtaining a short path that
avoids certain obstacles. Jovanović, Pamuč ar, and Pejč ić-Tarle
(2014) introduce an intelligent system for routing green vehicles
based on a neural network and a fuzzy approach. This work pre-
sents a distribution model of vehicles in a public transport net-
work. Zhao and Jiang (2015) apply the Memetic algorithm to
optimize the routes for reducing the network transit reducing cost
per passenger. It uses a genetic algorithm for solving this problem.
All these works are mainly focused on obtaining either the shortest
paths or the distribution with less transit, assuming each user
wants to go from one location to another. By contrast, the current
work is aimed at simulating people signing up for tourist routes, in
which these people select routes mainly according to their tourism
preferences.
There are several works that use MASs for simulating different
aspects of cities. For instance, Nguyen et al. (2012) present an
ABS that simulates the different kinds of transportation in a city.
This work takes travel, parking and transportation strategies into
account. In addition, Mustapha, Mcheick, and Mellouli (2013)
simulate natural disaster complex systems with an ABS. Its main
goal is to guide rescue teams in an effective organization for saving
as much lives as possible in natural disasters. In this line of
research, Wijerathne, Melgar, Hori, Ichimura, and Tanaka (2013)
present an ABS for simulating the evacuation of urban areas. In this
simulation, they show the effectiveness of a navigation algorithm
that allows a massive number of people to rapidly evacuate from
a large urban area. Similarly, Wagner and Agrawal (2014) havedeveloped an ABS for the evacuation of crowded places such as
auditoriums and stadiums where there is uncontrolled fire, in
order to establish the necessary measures beforehand, so that the
consequences in real fire situations are mitigated. All the afore-
mentioned works have in common with the current work that they
use ABSs for simulating scenarios beforehand in order to improve
the organization or measures when the real situations occur.
However, none of these works uses ABSs for guiding tourism
experts for obtaining suitable sets of tourist routes, as this work
does for promoting urban tourism.
Lin, Lin, Hu, and Lee (2014) present a method for generating
destination maps and thematic maps for tourists to meet their
mental map. In particular, firstly they apply a network warping
method. Then, they deform the road network according to theuser-specified mental map. In this manner, their resulting map
provides visual aids for route planning and navigation tasks for
tourists, as well as including certain purposes of advertising.
Thus, this work assists tourists in planning routes with a generated
map that is similar to mental maps, but it does not provide a sim-
ulator of tourists signing up for routes as the current work.
2.2. MASs and recommender systems in tourism
Some works present MASs for addressing certain goals in tour-
ism field. For instance, Jung (2011) presents a MAS that constructs
indirect alignment between ontologies with different languages.
The MAS was deployed through the JADE platform. By contrast,
the current work uses the MAS for predicting tourism people selec-tions, for achieving an appropriate set of Madrid routes.
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Moreover, the Tourist@ system (Batet, Moreno, Sánchez, Isern, &
Valls, 2012) is an agent-based recommender application for
suggesting personalized routes to tourists once they have arrived
to their location. This work takes several features of tourist profiles
into account. Similarly, the current work includes a web applica-
tion that also recommends routes for different kinds of tourists.
Nonetheless, that work does not consider the repercussion of
unbalanced sets of tourist routes, as the current work does.Lately, several recommender systems have been proposed for
recommending tourist routes. In particular, Borràs, Moreno, and
Valls (2014) present a survey of most recent recommender systems
for tourists. It classifies these systems between web-based applica-
tions and mobile applications. It also reviews systems according to
their functionalities. These functionalities are (1) to offer travel
destination and tourist packs, (2) to rank lists of suggested attrac-
tions, (3) to plan routes customized for each user and (4) to con-
sider certain social aspects in tourist routes. It also mentions the
main artificial intelligent techniques that have been applied in this
field.
The Gat platform (Rodriguez-Sanchez, Martinez-Romo,
Borromeo, & Hernandez-Tamames, 2013) provides a mechanism
for offering a mobile recommender system, by automatic crawling
points of interests from the web. This automatic process was
supervised by a human expert, and this platform was experienced
by obtaining Spanish points of interests from the Wikipedia.
The Objected-oriented Recommender System (ORS) (Tan, Liu,
Chen, Xiong, & Wu, 2014) incorporates several types of context
information for suggesting travel packages to their users. For
instance, it considers travel area, season and price of travel pack-
ages. It can also use additional information with feature-value
pairs. This system either uses (1) extraction of topics based on
intrinsic feature-value pairs, or (2) a Bayesian network for calculat-
ing probabilities that a tourist selects the same travel package as
another previous tourist.
Moreover, Liu, Xu, Liao, and Chen (2014) present a system for
recommending personalized routes, taking real-time traffic infor-
mation into account. Their goal is to reduce the time of touristsin traffic jams and queuing, mainly in tourist hot spots. Their
system receives input from the real-time traffic situation and the
users preferences, and recommend self-drive routes for their tour-
ist driver users.
