Virtual Tourist Hub for Infomobility
Service-Oriented Architecture and Major Components
Alexander Smirnov, Alexey Kashevnik, Nikolay Teslya and Nikolay Shilov
SPIIRAS, 39, 14 Line, St. Petersburg, Russia
Keywords: Tourist, Infomobility, Virtual Hub, Service-Oriented Architecture.
Abstract: Individual travellers are usually restricted in time but wish to see as many attractions as possible. The
proposed approach assumes developing a system for ad hoc generation of travel plans as well as helping
tourists to plan their attraction attending time and excursions depending on the context information about
the current situation in the museums. The major idea of the virtual tourist hub is to arrange transportation
based on the available schedules and capabilities of transportation and attraction providers, current and
foreseen availability and occupancy of the available transportation means and attraction services so that all
information flows are hidden “under the hood” the end-user (travelers), and only the final trip schedule is
seen. The trip schedule is generated based on the context of the current situation and user preferences. The
tourist logistics system members and elements are represented by sets of services provided by them. This
makes it possible to replace the configuration of the logistics system with that of distributed services.
1 INTRODUCTION
Recently, the individual tourism has become more
and more popular. People travel around the world
and visit museums and other places of interests.
They are usually restricted in time but wish to see as
many attractions as possible.
Personal travel via cars, buses and trains is
usually (and reasonably) done within the radius of
450-500 kilometers. The distance between
St. Petersburg, Russia and Helsinki, Finland together
with nearby cities (Imatra, Lappeenranta, Kotka,
Vyborg) falls into this radius. Taking into account
available airports in Helsinki, Lappeenranta, and St.
Petersburg as well as ferries in Helsinki, Kotka, and
St. Petersburg, this region constitutes a universal hub
for travelling all around the world (Figure 1).
In order for this hub to function, an efficient
transportation system within the region has to be
formed. However, today the travelling in the region
is complicated due to a number of reasons, e.g.,
unpredictable situation at border crossing, unknown
traffic condition on the roads, isolation of train, bus,
and airplane schedules. The proposed approach is
aimed at support of dynamic configuration of virtual
multimodal logistics networks based on user
requirements and preferences. The main idea is to
develop models and methods that would enable ad-
hoc configuration of resources for multimodal
logistics. They are planned to be based on dynamic
optimization of the route and transportation means
as well as to take into account user preferences
together with unexpected and unexpressed needs (on
the basis of the profiling technology).
The proposed approach assumes developing a
system for ad hoc generation of travel plans for the
region (the South of Finland and St. Petersburg
region) taking into account the current situation on
the roads and border crossings, fuel management
aspects, travel time and distance. The increase of
travelling will be a significant step towards
development of the integrated economic zone in the
region.
This approach is a step to "infomobility"
infrastructure, i.e. towards operation and service
provision schemes whereby the use and distribution
of dynamic and selected multi-modal information to
the users, both pre-trip and, more importantly, on-
trip, play a fundamental role in attaining higher
traffic and transport efficiency as well as higher
quality levels in travel experience by the users
(Ambrosino et al., 2010).
The other side of the approach is aimed at
helping tourists to plan their attraction attending
time and excursions depending on the context
information about the current situation in the
459
Smirnov A., Kashevnik A., Teslya N. and Shilov N..
Virtual Tourist Hub for Infomobility - Service-Oriented Architecture and Major Components.
DOI: 10.5220/0004447304590466
In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 459-466
ISBN: 978-989-8565-59-4
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: The South of Finland and St. Petersburg region.
museums (amount of visitors around exhibits, closed
exhibits, reconstructions and other) and tourists’
preferences, using their mobile devices.
The idea of virtual hub has already been
mentioned in the literature (though it could have a
different name, e.g., “e-Hub” in (Chang et al., 2003),
but it is still devoted very little attention in the
research community. For example, Working Group
on Logistics and Sweeney (Working Group on
Logistics, 2012); (Sweeney, 2002) consider the
virtual logistic hub from organizational and political
points of view.
Generally, virtual tourist hub represents a virtual
collaboration space for two types of members:
(i) transportation providers (who actually moves the
passengers), (ii) attraction service providers,
(iii) other service providers (who provide additional
services, e.g., sea port, border crossing authorities,
etc.). These providers can potentially collaborate in
order to increase the efficiency of the logistic
network (solid lines in Figure 2), however, it is not
usually the case.
The major idea of the virtual tourist hub is to
arrange transportation based on the available
schedules and capabilities of transportation and
attraction providers, current and foreseen availability
and occupancy of the available transportation means
and attraction services (“dash-dot” lines in Figure 2).
