=Paper=
{{Paper
|id=None
|storemode=property
|title=Multi-Layered Architecture of Decision Support System for Monitoring of Dangerous Good Transportation
|pdfUrl=https://ceur-ws.org/Vol-924/paper13.pdf
|volume=Vol-924
|dblpUrl=https://dblp.org/rec/conf/balt/DzemydieneD12
}}
==Multi-Layered Architecture of Decision Support System for Monitoring of Dangerous Good Transportation==
https://ceur-ws.org/Vol-924/paper13.pdf
128
Multi-Layered Architecture of Decision Support System
for Monitoring of Dangerous Good Transportation
Dale DZEMYDIENEa1 and Ramunas DZINDZALIETAb
a
Mykolas Romeris University, Ateities 20, LT-08303, Vilnius, Lithuania
b
Institute of Mathematics and Informatics, Vilnius University, Akademijos 4, Vilnius, Lithuania
Abstract. The consideration of this study is attached to the representation of
knowledge content of dynamic application domain of transportation related to the
risk evaluation of possible abnormal situations of dangerous good transportation.
Multi-layered conceptual architecture is assembled by the models of knowledge
representation at higher level including conceptual models of information
structures, dynamic process analysis, and problem solving tasks in transportation
processes of dangerous goods. The model represents behavioral analysis of target
system based on Petri nets. The paper presents the technological platform how
aggregate sensor components integrated with mobile technology can support the
on-line processing of real data for localization and monitoring of transport objects
and allow on-line recognition of abnormal situations. The representational
platform describes a general component model that is a basis for expressing
properties of knowledge of domain for informational structure specification.
Keywords. Decision support system (DSS), mobile control system, mobile sensor,
intermodal transportation
Introduction
Over the past few years, a large number of researches had emerged approaching the use
of mobile and other information management technologies in intermodal transportation
area. While there is much literature about the logistic chain analysis [1, 5] and the
intermodal transportation management itself [2, 3, 4] comparatively little has been
written on this subject in relation to mobile sensor based technology implementation in
intermodal container transportation.
Lee and Chan (2009) proposed a RFID-based reverse logistics framework and
introduces genetic algorithm to optimize the locations of collection points for product
returns in order to maximize the coverage of customers, which allow economically and
ecologically reasonable recycling [8]. Jedermann et al. (2006) analyzed new sensor,
communication and software technologies which were used to broaden the facilities of
tracking and tracing systems for food transports, where an embedded assessing unit
detected from sensor data collected by a wireless network potential risks for the freight
quality and estimated the current maturing state of agricultural products which were
supported by measurements of the gaseous hormone ethylene as an indicator for the
ripening processes [9].
This research is inspired by e-Safety Initiative [22, 24]. We can find works related
with hazardous materials transportation analysis [13, 14, 15, 16, 18, 19, 33]. Related
1
Corresponding Author: E-mail: daledz@mruni.eu
D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System… 129
works evaluate sensor readability [6] and integrate UHF RF powered chips by using
sensors for wireless monitoring [5]. Our proposed multi-layered conceptual
architecture assembles the technological platform of aggregating sensor components
which are working by mobile technology and supports the on-line processing of real
data for localization and monitoring of transport objects. The models of knowledge
representation for the on-line recognition of abnormal situations are included in the
decision support system (DSS) under development by conceptual models of
information structures, dynamic process description and decision making tasks.
The representational platform describes a general component model that is a basis
for expressing properties of knowledge by using Petri nets which consideration aimed
at helping in management processes of multi-modal transportation. The analysis of
transportation processes as complex technology has been proposed by means of
imitation modeling [11, 25]. The attention is paid to the representation of dynamic and
static aspects of a target system. The approach of using integrated conceptual models
such as semantic models for representing information structures and fuzzy logic Petri
nets [10] for functional analysis is focused on the consideration of temporal aspects of
domain. However, multimodal transportation conditions, information security and other
risk issues are less analyzed making them the primary objectives of the proposed
transportation management mobile control system. In this paper, we study an emerging
field of intermodal transportation and offer a combined RFID and mobile sensor based
mobile control system, to ensure seamless end-to-end tracking and visibility from
global to local level in intermodal transportation management by evaluating the
potential risks involved in transportation of dangerous goods.
