WO2025046266A1 - Method and smart service system for cargo transportation management with context recognition abilities using a conceptual model - Google Patents

Abstract

The conceptual model of service provision system that incorporates context recognition and operational management of cargo transportation is presented. The system tackles issues such as interoperability among wireless communication networks, sensors, embedded systems (GPS, GPRS, GIS), IoT, and distributed data warehouses, all while facilitating multi-dimensional decision-making. The objective is to establish a conceptual model of the context-aware architecture for a smart service provision system, aiding in e-management decisions during unforeseen circumstances. This invention introduces an innovative conceptual model to monitor geographical positions of transport vehicles, ensuring appropriate services and rapid decision-making for risky situations. The proposed conceptual model of the Smart-CASPS architecture stands out for its novel component-based system using the computer- based cargo transportation ontology. Through real-time algorithms, it offers on-the-fly service provision and route re-planning to address safety concerns. This system, equipped with big data ontology-driven hubs, efficiently manages data from various sources, fostering adaptability, sustainability, and safety compliance in cargo transportation.

Description

METHOD AND SMART SERVICE SYSTEM FOR CARGO TRANSPORTATION MANAGEMENT WITH CONTEXT RECOGNITION ABILITIES USING A CONCEPTUAL

MODEL

TECHNICAL FIELD

The present invention belongs to the field of intelligent (smart) service provision system development for cargo ground transportation management including context recognition functionality. More specifically, it discloses a conceptual model of the system based on some models for context recognition and multi-dimensional decision making by influencing operative service provision under uncertain and risky situations in ground cargo transportation processes, which are recognized by context information analysis.

BACKGROUND ART

Highlights of the proposed invention

An approach of developing the conceptual model of the smart context aware service provision system (Smart-CASPS) with the deep inside into componential structure of modules and knowledge base is proposed for representing the more optimal management of cargo transportation processes under recognition of uncertain situations of context and risks.

This approach is closely related with the topology of communication networks based on the Internet of Things (loT) technology and other embedded systems (like GPS, GPRS, GIS, WSNs) techniques that will enable such services. The approach shortly describes the infrastructure of needful techniques. The means for the description conceptual model contains: the structures of the knowledge base and data, activity models, decision-making models by using notations for design of class diagrams, use-case diagrams, activity diagrams, component diagrams, package diagrams and workflow diagrams, following the requirements of Unified Modeling Language (UML).

The ontology for expressing the complexity of cargo transport processes and context-aware information recognition is proposed. The rules for the operational management of situations are considered, like network infrastructure loads, load levels, the importance of risky context information extracted from the environment and cooperative devices. The design of conceptual model of the Smart context-aware service provision system (Smart-CASPS) demonstrate new opportunities of developing of componential architecture and appropriate knowledge base of system with design of algorithms of provision of smart services for cargo transport management under determining of risky and unsuspected situations.

The knowledge base management models for providing smart services for ground-based cargo (freight) transportation enable the most optimal routes, driver adaptation, and more safety conditions for transportation traffic. The description of possible scenarios for implementing smart services by defining and assembling the required parameters for environmental monitoring on the semantical models of knowledge base, assessing the adequacy and validity of all integrated sub-systems.

There are known some patent documents, which are related with such area of transportation management, but disclosing different inventions and solutions in this technical field according to developing methods of similar systems.

The European patent EP3066767B 1 discloses techniques for providing hybrid communications to devices on vehicles include using a forward link to deliver data, that is intended to be received by an on-board device, onto a vehicle, and using a reverse link in a different frequency band to send reverse data from the vehicle. Forward data may be multiplexed and/or multicast, and in some cases, multiple forward links may be used for distributed forward data delivery. These techniques allow the method for efficient data delivery to the vehicle, and while the vehicle is in transit and link conditions are dynamic. However, further it is important how the transferred data is processed, and further management decisions taken for routing and control of the cargo vehicles. This patent lacks these methodologies and system means for efficient cargo transportation control.

The international PCT patent application W02006128309A1 discloses a method for collecting and managing information related to the shipping and condition of a cargo during a cargo voyage from a source to a destination is provided. An electronic cargo voyage folder (ECVF) associated with the cargo is created, including a cargo ID, cargo contents and shipping methods for the cargo voyage. At the source a first assessment of an initial cargo condition is requested and the first assessment of the cargo condition, a first place of assessment and a first time of assessment is recorded in the ECVF. The events occurring during the cargo voyage are also recorded in the ECVF. At destination, a second assessment of the cargo condition is requested, and a second time of assessment is recorded in the ECVF. This application describes the methods of observing the situations of cargo traffic management by recording various data, use the recorded data for further management of cargo transportation and logistics, but lacks broadening description of context and does not analyze the uncertain situations and replanning conditions of transportation.

The US patent US10600022B2 discloses a synchronized delivery system for delivering parcels directly to an alternate delivery location such as a locker bank in line of making any delivery attempt at a primary delivery location such as a home or office. The system may deliver parcels directly to the alternate delivery location when a related parcel is currently stored at the alternate delivery location awaiting pickup. A related parcel may include a parcel addressed to the same consignee, to a related consignee (e.g., such as a neighbor, roommate, or spouse), or to another authorized to pick up parcels on behalf of the consignee. When delivering parcels to alternate delivery locations, the system may facilitate a grouping of related parcels in a single locker. The conceptual and technical solution herein described lacks integration of data from various sensors and different transport systems. Another US patent US 11562318B2 by UPS America discloses a system for redirecting a parcel from a primary delivery location (e.g., a residential address) to a locker bank is configured to redirect the parcel following an unsuccessful delivery attempt at the primary delivery location. In some embodiments, the system is configured to determine a suitable locker bank to which to redirect the parcel based on preferences received from a carrier (e.g., common carrier), shipper of the parcel, or consignee of the parcel. In various embodiments, following the unsuccessful delivery attempt, the system is configured to receive a request to deliver the parcel to the locker bank, provide access to one or more lockers at the locker bank for placement of the parcel, receive confirmation that the parcel has been placed in a particular locker at the locker bank, and associate the parcel with the locker. The conceptual and technical solution described lacks various smart services provision components, which are important during recognition of situations when monitoring data are analyzed and their integration for multi-purpose cases are omitted.