SigTur/E-Destination (Moreno, Valls, Isern, Marin, & Borràs,
2013) is a web-based recommender for routes in Tarragona
(Spain). This system applies an ontological approach for providing
routes for tourists. In addition, Garcia, Sebastia, and Onaindia
(2011) present a recommender system for tourist routes as an
extension of the e-Tourism system. Their extended system recom-
mends routes for not only tourist individuals but also to tourist
groups, as the current work does. The recommender is experienced
with routes in the Valencia city (Spain).
All these recommender systems are mainly focused on provid-ing personalized routes without (1) simulating different kinds of
tourists signing up for routes nor (2) providing a tool that assists
tourist experts in designing balanced sets of routes that avoid over-
crowded and non-profitable routes, as the current work proposes.
2.3. Tourism simulators
There are works that concretely perform simulations related to
tourism. Specifically, Balbi et al. (2012) have constructed an ABS
for assessing the impact of weather conditions on the alpine tour-
ism. Their system mainly considers three factors, which are the
weather conditions (snow cover and temperature), numbers of
the different kinds of tourists and the type of market competition.
In addition, they use eight different kinds of tourist agents. Thiswork is similar to the current one in two factors: (1) both works
use ABSs in the tourism context (2) both works use similar number
of tourist profiles. However, there are two main differences. Firstly,
the tourism environments are different (alpine areas opposed to
urban areas). Secondly, the improvement objective is quite
different; the former work is aimed at improving the infrastruc-
tures from the winter industries point of view, while the latter
work pursues the improvement of the set of offered tourist routes.
Moreover, Hamilton, Maddison, and Tol (2005) present a sim-ulation that analyzes the influence of international tourism in the
climate, population and income of different countries. This work
is based on data of departures and arrivals for 207 countries, and
concludes that the influence of international tourism is higher in
population and income than in climate. Nonetheless, the goal of
the current work is different, since it is aimed at promoting the
tourism in particular cities instead of forecasting its international
influence on different countries.
McArdle, Furey, Lawlor, and Pozdnoukhov (2014) introduces a
simulation of traffic flows in the Greater Dublin region. It receives
input from the digital footprints of city inhabitants in applications
such as Twitter and Foursquare. They simulate traffic volumes at
main road segments at certain travel periods. This information
could be used by tourists when selecting a self-drive route. In par-
ticular, they chose MATSim (Multi-agent Transport Simulation
toolkit) as the agent-based simulation toolkit. However, this works
is mainly aimed at predicting traffic flows due to citizens, which is
a different goal from the objective of the current work.
Furthermore, Paletta and Herrero (2011) present a simulating
collaborative system by means of an ABS. This system includes a
tool in which users can configure the parameters of each sim-
ulation. As a case study, they present a simulating collaborative
system for designing combined traveling packages. The purposes
of these traveling packages are mainly tourism, business and plea-
sure. However, these packages are related to the transport and
accommodation rather than the tourist routes as in the current
work.
Some works relate to the simulation of tourism situations with
3D environments. In the education context, Hsu (2012) uses theSecond Life 3D virtual environment for making eight students train
in tourism situations within this environment with considerably
less cost than training in real scenarios. In the e-Marketplace con-
text, the work of Gärtner, Seidel, Froschauer, and Berger (2010)
provides a mechanism for mapping the gap between software
agents and 3D environments, for allowing agent-mediated
e-Marketplace in immersive 3D virtual environments. On the con-
trary, none of these works is specifically aimed at obtaining an
appropriate set of tourist routes for a given city, as the current
work addresses.
2.4. Discussion
As one can observe in the previous subsections, there are manyrecent works related to the current one especially in the fields of
(1) expert and intelligent systems, (2) MASs and recommender sys-
tems in tourism, and (3) simulators in tourism. In the first field, one
can highlights the existence of several route finders considering
some current location and a desired destination, simulators for
other city aspects such as evacuation on disasters, and a generator
of tourist maps. The second field includes a MAS for ontology align-
ment in tourism, and plenty recommender systems for tourism
routes, travel packages and self-drive paths for tourists. In the third
field, there are simulators that simulate either the influence
between the tourism and the climate, traffic flows, the collab-
orative creation of combined travel packages, or 3D environments
for virtually experiencing tourism.
Nevertheless, in none of these works or any other to the best of author’s knowledge, there is a simulator that simulates how many
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tourist people sign up for tourist routes based on the features of
both routes and tourists. Thus, the current work is the first one that
proposes this kind of simulator.
3. ABSTUR: an Agent-based Simulator for Tourist Urban Routes
The presentation of ABSTUR has been divided into several
subsections. Section 3.1 introduces the specification of ABSTUR expressed with the Ingenias modeling language. Section 3.2 pre-
sents the adaptation framework that makes ABSTUR efficient.