The aims of this research concern the construction of knowledge base for risk
description of transportation dangerous goods and relation it with decision making
deriving actions according to the data from sensors working on-line as the monitoring
subsystem of transport objects. The tasks of this research are:
• to choice the knowledge representation techniques for description of risk
of transportation using recognition mechanism using information about
transport mean mobility and sensor parameters;
• to integrate the risk management component into the decision support of
transportation processes;
• to present the architecture of the decision support system working as
monitoring on-line system using mobile technologies;
• for assuring a high level of information and transportation security, and
improving the efficiency of the communication to upgrade the capability
of the general information system by integrating the mobile interaction
system, using SIP (Session Initialization Protocol).
For the construction of knowledge base we are choosing Petri nets for the
description of imitational model of transportation system. Petri nets are used to
describe decision making processes and SIP communication protocol. For the semantic
representation of data we are used class diagrams based on object oriented model of
UML. For risk representation and possibilities of evaluation the levels of risk we
describe the set S = { s k } of types of scenarios of accident events of transportation
which we can to recognize. Scenarios are described using the probability of evolving of
such type sk of scenarios.
130 D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System…
1. The Architecture of Decision Support System with Embedded Subsystems for
Monitoring and Localization of Moving Objects
The main design principles of DSS framework is presented by means of conceptually
layered framework, with a view to associate the functionality of implemented
components of the subsystems in the existing framework of the DSS (Figure 1). The
system is dividing into various layers. We depict different context models used for
representing, storing and exchanging sensing for contextual information representation
needed for support decisions.
The real-time working subsystems (monitoring of data part) are embedded in the
target system as a concurrent computing system related with the monitoring of sensors
(Figure 1).
Figure 1. Architecture of main components of the DSS
The monitoring subsystem connected with the expert subsystem must detect the
faults of process performance. The time for obtaining a solution is often strictly limited.
These conditions impose strict deadlines on the obtaining a decision and maintaining
the functioning correctness. The system behavior defines a set of temporal
dependencies, dynamic evaluation of situations, adaptively control feedbacks and
complexity management, which must be implemented in the embedded DSS, according
to related works [11, 12, 21]. The system works as a multiple agent based system.
The monitoring component of the system integrates several sensor systems which
observe the transportation means and indicate possible conditions of the state. Such
sensors are aimed at localization of the object, observing the main physical parameters
inside the object, which can characterize multiplex state evaluation. The main types of
sensors are represented in Figure 1. Such data became row data for transforming them
the data warehouses. The metadata represented in the conceptual schema of the
D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System… 131
repository of data warehouses are introduced in our system for a better understanding
of data semantics and contextual information. The extraction transformation loading
(ETL) engine is used for revealing and storing such row data into the data warehouses.
Data mining techniques are introduced in the DSS as the components for extraction of
the main rules and patterns of the situation recognition which can help to integrate
multi-dimensional parameters into decision support processes and control processes of
the accident event situation.
2. Description of Functional Requirements and Risk of Transportation System
The integration possibilities of knowledge representation techniques: semantic model
constructions, macro Petri nets with imitational interpretations of processes are
considered. Such common interaction model is illustrated in Figure 2. The whole
modelling system of multi-modal transportation was divided into three
subsystems: ”Environment”, “Node” and “Chain”. The structural scheme of component
application for multi-modal transportation system is presented in Figure 2.
In West-East and East-West directions
Passes signals Passes signals
about order to start about finished
goods transportation goods transportation
through “Node”
output chanel
output chanel
Time Shedules
Subsystem Subsystem Subsystem
"Environment" “Junction-Node” “Chain”
input chanel input chanel
Gets incoming signals Gets incoming signals
about the complete about the complete
transportation of certain transportation of certain
goods goods through “Chain”
To represents the real environment To model of a certain chain
of multimodal transportation of transportation:
# routes haulage
# rail freightage
# water transportation
To model a certain junction nodes
of transportation route:
# a city
# a border crossing
# a port
# a reloading terminal
Figure 2. The structural scheme of interaction of aggregate components for intermodal transportation
representation
Decision making is performed considering a lot of various factors: evaluating
technical infrastructure of multi-modal transportation and organizational aspects,
comparing reports with the real situation.