One more US patent application US20130311393A1 discloses a method and e- system is provided for managing a commodity shipment or container shipment. A Statement of Fact (SOF) is maintained on the e-system; and e- access is allowed to the SOF by one or more of a plurality of parties involved in the shipment using data communication. A contract for the commodity shipment may be formulated and stored on the base of received information from one of the parties. A laytime calculation may be made using information from the SOF and optionally from the contract. The conceptual and technical solution described lacks various smart service application possibilities, which are important for cargo transportation management during contextual information recognition.

The above overview closest prior art documents disclose various cargo transportation and logistics management systems. However, none of these disclosed conceptual ant technical solutions mention in a particular Smart-CASPS structures for cargo transportation problem solving or having some unique specific features, providing the certain beneficial effects in recognition structure development for context and uncertainty situation, which are evaluated during operative cargo transportation management processes. SUMMARY OF INVENTION

The main aim of this approach is to propose the development process of design of the conceptual model of infrastructure of the Smart-CASPS that will be adaptable for multi-functional needs of operative management of cargo transportation enabling context recognition.

The proposed invention improves the infrastructure of monitoring and managing the information about transportation processes of cargo on the ground. It does this by bringing together different ways of supervising on cargo, using new methods to understand the data being collected, and using smart analyzing algorithms for making decisions. This help in providing better services and making sure that all transportation process is running smoothly. The conceptual model of the system is implemented by using special models and diagrams to understand how the whole system works, and it uses modern ICT technology to design the best ways to handle different situations. Overall, this invention ensures cargo transportation to be efficient, safer, and adaptable well-managed.

The proposed Smart-CASPS design methodology enhances cargo transportation operational management through decision-making and control activities at all transportation stages. It involves context information retrieval using ICT infrastructure for process monitoring, the algorithms for information evaluation, and diverse ICT resources like onboard units, sensors, wireless networks, and communication channels. The methodology includes methods for multi-criteria decision-making, algorithms for context information limitation and dissemination, and recognition processes through context information analysis algorithms. The cargo transportation cycle comprises six stages, starting with order preparation and ending with information delivery. Challenges in transportation management include green logistics, quality management, and coordination of various processes. The Smart-CASPS methodology uses integrated context- aware systems and ontology-based solutions to address these challenges. Understanding interaction between transport system levels and modeling choices through context-aware service models is crucial. The paper emphasizes the rise of Cooperative Intelligent Transport Systems (C-ITS) for enhancing transportation efficiency, safety, and comfort. The development of the Smart-SPS aligns with these goals.

The objectives are forwarded for developing of the conceptual model (i.e., representation of data structures, processes, architecture of components of packages and decision-making algorithms) of the Smart-CASPS' that work is based on innovative communication network infrastructure.

The innovative issues of the proposed approach for the Smart-CASPS infrastructure development contain the multi-spectral characters and have several aspects:

• the development of the multi-criteria data evaluation methodology is quite new according to expressing the dynamics of ground-transportation processes of cargo by implementing all possible types of monitoring of land transportation means. This methodology enables by: o an analytical and systematic review of the proposed modem multi-criteria assessment methods in research studies, which proposes the applied methods; o the methodology ensures the adequate aspects of providing Smart-CASPS architecture, enabling the assessment of dynamics of transportation processes by including the infrastructure of ICT components; o possibilities of anticipating the most appropriate multi-criteria assessment methods and extending them with new components for inducting new properties in providing methodology by integrating multifunctional methods for description of smart service provision processes working online, ontological view of the analyzed domain and integration of interoperable structure of ICT components in whole system architecture.

• The proposed means for estimating data and flows of operative control of obtained data of ground transportation processes of cargo are important according to: o Presentation of means that combines several approaches that enable cargo monitoring from different equipment and including monitoring data into the Smart-CASPS. o By proposing means for evaluation of relevant data flows by describing concrete cargo transportation with the influence of new components of data flow monitoring and environmental-based context information recognition.

- The invention includes the classification of intelligent service delivery components based on their ability to support optimal wireless network management, and such description effectively is innovative according to: o the study the contemporary needs, which are provided for describing service delivery in the field of the domain of transportation (including multi-modal transportation), and the possibilities of their provision of services using ICT ; o a new approach for smart service classification; o the development process of the computer-based ontology of the domain is realized by describing the main concepts of processes of cargo transport and the context recognition data conceptual structures.

• The novel system architecture is proposes including stages of service provision: indicating monitoring data structures of transportation processes and conceptual models of kinds of measuring of physical indicators and ensuring the sought value of a physical indicators: o the componential view of Smart-SPS integrates the components of developed classification structures and computer-based domain ontology; o applicable techniques of contemporary ICT means are integrated and proposed for their implementation, including Wireless Sensor’s Networks (WSN), Road Side Units (RSU), On Board Units (OBU), GPRS, Geographical information systems (GIS), vehicle to vehicle (V2V) communication infrastructure; o the provided methods enable to define the evaluation criteria for the provision of smart services by proposing a multi-objective evaluation method; o the provided approach allows the creation of the structure for the appropriate prioritization of the provisioning process of possible intelligent services over transport safety and quality of works;

• The developed infrastructure of the Smart-CASPS provision system integrates: o developing of streamlined approach for the intelligent management of cargo transportation by providing multi-dimensional, heterogeneous services, which are enabling by including methods for collection, aggregation, and dissemination of contextual data recognition during the cycles of ground transportation of freights; o description of the conceptual models of static and dynamic processes of smart service provision using the UML, especially by implementing the standardized methodology of design class diagrams, activity diagrams, components diagrams, and other tools. o Implementation of the means of modern ICT that ensure the service proper realization by designing service scenarios and orchestration for the management of cargo transportation processes; • the infrastructure of the provision of smart services enables to assess of the requirements of operational management of freight transportation and contains: o the model of more optimal interaction between wireless computer networks and ground-based vehicles, enabling the selection of mobility scenarios based on real-life situations; o the model for interoperability between wireless networks and ground-based vehicles ICT for identification of the capacity needs and capabilities of existing wireless networks within the roadway infrastructure; o the infrastructure integrates the representation of the topology of communication networks based on the loT technology that will enable the implementation of the computer-based ontology of processes and operational management rules.

