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.
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 [
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 [
This research is inspired by e-Safety Initiative [
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 [
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 = { sk } 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. 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).
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 [
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 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 about order to start goods transportation output chanel
Passes signals about finished goods transportation through “Node” output chanel
Subsystem "Environment"
Subsystem “Junction-Node”
Time Shedules Subsystem “Chain” Gets incoming signals about the complete transportation of certain goods To represents the real environment of multimodal transportation input chanel
input chanel Gets incoming signals about the complete transportation of certain goods through “Chain”
To model of a certain chain 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
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 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 [
The set S = { sk } represents types of scenarios of accident events of transportation which we can to recognize, where k = 1, n .
Following the recommendations of approaches by [
n Βr = ∑ fr Nr, sk P(sk ) (1) k = 1 where fr is the frequency of accident in the r-th road stretch [accident·year-1], Nr, s is k the number of fatalities according to a scenario sk in the r-th road stretch [accident fatalities-1], P(sk ) 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).
The frequency of an accident involving the scenario sk, on the r-th road stretch, can
be expressed as:
fr, sk = fr P(sk ) (3)
fr = γ r Lr nr , where:
γ r = γ 0, rG ,
where γr is the expected frequency on the r-th road stretch
[accident·km-1·vehicle1·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·vehicle1·year-1], according to [
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 = ∏ G j (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), G is a parameter that depends on the metrological factor (such 3 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 Nr, s k is the total number of fatalities according to Eq. (2):
∆t Nr, sk = (Φisnkο vr + Φoskff dr )P(F, sk ) (7) Being the in-road and the off-road number of fatalities calculated, respectively, as: ∆t N inr, sk = Φisn ο
k N off r, sk = Φoff dr P(F , sk ) sk vr P(F , sk ) (4) (5) (8) (9) [m2]; where Φisnk is a consequence of the in-road area associated with scenario sk Φoff 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]; dr is the population density of the r-th road area environment 2 [inhabitants·km ].
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 outcoming channels with "Chain" subsystem, through which the " Junction-Node" passes the out-coming signal about the finished goods transportation through the " JunctionNode" (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
[
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 ∈ P s. Function α s: Ps → [
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 X1 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 . The set of IF-THEN rules forms linguistic description: R1 := IF X1 is Α11 AND ... AND X n is Α1n THEN Y is Β1 (10)
Rm := IF X1 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.
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 [
The client initially obtains its location information from the GPS receiver and from
the physical and virtual sensors [
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 [
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 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). 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 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).
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.
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 realtime 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.
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 = { sk } 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 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.