The subsystem "Environment" is dedicated to model the real environment of
multi-modal transportation. It has two output channels with the first and the last
junction-nodes of the "Node" subsystem, through which the "Environment" passes the
output signal about the order to start goods transportation accordingly to the West-East
132 D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System…
or East-West direction.
The route is divided into road stretches and each is characterized by different
characteristics. Risk is related with scenarios of accident events, influenced by types of
dangerous goods, and surroundings. The approaches of multiple complex description of
scenarios influence the classification them by types and can be based on the ontology
of this phenomenon. Federal and provincial legislation provide for the regulation of an
extensive list of products, substances or organisms classified as dangerous. The
products fall into one of nine classes: explosive, flammable, radioactive, etc. The
model is focused on evaluation of a proper frequency of accidents, following [15, 16].
The set S = { s k } represents types of scenarios of accident events of
transportation which we can to recognize, where k = 1, n .
Following the recommendations of approaches by [15, 17, 24], the expected
number of fatalities as a consequence of an accident occurred on the road stretch r and
evolving according to a scenario sk, can be expressed as:
n
Βr = ∑ f r N r , s P( sk ) (1)
k =1 k
where fr is the frequency of accident in the r-th road stretch [accident·year-1], N r , s is
k
the number of fatalities according to a scenario sk in the r-th road stretch [accident
fatalities-1], P( s k ) is the probability of evolving scenarios of type sk, following the
accident (i.e. collision; roll-over; failure, etc.).
The transportation network can be considered as a number of junctions (nodes)
linked one to another by a number of arcs (Figure 3).
Figure 3. The example of class diagram for conceptual representation of main parameters of route stretch
characteristics
The junctions represent the cross roads, towns, tool-gates, storage areas, etc. in the
transportation network. An arc between two junctions can be characterized by a
different number of road stretches and the expected number of fatalities for the arc is:
B =∑∑ f N P( s ) (2)
r r , sk k
r sk
D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System… 133
The frequency of an accident involving the scenario sk, on the r-th road stretch, can
be expressed as:
f r , s = f r P ( sk ) (3)
k
f r = γ r Lr nr (4)
, where:
γ =γ G,
r 0, r
(5)
where γr is the expected frequency on the r-th road stretch [accident·km-1·vehicle-
1
·year-1], Lr is the road length [km], nr is the number of vehicles through the road r-th
stretch in [vehicle], γ0,r is the regional accident frequency [accident·km-1·vehicle-
1
·year-1], according to [17].
G is probabilistic parameter, characterized as a common evaluation parameter of
environment. Various factors influence the accident events: environmental, behavioral,
physical, mechanical, Road intrinsic descriptors are described by these parameters.
m
G = ∏ Gj (6)
j =1
where G is the local enhancing/mitigating parameter. The main types of these
parameters we can describe as: G1 is a parameter depending on temperature, G2 is a
parameter that depends on the inherent factor (such as tunnel, bend radii, slope,
height gradient, etc), G3 is a parameter that depends on the metrological factor (such
as snow, sun, rain, ice, etc), G is a parameter that depends on the wind speed and
4
wind direction, and others until such parameter that we can recognize G .
m
N r , s is the total number of fatalities according to Eq. (2):
k
∆t off
N r , s = (Φin
sk ο vr + Φ d r ) P( F , sk ) (7)
k sk
Being the in-road and the off-road number of fatalities calculated, respectively, as:
∆t
N in r , s = Φin
sk ο vr P( F , sk ) (8)
k
off off (9)
N r , sk = Φ s d r P( F , sk )
k
in
where Φ s is a consequence of the in-road area associated with scenario sk
k
[m2];
off
Φ is a consequence of the off-road area associated with scenario sk [km2];
sk
P(F,sk) is a probability of fatality F for accident scenario sk ;
ο ∆t is the average vehicle occupation factor during specific time period ∆t ,
which can depend on the seasons or day time;
vr is the vehicle density on the road area [vehicle·m-2];
134 D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System…
dr is the population density of the r-th road area environment
[inhabitants·km2].