• The invention includes the multi-layered structure of the knowledge base, which became novel according to: o development of the appropriate computer-based ontology as semantical model for expressing the complexity of transport processes and context information; o description of the knowledge base with data structure application rules for the operational management of situations. o by implementing the knowledge base management model for providing concrete smart services for management of ground-based freight transportation. The expressed algorithms enable the choosing of most optimal routes, service adaptation for drivers, and providing help for more safety of performances of vehicle traffics; o description of conceptual model of Smart-CASPS by defining and assembling the required parameters for environmental monitoring, conducting and assessing the adequacy and validity of all integrated subsystems.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1. describes structure of main components of architecture for representation of subsystems involved in the infrastructure integrated with the Smart-CASDS;

FIG. 2. describes structure of architecture of subsystems integrated into Smart-CASPS;

FIG. 3. detailed representation of the componential structure of packages of subsystems for service support;

FIG. 4. represents the architecture with components used for the provision of services which enable the identification of different situations;

FIG. 5. shows structure of the system components for Service provider work support;

FIG. 6. presents class diagram for representation of characteristics of roads;

FIG. 7. shows agent model for revealing of transportation conditions based on ontological reasoning algorithm;

FIG. 8 describes main entities and relations for description of the road conditions; FIG. 9. presents main steps of sensing of information and reasoning about conditions of cycle of transportation is performed;

FIG. 10. describes transportation conditions and the road data structure;

FIG. 11. describes conditions of transportation by object class data structure;

FIG. 12. describes the trip data structure;

FIG. 13. describes freight data structure;

FIG. 14. describes inter-modal transportation data structure;

FIG. 15. shows algorithm of evaluation of arrival process at the intermodal terminal;

FIG. 16. presents process diagram of the recommended data transmission and transportation process in multimodal transport

LIST OF ABBREVIATIONS of FIG. 15: f - freight storage conditions, v - freight volume (in pallets), r - vehicle ID, n - terminal ID, c - current used capacity (of terminal cn and vehicle cr), cMAX - maximum capacity, g - gates (inbound gu), s - scan code of freight unit (pallet), e - seal number.

Smart-CASPS - smart context aware service provision system.

DETAILED DESCRIPTION OF THE INVENTION

1. THE METHODOLOGY OF DESIGN OF THE SMART-CASPS

The Smart-CASPS design methodology for cargo transportation during the operational management processes describes the design of the decision-making and control activities during the main stages of transportation management support. For context information retrieving, it is necessary to use the additional ICT infrastructure that enable to use the monitoring data of the processes. The design of conceptual models for monitoring data representation and storage are developed. The evaluation structure of obtained information, which is extracted from the environment (surroundings) is proposed. In the structure of system is included the types of monitoring data, which are realized by implementing possible types of the ICT infrastructure, i.e., data from onboard units (OBUs), roadside units (RSU), equipment of sensors, wireless sensor networks, and communication channels, which enables to connect with distributed and remote data warehouses and as well as, by the implementation of the heterogenic communication channels with the implementation of all possible communication protocols (but this field of ICT infrastructure is not included in the Patent).

The context of cooperating facilities is implemented in the communication infrastructure that is working in real-time conditions. The methods for multi-criteria decision-making are developed and help evaluate the usefulness of obtained and context information. These methods are constructed and integrated into the working algorithms of the Smart-CASPS. The algorithms for limitation and selection of context information are developed. The structures and algorithms for disseminating knowledge among other vehicle nodes have been integrated.

The system's structure integrates contextual models of data storage. The conceptual model of system describes how cooperatively are used the network channel resources in the automotive communication processes.

To understand the context recognition processes, the algorithms of context analysis are developed and integrated into the Smart-CASPS. For the development of context recognition algorithms, we evaluated these factors:

• the defining of the structure of components and architecture of the Smart-CASPS and relations with the operative management structure of transportation of goods (freights);

• defining the set of variables that are important for freight transportation and context-aware service engineering;

• the description of the ontology of used terms of concepts by object classes;

• the description of specifics of context-aware services for support efficiency improvement in the transport system;

• providing the set of methods of data collection, aggregation, and dissemination and their application;

• the representation of decision support for multimodal cargo transportation in Smart-SPS.

The cargo transportation process cycle begins with preparing the order and submitting it to the service provider. Therefore, the entire cargo transportation process can be divided into six stages:

- TRO.l) order preparation and submission;

- TRO.2) order acceptance and verification process to avoid further errors;

- TRO.3) order execution.

- If necessary, the organization of the additional production process is performed:

- TRO.4) preparation and completion of the cargo for delivery and performance of related actions;

- TRO.5) description of transportation activities, which are associated with the appropriate selection and performance of transportation procedures;

- TRO.6) delivery of required information to the recipient (unloading, preparation of documents, etc.).

Given that the first stages are preparatory stages for transportation, the problems of organization and management of the process are usually minimal and can be resolved as quickly and efficiently as possible.

However, the transportation process itself presents many challenges, such as:

• implementation of the concept of green logistics through the interaction of modes of transport;

• the transportation organization system consists of 3 levels: infrastructure, transport flows, and material flows, • between such processes, the quality management of transport processes must be ensured;

• have been enabled coordination of schedules for loading operations, and other participating components, etc.

Managing these challenges requires concrete operational solutions, which can be ensured using integrated context-aware systems. In the structures of data warehouses, online primary data are stored continuously. We need more effective algorithms for processing and managing this large real-time flow of data. The appropriate measures are needed to be able to make the correct, timely decisions. The computer-based ontology helps us in solving this problem.