Section 3.3 describes the simulator tool of ABSTUR and its related
web application.
3.1. Specification of ABSTUR with Ingenias models
The definitionof ABSTUR containsthree differentroles and seven
agent types for allowing users to simulate the tourists choosing
routes of a particular city. These three roles and seven agent types
are graphically presented in Fig.1 withthe Ingenias notation, along-
side the goals of the ABS. It is worth mentioning that this diagram
uses the -R suffix for roles and the -A suffix for agents, in order to
avoid conflict of names in an abbreviated way. In order to make this
diagram and the following ones understandable, the main concepts
of the Ingenias notations are determined in Fig. 2.
ABSTUR has the following roles with the corresponding agents:
Simulator role: the agent playing this role is in charge of con-
ducting the whole simulation. In particular, the simulator agent
plays this role. This agent provides a GUI, so that the human
expert can configure the parameters of the simulation and exe-
cute it. When the human expert asks this agent to conduct the
experiment, this agent initializes the remaining agents accord-
ing to the established parameters, and starts the necessary
interactions to make the other agents run for the simulation.
Tourist role: this role gathers all the agent types that represent
the different kinds of tourists. This role defines the commoninteractions to all the kinds of tourists. These interactions
mainly concern (1) the search for a trip when the simulator
agent asks so, and (2) the selection of a route by communicating
with the route manager agent. The agents of this role represent
both individual people and groups of people. Thus, each agent
can represent a different number of people. This role is played
by the following agent types:
– Single tourist agent : this agent represents a single person that
is interested in visiting the city of the simulation.
Fig. 1. The definition of agents with the Ingenias notation.
Fig. 2. Main concepts of the Ingenias notation.
Fig. 3. Interaction between the simulator agent and the agents playing the Tourist role.
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– Couple tourist agent : this agent impersonates a couple that
plans to travel to the city of the simulation.
– Family babies tourist agent : this agent represents a family
with at least one baby under two years old. The family can
also have other children of whatever age.
– Family tourist agent : this agent conforms a family with at
least one child, and all the children must be above two years
old.– Friends tourist agent : this agent represents a group of several
friends.
Route manager role: this role manages the routes of a city. This
role is the responsible for accessing the routes when an agent
playing the Tourist role requires so. In addition, the agent play-
ing the route manager role provides rank recommendations for
each presented route from one to ten according to the specific
kind of the particular tourist agent.
The simulations are triggered by the user through the GUI. Inthese situations, the simulator agent initializes the tourist agents,
and starts interactions with these. In particular, the interaction
Fig. 4. Interaction between the agents playing the Tourist role and the route manager agent.
Fig. 5. Excerpt of the adaptation framework.
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between the simulator role and the Tourist role is determined in
the diagram of Fig. 3. In this interaction, the simulator agent sends
a broadcast message to all the agents playing the Tourist role for
proposing them to start a trip, by means of the task namedRecommend Tourist Travel.
Each tourist agent determines the desired features of tourist
routes with the Start Manager Interaction task. In particular, within
this task, the agent determines the current position in the city of the tourist group or person that it represents. It also establishes
the desired type of route. Some possible route types are outdoor
walking routes, routes with museums, routes with nature, routes
with art, routes with monasteries, and routes with bicycle. The
agent also indicates the preferred duration of the route in number
of hours. Then, each tourist agent will start a negotiation interac-
tion with the route manager role for obtaining a trip that suits
its preferences and its type of tourist.
The negotiation process is performed through the interaction
between the Tourist role and the route manager role defined in
Fig. 4. In this negotiation, a tourist agent starts looking for a trip
in the corresponding task. The tourist agent sends an interaction
unit (also known as message in other methodologies) with its tour-
ist type and its preferences. The route manager agent collects all
the available routes with their information in the system for the
given city alongside the recommendation ranks of each route for
the corresponding tourist type. The list of routes is sent back to
the tourist agent, by means of the list of routes frame fact.
After this, the tourist agent starts the decision-making process
for selecting a route from the provided list of routes. To begin with,
it considers the distance between its current location and the start
point of each route in the city. It assesses the routes that start in
nearer locations as better. It also considers whether each route
belongs to its desired type (e.g. outdoor, with museums and so
on). It also takes the duration of each route into account in compar-
ison with its desired duration of route. Finally, it also considers the
generic recommendation of a route, ranked from one (lowest) to
ten (highest) of a route, for its specific tourist type (e.g. couple,
family with babies or other). The decision-making of each agent
assigns more importance to some features than others according
to the tourist type. For instance, proximity to start point and route
duration are highly relevant for families with babies. This decision-
making process assesses all the routes, and selects the route that
best suits the preferences and type of the corresponding touristagent.