Also the "Environment" has two incoming channels with the first and the last
junction nodes of "Junction-Node" subsystem, through which the "Environment" gets
the incoming signal about the complete transportation of the certain goods. The
subsystem "Junction-Node" is dedicated to model the certain node of the transportation
route, i.e. a port, a city or a border crossing. Each node from such subsystem has out-
coming channels with "Chain" subsystem, through which the " Junction-Node" passes
the out-coming signal about the finished goods transportation through the " Junction-
Node" (goods loading, warehousing, customs).
The results of analysis of attractiveness of the transport system between
forwarding agents showed that the most important evaluation criteria are: transport cost,
reliability and lead time of transportation. The weight of these three factors is varying
among different respondents. It is linked to the nature of cargo being carried and
depends on special requirements of senders and so on. Also the basic cargo
compatibility characteristics must be taken into account while allocating cargo in a
container because the interrelationships between the transport properties of cargos may
result in quality degradation and damage. Different cargo may react with one another
and possibly with their environment. Most changes of the cargo occurring during
transport are unwanted and considered damage. Cargo properties are described by their
characteristic features, specific functions, utility value and its quality and etc., where
transport properties cover the properties of a cargo which need to be taken into account
to ensure value loss-free intermodal container transportation.
The evaluation and selection of route also depends on the type of loads and on the
desirable duration of transportation. Information accumulated in the system should help
to determine technical state and reliability of routes, transportation duration. In order to
select the optimal route of transportation the price of transportation and reliability of
the route play an important role as well.
Reliability of the route is a complex evaluation and it is not easily determined. It
should reflect assurance of load safety, possibility of assault, assurance of freight
delivery in the limits of fixed terms.
3. Representation of Transportation Process Imitational Model using Petri Nets
The computing results of reasoning were obtained by application of logical Petri nets
[10]. Classical Petri nets are defined as a structure N = where S means set of
places, T is set of transitions and F is function of transition works.
F ⊆ (S x T) ∪ (T x S), where ( ∀ t ∈ T) ( ∃ p, q ∈ S)(p, t), (t, q) ∈ F.
Graphical representation of Petri nets is set up by the following symbols: places -
by rings, transitions - by rectangles, and relations – by pointers between transitions and
places or places and transitions. In classical Petri nets, there is a token placed if the
expression is true (1) or not if it is false (0).
Let FLPN=(P,T,F,M0,D,h,α,θ,λ) be a fuzzy logical Petri net. Set of places P0 = {p|
M0 (p)>0^∀p ∈ P} is called a set of places of initial true propositions. D0 corresponding
with P0 is called a set of initial true propositions. Function hs: Ps → Ds is an association
function, representing a bijective mapping from places to propositions. Propositions,
such that hs(p)= hs(p), ∀p ∈ Ps. Function α s: Ps → [0,1] is an association function,
D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System… 135
representing a mapping from places to real values between 0 and 1, such that αs(p)= α
s(p), ∀p ∈ Ps; θs,λs : Ts→ [0,1] are association functions, representing a mapping from
transition to real values between 0 and 1, such that θs (t) = θ (t), λs(t) = λ(t), ∀t ∈ Ts.
The firing rules are the same as in classical Petri nets.
The exceptional feature is the fact that the net transition can represent a sequence
of smaller operations with transition parameters connected with the processes. It is
possible to consider the net as a relation on (E,M0,Ξ,Q,Ψ), where E is a connected set
of locations over a set of permissible transition schemes, E is denoted by a four-tupple:
E=(L,P,R,A), where L is a set of locations, P is the set of peripheral locations, R is a set
of resolution locations, A is a finite, non-empty set of transition declarations; Mo is an
initial marking of a net by tokens; Ξ={ξj} is a set of token parameters; Q is a set of
transition procedures; Ψ is a set of procedures of resolution locations.
The net transition is denoted as ai =(si, t(ai),qi), where si is a transition scheme, t(ai)
is a transition time and qi is a transition procedure. In order to represent the dynamic
aspects of complex processes and their control in changing environment it is
impossible to restrict ourselves on the using only one temporal parameter t(ai) which
describes the delaying of the activity, i.e. the duration of transition. The input locations
Li’ of the transition correspond to the pre-conditions of the activity, and the output
locations Li” correspond to post-conditions of the activity. The complex rules of
transition firing are specified in the procedures of resolution locations Ψ and express
the rules of process determination.