Therefore, it is important to note that to carry out the freight transportation modeling, it is first of all important to understand how the interaction between the levels of the transport systems take place and how the transportation model is realized, as well as enable to imitate, simulate and analyze the relationships (connections) which are involved in these processes. Most of the time, the levels of the transport system interact with the help of ICT and given that the transport model is defined as a set of mathematical relationships that model choices (possible alternatives) in the transportation of products (cargo), it can be said that these choices must be linked (managed) through the context of smart, aware service models. Usually, a delivery plan model is created, which include: trip generation, trip distribution, model splitting, and trip assignment. It should be noted that route planning depends on parameters such as trip duration, distance, price, comfort, and safety. Based on the delivery plan, it is necessary to perform process execution following the plan or make some plan adjustments (delivery plan modifications in the case of urgency or contingency). Therefore, when modeling the cargo transportation process, it is appropriate to apply a computer ontology that describes all the typical elements, structures, and limitations of such a process that exist during cargo transportation.

The focus is on developing Cooperative Intelligent Transport Systems (C-ITS) is rising. C-ITS are developed to improve the efficiency of transportation, traffic safety, and comfort. Our needs and construction of the Smart-SPS are important and named in the paper [1].

2. ANALYSIS OF REQUIREMENTS FOR THE DEVELOPMENT OF SMART-CASPS

2.1. Analysis of route data essential for service execution.

Information flows are described in detail by analyzing the transportation cycles of cargo (freights). Before starting the cargo transportation process, the customer must provide the following information to the carrier.

For the cargo to be transported successfully, the customer must provide detailed information related to the cargo itself and its characteristics, as well as the destination and time. Considering the scheme, it could be detailed in such a way that important information in the process is associated with other important parameters:

2.1.1 analysis of load characteristics.

The main loading information that is important in the loading process stage is: 2.1.1.1.1 what kind of cargo, its weight, and how many units are in transport mean? The carrier needs to know this information to ensure the safe loading and securing of the cargo, as well as compliance with all necessary conditions during transportation.

2.1.1.1.2 Additional information: If there are any special technical requirements for loading, fastening, and transportation. It is usually presented only in exceptional cases.

2.1.2 Description of destinations and time.

A description of destinations and time are important:

2.1.2.1 the place of loading and sometimes even the coordinates are indicated (if it is partial loads, then several places) necessary information for all carriers;

2.1.2.2 date of loading (if these are partial loads, then several dates) necessary information for the carrier in all cases;

2.1.2.3 loading time (if it is partial loads, then several times) necessary information for the carrier in all cases

2.1.2.4 if multi-modal/intermodal transportation is carried out, indicate the terminal, date, and time of arrival

2.1.2.5 if the route takes place through several countries, it may be indicated where to drive through. This practice is applied in exceptional cases (for example, when transporting very expensive cargo), then the customer can provide a route and a list of countries through which it is possible to travel through.

2.1.2.6 The place(s) of unloading and sometimes even the coordinates are indicated as necessary information for the carrier in all cases. It may not be provided in the order (for example, only the city), but the sender must provide it. The manager must inform the driver about the unloading address. If he does not have one, he waits for the cargo to be loaded and forms a message to the driver from the received documents.

2.1.2.7 The date(s) of unloading is always given to the driver. In some cases, the time (if it is important, e.g., FIX TERMIN);

2.1.2.8 time of discharge If time is important, the date and time are given, but more often, only the date is given.

2.1.3. Additional information.

It is also noted that the driver must follow the mode of work and rest. In exceptional cases, possible resting places are provided, but drivers often stop places based on their assessment of the current situation and their driving/total times, refueling - provided by managers, border crossing points, where and what documents must be presented, and left. - managers submit. Places/countries of customs clearance and customs clearance are also indicated. 3 DESIGN OF COMPONENTIAL ARCHITECTURE OF THE SMART-CASPS FOR CARGO TRANSPORTATION OPERATIONAL MANAGEMENT PROCESSES

The description of components of the architecture of the main subsystems integrated into our proposed Smart-CASPS is proposed from a more abstract representation style to a more detailed description of components.

3.1 Design of the main componential architecture of the Smart-CASPS.

The conceptual model of the main components of the Smart CASPS architecture is presented in Fig. 1. The structure of system architecture (represented in Fig. 1) includes such main components:

1. Subsystem of Context Data Acquisition and Dissemination - the system responsible for data monitoring and data recording from sensors in transport means and surroundings. Subsystem 1 contains other subsystems:

1.1. subsystem for Monitoring Local Data from Sensors in Vehicles (InV);

1.2. subsystem for Monitoring and Processing Data from RSU, i.e., from roadside units with the set of interfaces as communication enables;

1.3 subsystem for Monitoring Data from Sensors in Trans-shipment Terminals - it is a special monitoring subsystem based on equipment and networks of sensors established on multi-modal transportation trans-shipment terminals;

1.4 subsystem for Obtaining Data from V2V Communication - i.e., the component as the package responsible for receiving, obtaining, and processing data from other vehicles, by including V2V Communication, creating Ad-Hoc communication networks;

1.5 repository of DWs - is the structure of repository of data warehouses (DWs), that responds to the structure of managing repository by expressing meta-models of conceptual models for semantical expression of control of the structure of the data ware houses;

1.6 DW for Monitoring data - is the data warehouses (DWs) devoted to storing and obtaining monitoring data. The infrastructure of such data warehouses is constructed as a set of distributed and interrelated data warehouses for recording primary data about surroundings and transport objects;

2. subsystem for Freight Transport Control - is the system that provides all needful e- documents and enables the control of flows of freights during all cycles of transportation and contains the subsystems involved in the architecture:

2.1. Transport Planning Subsystem - is the system that enables planning activities of transportation cycles;

2.2. subsystem for Traffic Routing and Scheduling - is the system that responds about traffics, routes, and scheduling activities of logistics and transportation;

2.3. subsystem of Processing & Transmission of e-Documents - is the system that prepares the e- documents and enables communication channels and protocols for e-documents transmission that accompany the cargo during the entire transportation; 2.4. multi-Criteria Decision Support Subsystem for Control is composed of methods used for multicriteria decision-making and support.

3. The Core of Smart-CASPS is the system's main component. The main functions of Smart-CASPS are:

3.1. to obtain the data from recorded data sources (DWs);

3.2. to select the algorithms (methods) for recognition of situations;

3.3. to select the concrete decision according to the recognized situation;

3.4. to deliver the appropriate service for users according to the selected decision.