Then, in the negotiation process, the tourist agent asks the route
manager to book the route for the given number of people that the
tourist agent represents, by means of the Sign Up Route interaction
unit. If there are any vacancies, the manager agent books the route
and confirms the operation to the tourist agent. If there are not any
vacancies, the route manager asks the tourist agent to select
another route. This negotiation process is repeated until the tourist
agent requests a route that has vacancies and consequently both
agents reach an agreement, or until some of the agents stops the
interaction.
After the negotiation process, in case of agreement, the tourist
agent makes note of the booked route, and sends the booking infor-
mation back to the simulator agent, finishing the other interaction
between the simulator agent and the tourist agent that was pre-
viously initiated. In particular, the tourist agent sends (1) basic
data of the route and (2) the number of people that the tourist
agent represents. Both pieces of information are transferred by
means of the Route and NumPeople frame facts.
The simulator agent collects the data of each tourist agent in theCollect Simulation Data task. After several rounds of trips, the sim-
ulator agent extracts the relevant information and presents it to
the user. The number of rounds of trips is one of the parameters
established for the simulation in the GUI by the user.
The complete definition of all the Ingenias diagrams of the ABS
is omitted in this paper for the sake of brevity. From all the
Fig. 6. Excerpt of the particularization of the adaptation framework for tourist routes.
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corresponding Ingenias diagrams, the programming code was
generated by means of the Ingenias Development Kit (IDK)(Gomez-Sanz, Fuentes, Pavón, & García-Magariño, 2008).
3.2. Adaptation framework for extensive tourist simulations
The programming code was generated for the ABS specified in
the previous section by means of the Ingenias Agent Framework
(IAF), which is one of the main IDK plugins. However, due to the
high number of agents that are necessary in these kinds of sim-
ulations, this paper presents an adaptation framework to fasten
the communications between agents. This framework has been
applied in the presented ABS.
In particular, an excerpt of the proposed simulation framework
is presented in Fig. 5. In this framework, the communications of
agents are performed trough Java method calls, instead of usinghigh-consuming messages through the JADE platform. The
Simulation class contains all the agents within a list. All the agents
have the live method, in which they perform their activitiesperiodically. The different kinds of agents are represented with
classes that implement the Agent interface. The communications
are performed through a blackboard represented with a class. In
this manner, all the agents can implicitly communicate among
themselves through the Blackboard class, saving the time and costs
of explicitly resubmitting the information. All the agents can access
to the blackboard through the Singleton pattern (Nguyen, 1998),
which guarantees that there is always a unique object of the
Blackboard class. The attributes and methods of the Blackboard
class are defined taking the specific domain into account. In addi-
tion, the GUI is represented with the MainGUI class. This class has a
reference to a Simulation object. In fact, the GUI interacts with the
user, creates the corresponding simulation, and then runs it.
Finally, it shows the results of the simulation to the user. In fact,this class is recommended to have at least methods for (1) creating
Fig. 7. Loading routes successfully.
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the agents according to the parameters established by the user, (2)
loading the necessary data from a file indicated by the user, (3)
performing the simulation with the corresponding agents and
parameters, (4) showing the results to the user.
An excerpt of the particularization of the aforementioned sim-
ulation framework for ABSTUR is shown in Fig. 6. Since all the tour-
ist agent types have very similar operations except for the tourist
type that is provided to the route manager agent, a unique classis implemented for all the tourist agents, and this class has an attri-
bute that specifies the tourist type from an enumeration type. This
class is called TouristAgent, and also has an attribute for indicating
the number of people each agent represents.
The blackboard allows tourist and route manager agents to
retrieve or establish the information related to the features of each
routes such as start and finish locations, the duration of the route,
its type or types, and the recommendation values for each specific
tourist type.
The route manager agent is responsible for loading the set of
routes from a file, with their identifiers, titles, and recommenda-
tions for different types of tourist. In this loading, the route man-
ager agent also loads other information from a MySql database
for each route by means of its identifier. This information includes
the initial and final locations of each route, its estimated duration,
and the type or types of the route (outdoor walking, with
museums, with monasteries, and so on). Notice that some routes
can have several types. For instance, there are routes that visit
museums and also have art (two types) (e.g. a route with the
Prado museum in Madrid). However, these two types are not the
same, because there are also routes that visit museums without
art, and routes with art that do not visit museums. The route man-ager agent saves the information retrieved from the file and the
database in the blackboard once, and all the later requests from
the tourist agents access this information from the blackboard. In
this way, these requests do not need to access again the database
avoiding consuming time for this, making the simulator efficient.
Additionally, the route manager agent also loads the information
of some hotels with their locations and their number of rooms
from the database, and stores this information in the blackboard
once.
After the negotiation processes, the route manager agent
updates the number of people signed up to each route in the
blackboard, considering the number of people that are represented
by a particular tourist agent. Then, when the simulator agent is
informed that all tourist agents have finished their tourist
Fig. 8. Detection of errors in the loading process.