In this case, even though the schedules and actions
of different members are not coordinated, the virtual
tourist hub will be able to find the most feasible
schedule depending on the current situation and its
likely future development. For the end-user
(travelers), all this is hidden “under the hood”, and
only the final trip schedule is seen (solid lines in
Figure 2).
Figure 2: Generic scheme of the virtual logistic hub.
The major scientific challenges of the presented
research include context-aware information
management, multimodal ad-hoc logistic network
configuration, and human-centric cyber-physical
system design.
The paper is structured as follows. Section 2
represents related work in the area of tourist support.
The reference model of the virtual tourist hub for
infomobility is proposed in section 3. The approach
is described in section 4. The case study based on
the developed approach is given in section 5. Major
results are discussed in the conclusion.
2 RELATED WORK
IN THE AREA OF TOURIST
SUPPORT
There is a large amount of research works and
projects related to assisting tourists in attending
various attractions (mainly, museum) and providing
information about exhibitions. The most typical of
Transportation
providers
Attraction
providers
Travellers
Relationship
Reference
ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems
460
them are presented below.
Google Art Project (Proctor, 2011) is a tool from
Google that lets people to visit world’s most
important museums of art, via a virtual tour. The Art
Project is available for more than a thousand works
of art.
The overall objective of the SMARTMUSEUM
project (Kuusik et al., 2009) is to develop a platform
for innovative services enhancing on-site
personalized access to digital cultural heritage
through adaptive and privacy preserving user
profiling.
The main research activity of HIPS project
(Bianchi and Zancanaro, 1999) is for developing of
an approach for navigating artistic physical spaces
(i.e., museums, art exhibitions). The system is meant
to provide the user with personalized information
about the relevant artworks nearby. The information
is mainly in the form of audio in order to let the user
enjoy the artworks rather than interacting with the
tool.
Bohnert et al. (Bohnert et al., 2008) describe a
system for providing a tourist with a challenge of
selecting the interesting exhibits to view within the
available time. It includes the recommendation and
personalization process, i.e., the prediction of the
visitor’s interests and locations in a museum on the
basis of observed behavior.
Kuflik et al. (Kuflik et al., 2010) describe an
approach for supporting users in their ongoing
museum experience, by modeling the visitors,
“remembering” their history and recommending a
plan for future visits. This approach identifies some
of the technical challenges for such personalization,
in terms of the user modeling, ontologies,
infrastructure and generation of personalized
content.
Project CRUMPET (Schmidt-Belz et al., 2003)
has realized a personalized, location-aware tourism
service, implemented as a multi-agent system with a
concept of service mediation and interaction
facilitation. It has had two main objectives: to
implement and trial tourism-related value-added
services for nomadic users across mobile and fixed
networks, and to evaluate agent technology in terms
of user-acceptability, performance and best-practice
as a suitable approach for fast creation of robust,
scalable, seamlessly accessible nomadic services.
The main difference of the proposed approach
from the existing services and solutions is that the
considered systems use attraction information
database or own information database, which has to
be prepared beforehand. This means that sometimes
tourists can get superseded information. These
systems don’t take into account information about
the current situation in the museums and in the
region, and they are oriented to assist user only in
one museum whereas the proposed approach allows
monitoring the current situation in several museums,
its usage for tourist assistance, and context-driven
update the travel plan “on-the-fly”. Also, the
approach presented in the paper allows using
tourist’s mobile device to assist him/her. It is not
needed to provide special equipment for museums.
3 REFERENCE MODEL
OF THE VIRTUAL TOURIST
HUB
The reference model of the proposed approach is
based on the above described components and
shown in Figure 3. The context-aware trip planning
service is in the center of the model. It collaborates
with transportation planning services to find
appropriate transportation means and schedules, and
attraction visit planning services to co-ordinate
transportation and attraction attendance schedules.
These services, in turn, use transport information
services and attraction information services
correspondingly to acquire information about
schedules, availabilities, occupancies, and prices of
the related resources. The latter services can also be
used by the tourist to decide preferable means of
transportation and attractions.
The tourist profile (Figure 4) accumulates and
stores main tourist information in the intelligent
environment. It includes context information and
long-term tourist information. The context
information includes:
Tourist location (to determine nearest attractions);
Current time in the tourist region (to prepare
attractions visiting plan);
Current weather (in case of rain it is better to
attend indoor museums then outdoor);
Traffic situation (for transportation means
suggestion).