In the architecture are involved the set of interfaces with different devices:

4. the set of interfaces with sensors in the local Environment of the vehicle - InV - is the set of interfaces with sensors that are placed in vehicles;

5. the set of interfaces with RSUs - is the set of interfaces of road side units. The roadside unis can be the sensors placed on roads, which are included in infrastructure for monitoring and provision of data (information) about transportation surrounding;

6. the set of communication channels with the communication infrastructure of V2V and the set of interfaces of Ad-Hoc communication;

7. the set of interfaces with sensors in trans-shipment terminals or good stores.

The data from the sensors are recorded in data warehouses (1.6).

The detailing of components, which are integrated into the architecture of the Smart-CASPS (3) are presented in Fig. 1.

The structure of architecture of the Core of Smart - CASPS (Fig. 2) represents the data flow between the system's components in a more detailed style. The detailed structure includes the flow of data between the set of components needed for the infrastructure realization of the whole system, and serves as packages, which are included as an extension of the components plugged into the system. The extension of components contains the items for recording and monitoring data from transportation processes and enables the provision of distributed services for different types of users. The architecture of the flow of data between the subsystem include: the Subsystem of Context Data Acquisition and Dissemination is separated into two parts:

1.1. the Context Data Acquisition Subsystem (S-CDA - this part is designed to represent the process of flowing data from parts 1.1.1-1.2.7 represented in Fig. 1). The main incoming data which are obtaining from different sensors flow into the Context Data Acquisition Subsystem - this is the main subsystem for processing data from Physical sensors, and it contains: obtaining data from Physical Sensors Inside of Vehicle (interfaces with obtaining equipment for data gathering are described by 4 Components in Fig. 2); obtaining data from Physical Sensors Outside of Vehicle (5-7 Components in Fig. 1); Such data flow to the component with pre-processing stage in the package named the Data Processing and Noise Reduction Subsystem and, after that, to the Data Warehouse of Monitoring Sensors (1.6 Component in Fig. 1). l.II. The Context Data Dissemination Management Subsystem is devoted to obtaining data from Components like: l.II.1. Comfort Information DB;

1.11.2. Safety Information DB; the information and data structures from such components flow to DB Management System in Vehicle for recognition and evaluation according to the estimation of priority for provision of them.

The part of l.III. Interfaces is devoted to obtaining the data from 1.1 part and l.II part of the system. The Interfaces contain the important components:

1.III.1. interfaces for Acceptance of Data from Sensors;

1.111.2. Service Provision Subsystem;

1.111.3. Provider of Context Data Messages;

1.111.4. Service of Support User Interfaces. l.IV. The Context Evaluation Utility Subsystem enables the obtaining data from part of l.III. and contains the components: l.IV.l. Recognition Subsystem of Sensor’s Data;

1.IV.2. Identifier of Collisions - this component is needful for the identification and recognition of different conditions of collisions of sending process of data; l.IV.3. Sent Data Packets- this component enables to save of the data with needful attributes which were sent for users;

1.IV.4. Reject Data Packets- this component enables to save the data with needful attributes which were not sent for users;

1.IV.5. Data Tough out;

1.IV.6. Compilation Subsystem of Context Data.

The data streams from such components of the package “Context Evaluation Utility Subsystem” (l.IV.) are flowing for the next processing stages, which enable the components:

1.IV.7. Cluster Identification Subsystem- is the subsystem responsible for the cauterization of messengers; l.IV.8. Management Subsystem of Channel’s Quality;

1.IV.9. Utility of Evaluation of Context Data.

The separate modules of the system (S-CDA, D-CDA etc.) are included in the whole structure of the system: the components and data flows of working packages of 1.1. The Subsystem for Context Data Acquisition (S-CDA) - is represented in Fig. 3. The detailing of components of S-CDA (1.1.) is designed for understanding how such component work and how obtaining data streams from sensors in the vehicle environment (In-V) and from sensors are implemented in the areas outside of vehicle environment (Out- V);

1.6 components in Fig. 1: The Data Warehouses (DWs) are used as big data stories for recording (monitoring) data from sensors implementing data cloud technology; l.II. the Subsystem for Context Data Dissemination (S-CDD) is designed for the assessment of data and distribution; l.III. The Interfaces for acceptance of data from sensors of heterogeneous equipment; l.IV. the subsystem for realizing the Utility of Context data Evaluation (S-CDEU).

The detailing of the componential structure of packages of the subsystem for the Service support component includes such components: the Service Cloud - is the component of all infrastructure that is constructed as the data storage places in the remote host machine, designed like the data warehouse (DW) of all service storage structures, provision scenarios and the Repository of DW as the meta-model of such DW structure.

3.2 Classification of Services for Selection of Priorities of Provision of Services in the

Smart-CASPS; the classification of heterogeneous services, which can be provided for transportation management processes and by all possible means participating in cargo transportation processes and are important in selecting service provision processes from the Smart-CASPS system, is presented in Fig. 4. The vehicle-to-infrastructure (V2I) needs some detailed description, and we follow the recommendations of the EU that focus on the development of Cooperative Intelligent Transport Systems (C-ITS) (Regulation (EU) No 2018/1724).

There are several types of services, which are important for the development of the C-ITS Platform, and we would like to considerate them in detail:

The evaluation structure of services in the Smart-CASPS according to the classification of their importance for the transportation safety and priority of reactions to control are presented in Table 1.

Table 1. Evaluation structure of services for classification realization in Smart-CASPS

The classification of such services is important for extracting from them the ranges of types of importance for the provision of concrete operative control actions according to the safety criteria or rejecting them as not important for control, or important for alarming operative activity. For evaluation and specification of ranges of services into the proposed gradations is implemented by empirical research method by surveying the specialist-experts of transportation. The main parameters are singled out so that separate classes of service provision can be determined, they can be automatically evaluated, and their importance intervals for ensuring the safety of cargo transportation are entered into the system. They are used to provide operational warning actions for automatic decision-making or to transmit information about the priority of the needs of users which are driving other vehicles and the received for setting up services. Such gradation is important for the construction of the proposed algorithms of the Smart-CASPS.