Any IF-THEN rule is given of the form of:
IF X 1 is Α1 AND ... AND X n is Αn THEN Y is Β , where A1,.., An and B are certain
predicates characterizing the variables X1,...,Xn and Y .
Figure 4. An example of description of transportation chain by means of macro-Petri nets
The set of IF-THEN rules forms linguistic description:
R1 := IF X 1 is Α11 AND ... AND X n is Α1n THEN Y is Β1
(10)
Rm := IF X 1 is Αm1 AND ... AND X n is Αmn THEN Y is Βm
where each transition of the result of fuzzy Petri net corresponds to one rule of such
linguistic description.
136 D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System…
4. Integration of Localization and Sensing Information of the Transport Objects
in DSS
Moving objects are constrained by a road network and they are capable to obtain their
positions from an associated GPS receiver [28]. Moving objects (termed as mobile
clients) are recognized by their location information. Location server and the central
data warehouse are in the server site. The relationship is possible via a wireless
communication network [11, 20]. The disconnection between client and server is
realized by other mechanisms in the network than the tracking. The disconnection
occurrences activate mechanisms which notify the server which appropriate actions are
needed. After each update from a moving object, the position is represented in the data
warehouse and the system informs the moving object about the location. The moving
object issues an update when the predicted position deviates by some threshold from
the real position obtained from the GPS receiver [26, 27, 28, 29].
The client initially obtains its location information from the GPS receiver and from
the physical and virtual sensors [30, 31]. This possibility allows collection of the data
from the sensors and processes them on-line. The data of sensor parameters are
exchanged, and then the event eti influence changes in reality. If the data are changed
critically, DSS gets a signal or message. The architecture of these components is
represented in Figure 5 and 6.
To combine the web service protocol, e.g. simple object access protocol (SOAP),
with SIP is very important for securing the communication between server systems and
mobile devices [20, 23]. SOAP can be used on top of SIP or in parallel in the same
layer. SIP is defined to be used only as a signaling protocol in the application layer.
Thus, work is focused on the use of SIP on the control (signaling) plane in parallel of
SOAP on the user plane according to [27].
Figure 5. The scheme of integration of mobile Web services and a SIP user agent
Separation the user and signaling plane has advantages with respect to protocol
design, communication software design, and performance. SIP is used to transmit
“application layer” signaling messages.
In order to communicate between two different mobile devices via Web Service
there must be a mobile web service endpoint. The mobile web service endpoint is a SIP
URI (URI is based on the IP address). In generally, each terminal is able to provide and
use mobile web services (MWS) at the same time and within the same SIP session. The
use of MWS in a P2P manner is possible by establishing a SIP session between the
devices. The MWS endpoints are SIP URIs, the web services endpoints of both clients
are URIs containing the current IP address. First, we need a set of building-block of
D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System… 137
web services. They are common basic web services required by most mobile-service
applications. The MWS and proxies have to register to the SIP agent in order to be
notified about URI (IP address) changes (Figure 6).
Figure 6. Sequence diagram of messaging of the connection session between SIP agents
The user of mobile device must share its physical address with the registrar in the
network. Along with this “registration” is the public identity that is to be bound to the
physical address (Figure 7).
The public URI can change physical addresses many times as a subscriber moves
about the network, so the binding of addresses may change frequently. The connection
of two participators is able to start by sending a SIP INVITE message after starting the
SIP session between two devices (or conference). This session is initialized by request
that enables a virtual connection between two or more entities for exchange of user
data. Registration is not required for the agents using a proxy server for outgoing calls.
It is necessary, however, for an agent to register the receipt of income calls from
proxies.
The sensor’s subsystems are worked as agents in parallel and the important
information is writing on the temporal information registration window (TIRW). The
process control subsystem of the DSS must detect such facts: what the maximum value
was in concrete time interval in surroundings, the number of times a value exceeded a
138 D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System…
predefined reference value (i.e., the limitations of concentrations of harmful materials
in the surrounding, sewerage water, etc.), the temporal delay between the maximum of
a variable, and the maximum effect on another variable (Figure 9).