The long-term tourist information includes
tourist role and his/her preferences. The role
determines a template for suggesting the tourist
attractions visiting plan (e.g., business, education,
health, adventure, cultural, eco-tourists, leisure, visit
friends and relatives, youth, religious, shopping,
sport). Preferences include the following:
Trip length (to provide suggestions about
attractions visiting);
VirtualTouristHubforInfomobility-Service-OrientedArchitectureandMajorComponents
461
Figure 3: Virtual tourist hub for infomobility: the reference model.
Figure 4: Tourist profile.
Interaction mode with the mobile device (e.g.
textual, audio, and video);
Types of attractions, which are interesting for the
tourist (e.g. Renaissance painting, sculpture of
XIX century);
Preferable attractions (a list of interesting and
“must see” attractions);
Transportations means (the tourist’s preferences
related to the types of vehicles for changing
location, e.g. taxi, ridesharing, public transport).
The trip schedule is generated based on the
context of the current situation provided by the
context management service. It can provide such
information as weather, special events, etc. The
services constitute a so called “intelligent
environment”, which is accessed by the tourist
through his/her computer (when at home) or
smartphone (when in the trip). The tourist’s
preferences are stored in his/her profile and also
taken into account during trip planning.
4 APPROACH
The main idea of the approach (Figure 5) is to
represent the logistics & attraction service providers
by sets of services provided by them. This makes it
possible to replace the configuration of the tourist
logistics system with that of distributed services. For
the purpose of semantic interoperability, the services
are represented by Web-services using the common
notation described by a common ontology. A
detailed overview of the approach can be found in
(Smirnov et al., 2012). The agreement between the
resources and the ontology is expressed through
alignment of the descriptions of the services
modelling the resource functionalities and the
ontology. As a result of the alignment operation the
services get provided with semantics. The operation
of the alignment is supported by a tool that identifies
semantically similar words in the Web-service
descriptions and the ontology. In the proposed
approach the formalism of Object-Oriented
Constraint Networks (OOCN) is used (its detailed
description can be found in (Smirnov et al., 2010)
for knowledge representation in the ontology.
Depending on the problem considered, the
relevant part of the ontology is selected forming an
abstract context. The abstract context is an ontology-
based model embedding the specification of
problems to be solved. It is created by core services
incorporated in the environment. When the abstract
context is filled with values from the sources, an
operational context (formalized description of the
current situation) is built. The operational context is
Long-term
context
Tourist Profile
Long-Term Information Context Information
Location
Weather
Role
Preferences
Trip length
Types of attractions
Traffic situation
Preferable attractions
Interaction mode
Time
Transportation means
In
t
elligent environment
Trip planning
service
Context management
service
Transportation
planning services
Transportation
planning services
Transportation
planning services
Attraction visit
planning services
Places of interest
database
Transportation
planning services
Transport
information services
Transportation
planning services
Attraction
information services
Profile
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Figure 5: Generic scheme of the approach.
an instantiated abstract context and the real-time
picture of the current situation. Producing the
operational context is one of the purposes of
resource configuration. Since the resources are
represented by sets of services, the configuration of
the resources is replaced with that between the
appropriate services. Besides the operational context
producing, the services are purposed to solve
problems specified in the abstract context and to get
the resources to take part in the trip plan. Due to the
usage of the OOCN formalism the operational
context represents the constraint satisfaction
problem that is used during organisation of services
for a particular task.
It can be guessed that for each particular
situation there can be a large amount of feasible
solutions for the users to choose from (e.g., the
fastest transportation, the least amount of transfers,
sightseeing routes, etc.). As a result, the paper
proposes to build such a system as a group
recommendation system that learns user preferences
and recommends solutions, which better meet those
preferences.
The overall scenario of the tourist hub usage is
shown in Figure 6. Before the trip the tourist
configures the preliminary plan consisting of the list
of attractions the he/she would prefer to visit, and
gets information about specifics of the
country/region of the trip.
During the trip the tourists gets updates of the
actual trip plan and movement directions. If there is
no appropriate public transport available, the
corresponding service can call for a taxi.
After the trip the tourist can leave his/her
feedback and comments regarding the trip in social
networks.
5 CASE STUDY
The prototype of the virtual tourist hub has been
implemented based on the proposed approach.
Maemo 5 OS-based devices (Nokia N900) and
Python language are used for implementation.
An open source software platform (Smart-M3)
(Honkola et al., 2010) that aims at providing a
Semantic Web information sharing infrastructure
between software entities and devices is used for
system implementation. In this platform the
ontology is represented via RDF triples (more than
1000 triples). Communication between software
entities is developed via Smart Space Access
Protocol (SSAP) (Honkola et al., 2010).