Another step of classification is presented in Table 2. The important parameters and classes of services for specification of gradation classes of services are excluded.

Table 2. Structure of classification of services and ranking.

The implementation covers various aspects, including business and legal aspects, from building an in-vehicle platform to the safety system architecture and the needed infrastructure. Services that provide the provision functions for vehicles are grouped into the clusters. The development of new kinds of services and technology platforms can support the relations and communication of vehicles with information infrastructure (V2I) and between vehicles (V2V). The technological platform helps in increasing concrete road safety and management efficiency (C-ITS Platform). The service provision platform includes services, which are named in paper [1]. Evaluation of data transfer parameters for heterogeneous service support in vehicular communication networks are named in [2] .

The sets of primary data flow from sensors are transmitted into the data structures by the classification algorithms. The transferring process is provided in two directions: from the vehicle to the DW server and from the DW server to the vehicle. The main types of sensors, which are needful for covering the spectrum of heterogenic services, are classified in paper [2].

The matrix ML is constructed in paper [2] to evaluate the usefulness of each data message, which can be expressed as a Cartesian product.

3.3 Design of components of Smart-SPS for the provision of services that enable the identification of different situations;

To understand the process of algorithms' work, the architecture of supporting such components for providing services is presented in Fig. 4. The main function of partially defined infrastructure is enabling the identification process of situations and providing a concrete conclusion about the actions which are forwarded for the next steps of management decisions and the concrete messages transmission process of cargo transportation stages (Fig. 4).

The services assigned to the type of comfort and entertainment have to provide concrete information for drivers or passengers related to weather conditions and traffic information management. The components, which are needful for such data retrieving, are integrated with the functionality of some types of embedded systems (like geographical information systems - GIS, etc.). Description of some types of embedded subsystems that are included in the Smart-CASPS:

3.4.1. Geographical information systems (GIS);

3.4.2. The systems with enabled cameras;

3.4.3. The GPRS subsystems.

The embedded subsystems are included with built-in functions:

3.5.1. The functions for vehicle collision alert;

3.5.2. The functions for lane departure warning;

3.5.3. The functions of emergency video broadcast;

3.5.4. The functions of prediction and adverse event warning.

Information for service provision from GIS is obtained about services from (3.4.1):

3. GIS.1. The functions for the location of the nearest restaurants,

3. GIS.2. The functions for the location of gas, electrical, and oil stations;

3. GIS.3. The functions for the location of vehicle parking areas; 3.GIS.4. The functions for the location and conditions of hotels and their prices.

The communication infrastructure enables connecting with the Internet and sending or receiving instant messages when the vehicle is connected to the infrastructure network, as presented in Fig. 4.

Six categories characterize the services, which are described in book [3], which can indicate the safety conditions; traffic monitoring and vehicle engine monitoring data and other data important for management needs.

The class diagram of the main components of computer-based ontology of freight transportation process is modelled with UML class diagram notation and present main inter-related concepts, which are stated in [3] .

Vehicles can support heavier computing capacities, radio frequencies, the large spectrum of sensors In-V, and wireless interfaces with high-speed communication. Analyzing historical statistical data can help recognize certain peak patterns and links between mobile automotive groups. The algorithms for filtering the services are provided and properly managed according to the required contextual data. Context data can be diverse and dynamically changing, making user-initiating service discovery impractical and useless. Service-Oriented Architecture (SO A) has been used to create and implement services. For more information about service discovery methods, please, check book published by [3].

The computer-based ontology of the transport system is helpful for the definition of the concepts used in the transportation domain and helps in the construction of meta-models of the repositories of DWs. A very important part of KB are rules of management of transportation processes. KB is the structure of recognition of dangerous activities in transportation situations, identification of important indications of the alarming situations, and enabling the control with avoidance of unexpected events. The interfaces are designed to arrange additional support in controlling decisions when situations are faced with unsuspected incidents.

The structure of the work of the Smart-SPS with the connection of the knowledge base (KB) is presented in Fig. 5.

4 ANALYSIS AND DESIGN OF SERVICE PROVISION SCENARIOS

Data structure descriptions are designed by using UML standardized diagrams as object classes. For the description of data structures of road characteristics, the schema contains some object classes, which are partially represented in Fig. 6.

For simulation procedures, examples of activity models of intermodal transportation processes with different interconnection possibilities are constructed (Fig. 6). The activity-decision-making model represents the decision-support process of choosing alternative ways of intermodal transportation corridors for cargo transportation (Fig. 8).

The semantic description of components:cl- is the node that represents the starting position of transportation possibilities for the planning of intermodal transportation corridor of cargo; 43. pl - is the node that represents an example of an object of class “Junction node” that can be chosen as the intermodal terminal for trans-shipment of the cargo between Highway road and Sea road types of class from “Road sections”;

44. p2 - is the node that represents an example of an object of class “Junction node” that can be chosen as the intermodal terminal for trans-shipment of the cargo between Sea road and Railway truck of types of objects of class “Road sections”;

45. input i,j,t - the services/obtained information), which are getting in this type of node, where i -is the index represent of node, j is the index represent of cargo, t- is time moment then the cargo arrived in this node;

46. output i,j,t - the services/obtained information, which are provided from this type of node where i -is the index - represent of the concrete node, j is index-represent or of concrete cargo, t- is the time moment when the cargo arrived in this node;

47. possibility of output l,j,t - possible alternatives of choosing ways of Road sections of intermodal transportation;

48. Car_roadk - is the connector that represents an example of an object of class “Road section” - i.e. road of type of highway or road for automobiles;

49. Sea_routen - is the connector that represents an example of an object of class “Road section” of sea water road;

50. Railway _track_m - is the connector that represents an example of an object of class “Road section” of railway road;

51. p3 - is the node that represents an example of an object of class “Junction node” that can be chosen as an intermodal terminal for the trans-shipment of the cargo between Sea road and Automobile car road;

52. c2 is the node that represents an example of an object of class type “Junction node” that can be chosen as an intermodal terminal for trans-shipment of the cargo between Railway_n and Railway,,,;

53. Stages of decision making on choosing the alternative road and transport mode

54. c3 - is the node that represents an example of an object of class type “Junction node” that can be chosen as an intermodal terminal for trans-shipment of the cargo between Automobile_way_n and Automobile_way_m;

55. bl- is the node representing an example of an object of class type “Junction node” that can be chosen as an intermodal terminal for trans-shipment cargo between Automobile_way_n and/or Railwaym and/or Railway^

5 CONCEPTUAL MODEL OF DATA STRUCTURES IN THE SMART-CASPS

The design method for representation of subsystem work is based on the multi-agent system componential design method. There are extracted and designed the set of main actors - agents. For example, the agent/actor of functions of drivers is a combination of the driver’s functionality and other agents correspond to other required transportation means necessary for freight transportation.