Fig. 9. Excerpt of file of routes with errors.
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simulation by means of interactions, the simulator agent directly
accesses to the information of people signed up to each route from
the blackboard, without needing to recount all these information
from the corresponding interactions with the tourist agents.
The strategies of tourist agents for selecting routes are imple-
mented within each tourist agent type. However these strategies
need information of routes such as the start location, duration
and types of certain routes previously offered by the route man-ager agent with the corresponding identifiers. Tourist agents can
directly access to this information of particular routes from the
blackboard with the identifiers, without wasting time performing
new interactions with the tourist manager agent, making the sim-
ulator efficient. In addition, the locations of tourist agents are ini-
tially established as the locations of some hotels of the city. This
hotel location is established by asking for an available hotel room
from the blackboard. Then, after following short routes, the loca-
tion of a tourist agent is updated as the final location of the
particular route. After one long route or several short routes, each
tourist agent is assumed to come back to the hotel.
Therefore, although the negotiation process is performed
through interaction between agents, the retrieval of information
is performed through the blackboard. In particular, the tourist
and manager agents interchanges identifiers of routes in the
negotiation, but all the related information of particular routes is
mainly directly taken from the blackboard by the agent that actu-ally needs this information.
Moreover, the GUI has the necessary attributes and methods for
implementing an internal chronometer to measure the elapsed
time of each simulation. The GUI allows users to export the routes
to a web application once an appropriate set of routes is achieved.
For exporting the routes, the WebGenerator class was imple-
mented. This class generates several files into web format from a
list of routes. These files can be directed uploaded to a web appli-
cation, so people can freely access to these routes with the
Fig. 10. Results of the simulation.
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associated recommendations from the web. The web application is
further described in the next section. The RouteComparator class
has been added to compare routes regarding the different tourist
types. The comparator allows the web generator to sort the list
of routes respectively according to the recommendations for the
different tourist types.
3.3. The tourism simulation tool and the web application
ABSTUR is composed of the ABS tool and the web application.
The ABS tool allows users to load a set of routes from a file by
pressing the button titled Load Routes From File in the top-right side
of its graphical interface, which is shown in Fig. 7. In fact, when this
button is pressed, the user can browse which file to load from a file
chooser. After loading the routes, the label just before the bottom
table changes to Loaded Routes for the Simulation, as one can
observe in this figure, and the bottom table displays the loaded
routes.
An example of the format of the routes is shown in Appendix A.
Basically, the first line of the file is used as header, and reminds the
route designers the format and content of each line to represent aroute. Consequently, the parser ignores this first line. After this,
each line represents a route with some recommendations for each
tourist type. Each piece of information is separated by a semicolon,
and will be referred as an argument from this point forward. The
first argument contains the identifier of the route, which identifies
the route so that users can access to all its documentation collected
by the members of the acknowledged research project. This identi-
fier also allows the ABS tool to obtain some information of each
route from a database defined from this documentation of routes.
The second argument is the name of the route. The next five argu-ments must be numbers that determines the suitability of the
route for respectively each tourist type, with the following order:
singles, couples, families without babies, family with babies, and
groups of friends. The header first line is mainly used to remind
this order to route designers.
In some cases, and especially when working with large number
of routes, designers can make some errors in their files. For this
reason, the presented tool provides some assistance for fixing the
files of routes. In particular, if there is any error, the encountered
errors are mentioned in a message dialog. An example of this mes-
sage dialog is presented in Fig. 8. The other routes that do not have
errors are loaded into the tool as one can see in the bottom table of
this figure. For each erroneous route, the message indicates its
identifier and the line number of the route alongside a brief indica-tion of the error kind. In particular, in this example the route with
identifier CHG3 of line 5 has a wrong number of arguments. Figs. 9
shows an excerpt of the file of routes for this example, and one can
see that this routes has one more argument than required (six
instead of five numeric recommendations). The other error is
related to the fact that there is a wrong number format in one of
the arguments of route LF1 in line 13. Specifically, in the file one
can see the e character instead of a number in the fourth numeric
recommendation.
After loading the routes, one can establish certain parameters of
the simulation directly in the tool. All these parameters are located
below the Input of Simulation label. Each parameter must be speci-
fied in a text field. Each text field is next to a label that indicates the
required content. In particular, each designer can specify the num-ber of each type of tourist or group per iteration. Hence, one can set
Fig. 11. Exporting the tunned set of routes for web application.
Fig. 12. Exported files for including these in the web application.
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the number of single tourists, the number of couples, the number
of families with babies, the number of families without babies
and the number of groups of friends. Finally, one can also establish
the number of iterations, which is referred as the number of trips.The simulation applications starts with some default values in
these parameters to facilitate beginners their first steps in the tool,
but these can be altered by the user depending their goals. For
instance, one can set the number of each tourist type expected to
the next year, or who visited the city the last year, as reference val-
ues. The simulation can also be designed for a specific month of the
year (e.g. August or November). The number of trips is commonly
used to obtain values from different iterations and achieve more
reliable results mitigating the singular situations.