Different entities of the system are interacting
with each other through the smart environment using
the ontology. Each device has a part of this ontology
and after connecting to smart environment it shares a
part of the own ontology with the smart
environment.
The system has been partly implemented in the
Museum of Karl May Gymnasium History
(Gymnasium of Karl May, 2012) located in St.
Petersburg Institute for Informatics and Automation
Tourist Logistics
Network Model
Service
Web-service
interface
Ontology Abstract context Operational context
Problem solving
Organization of
distributed
services
Relationship
Correspondence
Reference
Information flow
Web-service network
Tourist Logistics
Network
Service provider Service
Logistics network
configuration
VirtualTouristHubforInfomobility-Service-OrientedArchitectureandMajorComponents
463
Figure 6: Tourist hub usage scenario.
Figure 7: A smaple of museum attending plan in a visitor mobile device in the center of St. Petersburg.
Russian Academy of Science building.
The tourist downloads software for getting
intelligent tourist support. Installation of this
software takes few minutes depending on operating
system of mobile device (at the moment only
Maemo 5 OS is supported). When the tourist runs
the system for the first time the profile has to be
completed. This procedure takes not more than 10
minutes. The visitor can fill the profile or can use a
default profile. In case of default profile the system
can not propose preferred exhibitions to the visitor.
Response time of the Internet services depends
on the Internet connection speed in the museum,
number of people connected to the network, and
workload of the services. Average response time
should not exceed one second.
A museum attending plan is presented in Figure
7. It consists of five museums: the Hermitage,
Kunstkamera, the Museum of Karl May Gymnasium
History, St. Isaac Cathedral, Dostoevsky museum.
The Hermitage Kunstkamera
Dostoevsky Museum
St. Isaac Cathedral
Museum of History of
Karl May Gymnasium
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6 CONCLUSIONS AND FUTURE
WORK
The paper presents an approach to development of
service-based system for virtual tourist hub. Virtual
tourist hub performs ad-hoc transportation
scheduling based on the available schedules, current
and foreseen availability and occupancy of the
transportation means and services even though they
do not cooperate with each other. It also helps
tourists to plan their attraction attending time and
excursions depending on the context information
about the current situation in the museums (amount
of visitors around exhibits, closed exhibits,
reconstructions and other) and tourists’ preferences,
using their mobile devices. User profiles allow
keeping important information about the visitor and
using it in the smart environment.
The future work is aimed at implementation of
the proposed system as well as adding features of
group recommending systems. Generation of
feasible trip plans taking account explicit and tacit
preferences requires strong IT-based support of
decision making so that the preferences from
multiple users (accumulated in the system and/or
obtained from social networks) could be taken into
account (McCarthy et al., 2006); (Wang et al.,
2012); (Zhang et al., 2012). Group recommending
systems are aimed to solve this problem.
Recommendation / recommending / recommender
systems have been widely used in the Internet for
suggesting products, activities, etc. for a single user
considering his/her interests and tastes (Garcia et al.,
2009), in various business applications (e.g.,
Hornung et al., 2009; Zhena et al., 2009) as well as
in product development (e.g., Moon et al., 2009,
Chen et al., 2010).
The preference revealing can be interpreted as
identification of patterns of the solution selection
(decision) by a user from a generated set of
solutions. The ability to automatically identify
patterns of the solution selection allows to sort the
set of solutions, so that the most relevant (to user
needs) solutions would be in the top of the list of
solutions presented to the user.
Currently, three major tasks of identification of
user preferences can be selected:
1. Identification of user preferences based on
solutions generated for the same context. In this
case, the problem structure is always the same,
however its parameters may differ.
2. Identification of user preferences based on
solutions generated for similar contexts. This
task is more complex then the first one since
structures of the problem are partially different.
3. Identification of user preferences in terms of
optimization parameters. This task tries to
identify if a user tends to select solutions with
minimal or maximal values of certain parameters
(e.g., time minimization) or their aggregation.
Based on the identified user groups, the user
preferences can be revealed as common preferences
of the users from the same group.
ACKNOWLEDGEMENTS
Some parts of the research were carried out under
projects funded by grants # 13-01-00286-a, # 13-07-
00271-a, # 12-07-00298-a, # 13-07-00336-a, of the
Russian Foundation for Basic Research, project #
213 of the research program “Intelligent information
technologies, mathematical modeling, system
analysis and automation” of the Russian Academy of
Sciences; and project 2.2 of the Nano- &
Information Technologies Branch of the Russian
Academy of Sciences.
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