The dynamic processes of agents represented as dynamic knowledge about these activities are represented as a set of situations, which are needful to recognize at the concrete time, geographical and environmental conditions. The loT devices provide primary data obtained from sensors, which data sets are aggregated and recognized to a set of different conditions. The recognition of conditions is based on different conditions obtained by conditional characteristics related to weather, road, traffic, etc. A driving situation is a set of particular driving conditions on a particular road segment. Acting can be made based on the concrete perceived information.

The construction of Agent’s work models is based on computer-based ontology, i.e., models of its structure using corresponding entities from computer-based ontology, i.e., concepts expressed by entities like freight and transport mean, environment, and others. Every concept has relations with the necessary concept and forms ontological semantical models. In representing the entity as an “Environment,” we need to represent it by acting like a road network comprised of road segments.

The entity “Route” represents transportation being carried out. It consists of a set of journey points that are time constrained. Each route consists of a planned part and a completed part. The route is constantly evaluated and based on the situation, including the driving situation and current location.

The reasoning is based on concrete decision-making models chosen from the model based on the Smart-CASPS. The reasoning models are multiple models, and the models provided in the diagram are not limited. The main models which are represented and used in the Smart-CASPS:

• sensor model - model that describes sensing information used. Information provided by the sensors does not always contain all the necessary information for reasoning. For example, the sensor location might be static; therefore, it might not be provided with the data.

• Road network - road topology and restrictions are represented by the road network model.

• Freight model - allows to model and reason about freight being delivered.

• Transport mean model - models multiple parameters of transport mean that is used for freight delivery. This enables reasoning about transport's mean suitability for the particular road for route selection.

All models are based on common top-shared ontology, which allows us to integrate them into a single reasoning process.

The situation can affect the existing journey. Multiple sources of information exist, and not only loT-generated data can affect the driving situations. The are multiple sources of information:

• authorities services - are public services that provide road traffic information. Usually, organizations responsible for managing roads and traffic safety provide web services, where information about road status, hazards, and weather conditions can be obtained.

• Community-based services - provide information where users can share their information about situations, like Waze. • Vehicular ad hoc networks (VANETS) - where other vehicles share/provide information to other nodes in the ad hoc networks.

• loT services- where multiple devices installed on the road share their information.

All events come with (or are enriched with) geographical information defining their location. That allows filtering based on geographical information. Only possibilities that might be relevant to the planned route are processed.

All road events are processed are grouped:

• Hard restrictions process - which is usually provided by authority’s services, where particular restrictions are applied to road segments: like weight and speed limits. These restrictions should be applied to existing road models (map) and should be strictly followed. Hard restrictions are identified using a rules-based system that applies rules defined in the ontology to incoming data streams. These rules specify which events are relevant to a particular driving situation and which should be discarded. The information might come from other participants, like online services or other participants. Ontology-based rules approach makes the identification process transportable.

• Recommendations process - recommendations combine gathering data from multiple sources. Information about weather conditions is fed into the process. In a complex driving situation (like icy roads or strong winds), recommendations for reducing speed are provided. This processing can combine multiple, multiple approaches: Machine learning; Rules-based approach; Decision trees based on expert evaluations.

• Estimation process - where estimations of parameters like speed are evaluated. Estimated average speed might come directly from external sources like reduced speed in a traffic jam or might be calculated from several separate events out of multiple sources.

Route evaluation is made based on newly identified values. Route evaluation is based human in the loop approach, where the human decides to change the route. Also, humans provide feedback and guidance to the system.

The identifying of the route is then the procedure of filtering and processing of new event is proceeded.

The ontological view of needful data structures by using the class diagram notation of UML is represented in Fig. 16.

6 6. DESIGN OF DATA STRUCTURES FOR REPORTING ABOUT THE TRUCK /FREIGHT

DELIVERY CONDITIONS DURING ROUTING

During routing and execution of activities, the main data is reported. Such data structures cover: data of current transport location, time, speed, temperature, engine work, door status, etc.

Table 3. Structure of data for reporting about the Truck delivery primary conditions

Explanation of means of columns:

No: this column contains the number that is used for the field in this document.

Name: the field's name as shown in this functional design.

Length: this column contains the maximum length of the field.

Format: the data type of the field.

R: This column indicates whether a field must be filled (required) or whether the field is optional, X = required.

Examples: This column contains examples of data (if any)

Following Table 3, temperature, speed, and engine data status are not mandatory fields for freight delivery.

6.1 Description of Arrival Conditions of Cargo at the Terminal

The delivery scenario includes three parties: Sender, Carrier, and Terminal. The sender loads pallets, seals the truck, and issues an e-document for delivery.

The Carrier must check the delivery conditions mentioned on the e-document, forecast the arrival time based on actual route fulfillment conditions, investigate if the terminal planned for the route has a freight place, register delivery time, and reservation of terminal doors for unloading. Overwise, the carrier has to search for a new terminal.

Table 4. Examples of description of Booking of arrival to the terminal

The terminal accepting the freight has to check the seal, register the time of freight arrival, unload and scan pallets, check if all pallets are unloaded, and confirm the unloading.