Figure 7. The Petri nets schema of monitoring processes and connection with SIP
Active RFID tags are also constantly powered, whether in range of a reader or not,
and are therefore able to continuously monitor and record mobile sensor status,
particularly valuable in measuring temperature, humidity and vibration limits, thus they
have the flexibility to remain powered for access and search of larger data spaces, as
well as the ability to transmit longer data packets for simplified data retrieval. Also,
they can power an internal real-time clock and apply an accurate time/date stamp to
each recorded mobile sensor value or event.
The detailed data collected from the tags during intermodal container loadings and
transportation may uncover inefficiencies in established procedures and among
operations strategy elements that could not previously be identified, thus making its
transportation processes more agile and safer and improve the overall quality of the
general intermodal container transportation management information system, therefore,
the efficiency of all transportation operations. Also, automatic tracking of information
is valuable in many service operations: for many applications, it is sufficient to know
that a tag has passed by a reader in a given location. The automatic wireless reading of
multiple RFID tags creates an enormous data flow that is beneficial to the transport
operation management of many transportation services, enabling improvements in the
accuracy of delivery promise, and in the speed of cargo delivery, but hardens the part
of that data analysis. Whereas, in an alert situation the source of the problem can be
defined by some basic predefined business process rules within the basic transportation
management information system, such that if an object passes into or out of a
predefined secure area, or if a problem occurs during a cargo check, then this action
can trigger also other events, processes, e-mail or SMS alerts or report notifications to
D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System… 139
occur automatically. Such safe precaution system would be capable of minimizing the
time spent on cargo checks and would let the system automatically decide when to
bother employees, thus minimizing the rate of errors in the proposed basic information
system in real time manner.
This provides company managers with an up-to-the-minute picture of
transportation processes and activities and that, in turn, allows them to respond to
developing problem situations in a timely manner. Active RFID tags are also constantly
powered, whether in range of a reader or not, and are therefore able to continuously
monitor and record mobile sensor status, particularly valuable in measuring
temperature, humidity and vibration limits, thus they have the flexibility to remain
powered for access and search of larger data spaces, as well as the ability to transmit
longer data packets for simplified data retrieval. Also, they can power an internal real-
time clock and apply an accurate time/date stamp to each recorded mobile sensor value
or event.
The detailed data collected from the tags during intermodal container loadings and
transportation may uncover inefficiencies in established procedures and among
operations strategy elements that could not previously be identified, thus making its
transportation processes more agile and safer and improve the overall quality of the
general intermodal container transportation management information system, therefore,
the efficiency of all transportation operations. Also, automatic tracking of information
is valuable in many service operations: for many applications, it is sufficient to know
that a tag has passed by a reader in a given location. The automatic wireless reading of
multiple RFID tags creates an enormous data flow that is beneficial to the transport
operation management of many transportation services, enabling improvements in the
accuracy of delivery promise, and in the speed of cargo delivery, but hardens the part
of that data analysis. Whereas, in an alert situation the source of the problem can be
defined by some basic predefined business process rules within the basic transportation
management information system, such that if an object passes into or out of a
predefined secure area, or if a problem occurs during a cargo check, then this action
can trigger also other events, processes, e-mail or SMS alerts or report notifications to
occur automatically. Such safe precaution system would be capable of minimizing the
time spent on cargo checks and would let the system automatically decide when to
bother employees, thus minimizing the rate of errors in the proposed basic information
system in real time manner.
Conclusions
An approach for developing the interaction architecture of mobile devices and remote
server systems with additional functionalities for contextual information transmission is
proposed. The choosing of Petri nets allows describing the transportation system by
imitational model and analyzing dynamic properties of this complex system. Petri nets
provide effective formal means for description of decision making processes and
scenarios of SIP communication protocol. For the semantic representation of data we
are used class diagrams based of semantic object oriented model of UML. For risk
representation and possibilities to evaluate the levels of risk we describe the set
S = { s k } of types of scenarios of accident events of transportation which we can to
recognize. Scenarios are described using the probability of evolving of such type of
140 D. Dzemydiene, R. Dzindzalieta / Multi-Layered Architecture of Decision Support System…
scenarios. The proposed context modeling mechanism assures an always up-to-date
context model that contains information on the transport device and location. We offer
mobile internet services to extend the user interaction with architecture. The main
advantage is the extensible architecture so that you can get the data to a mobile devises
through web services. In this way, we try to solve the data integration of heterogeneous
systems and compatibility issues.
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