After setting the parameters, one can run the simulation by
pressing the Run Simulation button. Fig. 10 presents an example
of the results of the simulation for certain parameters. The bottom
table displays the number of signed people to each route, indicat-
ing its identifier and name. This table is scrollable so the user caninspect all the routes. This information can be used to tune a set of
routes and provide a more balanced set. Alongside these results,
some additional information is presented in the Results of
Simulation label and below it. This information is the time elapsed
during the simulation, the number of agents used in the ABS, the
number of routes, the number of iterations, the number of people
represented by the agents in each iteration, and the number of peo-
ple accumulated in the whole simulation taking into account the
number of people per iteration and the number of iterations.
After each simulation, the route designer can improve their set
of routes with the specific recommendations for each tourist type.
Once the route designer considers that they have an appropriate
set of routes, they can export the routes to be uploaded in the
web application. This exportation is performed by pressing button
labeled as Export Routes for Web Application, as one can observe in
Fig. 11. After pressing the button, the user can select a directory of
their hard drive to store the routes in the format for the web
application. When the exportation is complete, the label before
the bottom table changes to Exported Routes for Web Applicationindicating the number of exported routes, and displaying the
routes in the table, as shown in this figure.
In the directory selected for the exportation, five files are stored
as one can observe in Fig. 12. All these files have all the routes, but
each of these has the routes ordered decreasingly by the recom-
mendations for a specific tourist type. Consequently, each file is
named with the corresponding tourist type.
Moreover, a web application is developed with the PHP
programming language. The aforementioned generated files can
be uploaded in a specific server directory location, and then the
web application automatically considers the new uploaded data.
An example of this web application is freely available from its
website1, and is presented in Fig. 13.
In this web application, the user can select the type of touristgroup they are planning to visit the corresponding city, and press
the Recommend Routes button. Then, the web application recom-
mends routes with ranking values from one (least recommended)
to ten (most recommended) for the specific tourist type, decreas-
ingly ordered according to these ranking values, as presented in
Fig. 14.
Finally, it is worth mentioning that sorting the routes for each
tourist type is a task considered relatively time-consuming, i.e.
with O(N log N) computational cost. The sort operation has been
decided to be performed in the exportation process of the sim-
ulation application instead of in the web application, because the
exportation is considered to be less frequent than the repetitive
queries of the corresponding tourists from the web application.
Fig. 13. Web application for recommending tourist Madrid routes.
1 http://ivangarciamagarino.net23.net/tourism/ (last accessed 02/13/15).
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4. Experimentation of ABSTUR with Madrid routes
Several tourism experts have experienced ABSTUR for preparing
appropriate sets of routes in the historic center of Madrid. In order
to illustrate the current approach with ABSTUR, Section 4.1 pre-
sents a specific simulation case in detail. In addition, Section 4.2
compares the elapsed time in several simulations with ABSTUR
with another ABS that has been recently developed with the same
specifications but without the presented adaptation framework.
4.1. A simulation case
In this simulation case, the tourism experts provided a set of 32
routes in the historic center of Madrid. These routes were attachedto information such as detailed descriptions, the number of stages,
their topics, their historic backgrounds, the related patrimonial ele-
ments, the location of the stages, the beginning and ending places,
the availability of cartography, the designer of each route, the
owner and its grade of implication, the manager entity and its
grade of implication, the main tourist locations, and whether they
need to be integrated in specific schedules. Other information was
also included like for instance whether a specialized guide is neces-
sary, their economical cost, the availability of several languages,
the accessibility, the duration, the length, the transport means,
URLs to some webs that include photos among other things, and
whether there is additional information in social networks. All this
information about each route is omitted in this article for the sake
of brevity. All the routes have identifiers, which allow experts andusers to easily associate the routes of the simulation with all the
mentioned information. Bearing in mind all these data, some
recommendation ranking values were assigned to each route for
the different tourist types. The identifier of the route and its name
were included in a file with all the recommendation values, follow-
ing the format previously described in Section 3.3. The content of
this file is shown in the Appendix A, and this file is used as the
main input part of the simulation alongside the corresponding
database with the data of routes.
Besides the routes, the simulation receives input from other
parameters such as the numbers of tourist types for each iteration,
i.e. the number of singles, the number of couples, the number of
families with babies, the number of families without babies and
the number of groups of friends. In addition, the simulation also
receives input from the number of iterations (referred in the toolas the number of trips). The values of these input parameters are
presented in Table 1 for this simulation case. The selection of these
parameter values is based on the data provided by the Institute of
Tourism of Spain (Government of Spain, 2015).
Fig. 14. Example of recommendation for a given tourist type.
Table 1
Input parameters of the simulation.