The schema of an algorithm for the representation of different actions (activities) and provided decisions according to different conditions of arriving and uploading of Freight, which recognizes the investigation of actions according to the conditions is True according to the information if the truck is loaded, (2) if the terminal has a place, and (3) if all pallets are uploaded.

The main process parameters are the number of freight units and the terminal's capacity. If the terminal has capacity for freight (f=fn and cn+v<=cMAX), the capacity check of the terminal will be important.

6.2 Data Structures for Recognition of Actual Route Execution

The data structures provided by different RSUs and OBUs become the set of contextual information about Truck and Freight's concrete conditions and the drivers. RSU and OBU depend on the sensor’s network technologies that are installed, (e.g., handles, pads). Table 5 shows the data structure for context information description obtained by onboard sensors.

Table 5. Structure for collection of contextual data from some types of sensors (OBU)

CITATION LIST: NON-PATENT LITERATURE

[1] Burinskiene, A.; Dzemydiene, D., Miliauskas, A. An Approach for Ensuring Data Flow in Freight Delivery and Management Systems International Journal of Transport and Vehicle Engineering, Vol: 15, No:3, 2021.

[2] Dzemydiene, D., & Burinskiene, A. (2021). Integration of context awareness in smart service provision system based on wireless sensor networks for sustainable cargo transportation. Sensors, 21(15), 5140.

[3] Dzemydiene, D., Burinskiene, A., Ciziuniene, K., & Miliauskas, A. (2022). Development of Smart Context- Aware Services for Cargo Transportation. International Series in Operations Research and Management Science.

Claims

1. A system for service provision is based on monitoring and managing ground transportation processes of cargo, by comprising:

- means for combining multiple approaches for cargo monitoring from different equipment and integrating them into a smart system;

- means for evaluating relevant data flows in cargo transportation using new components of data flow monitoring and context information recognition;

- means for classification of intelligent service delivery components based on their ability to support optimal wireless network management;

- means for provision of smart services in cargo transportation, comprising:

- means for streamlined approach for intelligent management of cargo transportation by collecting, aggregating, and disseminating contextual data during ground transportation cycles;

- means for description of conceptual models for static and dynamic processes of smart service provision using UML diagrams;

- means for implementation of modern ICT means for designing service scenarios and orchestrating cargo transportation processes

- means for estimating data and flows of operative control of obtained data of ground transportation processes.

2. System according to claim 1, characterized in means for estimating data and flows of operative control of obtained data of ground transportation processes comprises:

- means that combines several approaches that enable cargo monitoring from different equipment and including them into the Smart-CASPS;

- means for evaluation of relevant data flows by describing concrete cargo transportation with the influence of new components of data flow monitoring and environmental-based context information recognition.

3. System of any of claims 1-2, wherein the system architecture includes the integration and implementation of modem ICT means such as GPRS and geographical information systems (GIS) for the management of cargo transportation processes.

4. A method for developing the conceptual model of a Smart-CASPS infrastructure adaptable for multifunctional needs of operative management of cargo transportation, comprising:

- developing a multi-criteria data evaluation for assessing the dynamics of ground transportation processes of cargo, including the infrastructure of ICT components; - estimating data and flows of operative control of obtained data of ground transportation processes of cargo, incorporating new components of data flow monitoring and environmental-based context information recognition;

- classifying intelligent service delivery components based on their ability to support optimal wireless network management;

- designing a novel system architecture integrating monitoring data of transportation processes, new kinds of measuring physical indicators, and contemporary ICT means;

- developing an infrastructure for the provision of smart services, including the intelligent management of cargo transportation, collection and dissemination of contextual data during transportation cycles, and implementation of modern ICT means for the management of cargo transportation processes;

- assessing the requirements of operational management of freight transportation and incorporating a model for interoperability between wireless networks and ground-based vehicles;

- implementing a multi-layered knowledge base, including a computer-based ontology for expressing transport processes and context information, and a knowledge base management model for providing concrete smart services for management of ground-based freight transportation.

5. The method of claim 4 wherein the multi-criteria data evaluation methodology includes an analytical and systematic review of proposed assessment methods, and integration of multi-functional methods for smart service provision processes and interoperable structure of ICT components in the system architecture.

6. The method according to claim 4 or 5, wherein the estimation of data and flows of operative control includes combining multiple approaches for cargo monitoring and evaluating relevant data flows for concrete cargo transportation with the influence of new components of data flow monitoring and environmental-based context information recognition.

7. The method according to any of claims 4-6, wherein the classification of intelligent service delivery components is based on the study of contemporary needs in transportation services and the development of a new smart service classification approach.

8. The method according to any of claims 4-7, wherein the novel system architecture integrates components of developed classification structures, computer-based domain.

9. The method according to any of claims 4-8, wherein the infrastructure for the provision of smart services includes a streamlined approach for intelligent management of cargo transportation, conceptual models of static and dynamic processes of smart service provision using UML, and implementation of modern ICT means for service scenarios and orchestration for the management of cargo transportation processes.

10. The method according to any of claims 4-9, wherein the infrastructure enables the assessment of requirements for operational management of freight transportation, including a model for optimal interaction between wireless computer networks and ground-based vehicles and interoperability between wireless networks and ground-based vehicles for identification of capacity needs and capabilities.

11. The method according to any of claims 4-10, wherein the approach includes the development of a multi-layered knowledge base, including a computer-based ontology for expressing complexity of transport processes and context information, and a knowledge base management model for providing smart services for the management of ground-based freight transportation.

12. The method according to claim 11, further characterized in the knowledge base including data structure application rules for the operational management of situations and algorithms for choosing optimal routes, adapting services for drivers, and ensuring the safety of vehicle traffics, ensuring that the smart services provided are tailored to specific scenarios and prioritize safety and efficiency.

13. The method according to any of claims 4-12, wherein the conceptual model of the Smart-CASPS includes defining and assembling parameters for environmental monitoring, conducting, and assessing adequacy and validity of integrated sub-systems, and implementing a computer-based ontology of processes and operational management rules.

14. The method according to any of claims 4-13, wherein includes the integrating and implementing of ICT such as GPRS and geographical information (GIS) for the management of cargo transportation processes.