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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

Contents lists available at ScienceDirect

Expert Systems with Applications

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e s w a

http://dx.doi.org/10.1016/j.eswa.2015.02.023mailto:[email protected]://dx.doi.org/10.1016/j.eswa.2015.02.023http://www.sciencedirect.com/science/journal/09574174http://www.elsevier.com/locate/eswahttp://www.elsevier.com/locate/eswahttp://www.sciencedirect.com/science/journal/09574174http://dx.doi.org/10.1016/j.eswa.2015.02.023mailto:[email protected]://dx.doi.org/10.1016/j.eswa.2015.02.023http://-/?-http://-/?-http://-/?-http://-/?-http://crossmark.crossref.org/dialog/?doi=10.1016/j.eswa.2015.02.023&domain=pdfhttp://-/?-


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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.


http://-/?-


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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).


http://ivangarciamagarino.net23.net/tourism/http://ivangarciamagarino.net23.net/tourism/http://-/?-


12/16

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


http://-/?-http://-/?-


14/16


15/16

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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