Input parameter Value
Number of singles 300
Number of couples 800
Number of families with babies 250
Number of families without babies 600
Number of groups of friends 400
Number of trips (iterations) 1200
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In the simulation application, each family (with or without
babies) has a random number of members from three to six, while
each group of friends has a random number of members from two
to ten.
The tool running this simulation has already been presented in
Fig. 10 of the current article. The number of the people signed up
for each route is presented in Table 2. As one can observe, there
are several routes that are overcrowded with more than 349,000people signed up such as the routes named Retiro and San
Jeronimo, Districts of Rastro and Lavapies: Genuine Madrid, District
of Letters and Gran Via 100 years of History, while the Historic
Madrid in Bicycle route can be non-profitable with only 132,867
people signed up. Some tourism exerts reflected on these results,
in order to select another set of Madrid routes that produces a
more balanced distribution of tourist people.
The elapsed time of this simulation was 3203 ms. The sim-
ulation was composed of 2350 agents. These agents represented
7538 tourist people in each iteration. The tourist agents chose from
32 routes. The simulation ran 1200 iterations. The accumulative
number of all the iterations was 9,045,600 represented people.
This number of people is similar to the number of people that actu-
ally visited Madrid in 2012 (i.e. 9,056,504 people) according to the
corresponding 2012 report provided by the Institute of Tourism of
Spain (Government of Spain, 2012).
In fact, the execution of this simulation is considered to be quite
efficient (more than nine million people in about 3.2 s). The main
reasons are the omission of agent-specific communication plat-
forms such as JADE, the use of only one agent for representing a
group of several people, and the use of the same agent to represent
similar groups of tourist people (e.g. families of four people
without babies) through the different iterations. This simulation
was run in a laptop with a processor Intel Core i7-3612QM
2.1 GHz with Turbo Boost up to 3.1 GHz, 8 GB DDR3 RAM memory,
and a graphics card NVIDIA GeForce 710 M with a 1 GB dedicated
VRAM. This same hardware was also used in the other simulation
experiments presented in the next subsection.
4.2. Comparison of performance
In order to assess the performance of ABSTUR, this has been
compared with another later ABS that has been recently developed
following the same specifications and using a blackboard architec-
ture. In particular, this ABS was developed with the Erlang pro-
gramming language, and used hash tables for representing the
blackboard. This ABS received the same parameters as ABSTUR
such as the number of each type of tourist agents, the set of routes,
and the number of iterations. The output was also the number of
people signed up for each route. This ABS runs when invoking
the corresponding command from the Erlang environment with
the appropriate parameters. Fig. 15 shows an example of execution
of this ABS. This ABS also measures the time, in which the wall
clock time represents the time that was actually consumed for per-forming the simulation.
The performance of ABSTUR was compared with this other ABS
with twelve different configurations. Both ABSTUR and the other
ABS were run in the same hardware, which was detailed in the pre-
vious section. The configurations were obtained departing from a
default setting of 300 single tourists, 800 couples, 250 families
with babies, 600 families, 400 groups of friends, a set of 32 routes,
and 1200 iterations. The twelve configurations have been obtained
maintaining these default values but changing respectively the fol-
lowing parameters:
The number of agents of each tourist type for all tourist types,
with values 200, 400, 600 and 800.
The sets of routes with respectively 10, 20, 30 and 40 routes. The number of iterations, with values 500, 1000, 1500, 2000.
Table 3 presents the results of this comparison of performance.
The execution times are expressed with milliseconds (ms). For
each simulation configuration, the ratio among execution times
is calculated, and is denoted as the factor of improvement. This fac-
tor is the division of the execution time of the other ABS between
the execution time of ABSTUR. As one can observe, the average of
these improvement factors is 8.99 for the twelve pairs of tests.
This suggests that ABSTUR is about nine times faster in average
than the other ABS developed with the same specifications but
without the presented adaptation framework.
5. Conclusions and future work
In comparison to the existing expert and intelligent systems to
the best author’s knowledge, ABSTUR is the first simulator that
simulates how many people sign up for each tourist route from a
given set of routes and tourists of certain types, considering both
the characteristics of routes and the types of tourists with their
preferences.
This novel simulator has allowed tourism experts to simulate
the implications of different sets of routes for certain numbers
and types of tourists. In this way, ABSTUR has assisted tourism
experts in designing an appropriate sets of routes for the historic
center of Madrid, considering their implications about how many
people will probably sign up for each route. In this manner, they
were able to avoid both overcrowded tourist routes and non-profitable routes.
Table 2
Results of the simulation: the people signed up for each route.
Id. of
route
Id. of route People signed
up
CHG1 Barroco 275146
CHG2 Palaces and Monasteries 323209
CHG3 Restaurant and something more. All the tastes 252573
CHG4 Historic Fonts 322179
CHG5 Churches and Singulars 274044
CGH6 Promenade of Madrid Art 330454
CHG7 Goya in Madrid 1 332213
CHG8 Goya in Madrid 4 285232
CHG9 Ma riano Benlliure. M adrid itinerarie s 300519
CHG10 Contemporary Art 291255
LF1 Literary Madrid 288658
LF2 Ancient Madrid 216586
LF3 Madrid Villa and Corte 276934
LF4 Elegant Madrid 331594
LF5 Retiro and San Jeronimo 349478
LF6 Panoramic visit 307230
LF7 Panoramic visit and Royal Palace 237876
LF8 Panoramic visit and Prado Museum 217696LF9 Panoramic visit and Thyssen-Bornemisza
museum
226381
LF10 Panoramic visit and Reina Sofia museum 266597
AGC1 The Madrid of Austrias 258716
LF11 Night Panoramic 277103
PDJ1 Historic Madrid in Bicycle 132867
PDJ2 Districts of Rastro and Lavapies: Genuine
Madrid
349851
PDJ3 Yesterday and today of Plaza Mayor 264879
PDJ4 Distric t of M arav illa s a nd Conde D uque 301058
PDJ5 Chueca : history , leisure a nd much more 276886
PDJ6 District of Letters 349553
PDJ7 Gran Via 100 years of History 349449
PDJ8 Imagine Madrid(for families) 259356
PDJ9 Madrid Treasures (for families) 261122
PDJ10 Following the illustrious Artist Steps (for
families)
258906
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distributions of tourist loads among different cities and towns of a
given region. In this extension, the results will include a new
column for indicating the city or town of each route, and the appli-
cation will analyze and present the results gathering the tourist
people signed up for the routes of each city and each town, as well
as indicating the people signed for each route. Furthermore, the
proposed adaptation framework for simulations is planned to be
provided in a more automated way, maybe conforming a newplugin for IDK.
Acknowledgments
This work is supported by the project Los Sistemas de
Información Turística como herramientas tecnológicas para incremen-
tar la operatividad tourística de los itinerarios culturales urbanos: El
centro histórico de Madrid como proyecto piloto (SIT-MAD) funded
by the Hergar Foundation with Grant FH-2012-03. This work has
also been done in the context of the project Social Ambient
Assisting Living – Methods (SociAAL), supported by the Spanish
Ministry for Economy and Competitiveness, with Grant TIN2011-
28335-C02-01. This work also acknowledges the Fondo Social
Europeo and the Departamento de Industria e Innovación del
Gobierno de Aragón for their support.
Appendix A. Content of the file with Madrid routes
#Id.;Name;Single;Couple;Family;FamilyBabies;Friends
CHG1;Barroco;10;8;7;2;6
CHG2;Palaces and Monasteries;5;4;10;4;8
CHG3;Restaurant and something more. All the
tastes;8;10;6;1;5
CHG4;Historic Fonts;5;10;6;7;7
CHG5;Churches and Singulars;8;8;6;4;6
CGH6;Promenade of Madrid Art;8;10;6;4;9
CHG7;Goya in Madrid 1;8;10;6;4;9
CHG8;Goya in Madrid 4;5;6;9;1;7
CHG9;Mariano Benlliure. Madrid itineraries;8;6;6;4;9CHG10;Contemporary Art;8;10;6;4;6
LF1;Literary Madrid;8;10;5;4;7
LF2;Ancient Madrid;4;5;6;9;1
LF3;Madrid Villa and Corte;8;7;4;4;9
LF4;Elegant Madrid;8;10;6;4;9
LF5;Retiro and San Jeronimo;5;10;6;7;9
LF6;Panoramic visit;8;10;6;1;9
LF7;Panoramic visit and Royal Palace;8;8;6;1;5
LF8;Panoramic visit and Prado Museum;4;5;6;9;1
LF9;Panoramic visit and Thyssen-Bornemisza
museum;8;10;6;1;3
LF10;Panoramic visit and Reina Sofia museum;8;5;6;1;9
AGC1; The Madrid of Austrias;8;4;6;1;9
LF11;Night Panoramic;10;8;7;2;6PDJ1;Historic Madrid in Bicycle;5;4;4;1;2
PDJ2;Districts of Rastro and Lavapies: Genuine
Madrid;5;10;6;7;9
PDJ3;Yesterday and today of Plaza Mayor;5;3;4;7;9
PDJ4;District of Maravillas and Conde Duque;5;9;6;7;6
PDJ5;Chueca: history, leisure and much more;10;8;7;2;6
PDJ6;District of Letters;5;10;6;7;9
PDJ7;Gran Via 100 years of History;5;10;6;7;9
PDJ8;Imagine Madrid(for families);1;3;10;10;1
PDJ9;Madrid Treasures (for families);1;3;10;10;1
PDJ10;Following the illustrious Artist Steps (for
families);1;3;10;10;1
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