Demand Responsive Transport (DRT) is seen as a means to providing mobility for passengers living in lowdensity population areas and impaired passengers with a reasonable cost. Conventional public transport istoo expensive to provide a desired level of mobility for these categories of passengers. Hence DRT hasbeen introduced in order to replace or supplement existing transportation schemes. However, multiple DRTschemes were discontinued due to a high cost or poor patronage. In this work we argue that a simulationtool is required to analyze DRT applicability in given conditions before implementing it. As a first steptowards this tool, we describe the requirements that DRT impose on a simulator.

The rapid proliferation of the Internet of Things (IoT) is changing the way we live our everyday life and the society in general. New devices get connected to the Internet every day and, similarly, new IoT services and applications exploiting them are developed across a wide range of domains. The IoT environment typically is very dynamic, devices might suddenly become unavailable and new ones might appear. Similarly, users enter and/or leave the IoT environment while being interested in fulfilling their individual needs. These key aspects must be considered while designing and realizing IoT systems. In this paper we propose ECo-IoT, an architectural approach to enable the automated formation and adaptation of Emergent Configurations (ECs) in the IoT. An EC is formed by a set of things, with their services, functionalities, and applications, to realize a user goal. ECs are adapted in response to (un)foreseen context changes e.g., changes in available things or due to changing or evolving user goals. In the paper, we describe: (i) an architecture and a process for realizing ECs; and (ii) a prototype we implemented for (iii) the validation of ECo-IoT through an IoT scenario that we use throughout the paper.

The Internet of Things (IoT) pervades more and more aspects of our lives and often involves many types of smart connected objects and devices. User’s IoT environment changes dynamically, e.g., due to the mobility of the user and devices. Users can fully benefit from the IoT only when they can effortlessly interact with it. To accomplish this in a dynamic and heterogenous environment, we make use of Emergent Configurations (ECs), which consist of a set of things that connect and cooperate temporarily through their functionalities, applications, and services, to achieve a user goal. In this paper, we: (i) present the IoT-FED architectural approach to enable the automated formation and enactment of ECs. IoT-FED exploits heterogeneous and independently developed things, IoT services, and applications which are modeled as Domain Objects (DOs), a service-based formalism. Additionally, we (ii) discuss the prototype we developed and the experiments run in our IoT lab, for validation purposes.

The home environment is rapidly becoming more complex with the introduction of numerous and heterogeneous Internet of Things devices. This development into smart connected homes brings with it challenges when it comes to gaining a deeper understanding of the home environment as a socio-technical system. A better understanding of the home is essential to build robust, resilient, and secure smart home systems. In this regard, we developed a novel method for classifying smart home devices in a logical and coherent manner according to their functionality. Unlike other approaches, we build the categorization empirically by mining the technical specifications of 1,193 commercial devices. Moreover, we identify twelve capabilities that can be used to characterize home devices. Alongside the classification, we also quantitatively analyze the entire spectrum of commercial smart home devices in accordance to their functionality and capabilities. Overall, the categorization and analysis provide a foundation for identifying opportunities of generalizations and common solutions for the smart home.

The Internet of Things (IoT) involves intelligent, heterogeneous, autonomous and often distributed things which interact and collaborate to achieve common goals. A useful concept for supporting this effort is Emergent Configuration (EC), which consists of a dynamic set of things, with their functionalities and services, that cooperate temporarily to achieve a goal. In this paper we introduce a commitment-based approach that exploits the concept of commitments to realize ECs. More specifically, (i) we present a conceptual model for commitment-based ECs, (ii) we use the smart meeting room scenario to illustrate how ECs are realized via commitments.

The Internet of Things (IoT) is envisioned as a global net- work of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. How- ever, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT sys- tems and applications, it is key to orchestrate heterogeneous ’things’ in a seamless, adaptive and dynamic manner, such that the system can ex- hibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users’ requirements. To this end, contextu- alisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user’s interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users’ intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.

This book constitutes the refereed proceedings of the 15th International Conference on Practical Applications of Scalable Multi-Agent Systems, PAAMS 2017, held in Porto, Portugal, in June 2017.The 11 revised full papers, 11 short papers, and 17 Demo papers were carefully reviewed and selected from 63 submissions. The papers report on the application and validation of agent-based models, methods, and technologies in a number of key application areas, including day life and real world, energy and networks, human and trust, markets and bids, models and tools, negotiation and conversation, scalability and resources.  

Smart connected home systems aim to enhance the comfort, convenience, security, entertainment, and health of the householders and their guests. Despite their advantages, their interconnected characteristics make smart home devices and services prone to various cybersecurity and privacy threats. In this paper, we analyze six classes of malicious threat agents for smart connected homes. We also identify four different motives and three distinct capability levels that can be used to group the different intruders. Based on this, we propose a new threat model that can be used for threat profiling. Both hypothetical and real-life examples of attacks are used throughout the paper. In reflecting on this work, we also observe motivations and agents that are not covered in standard agent taxonomies.

The Internet of Things (IoT) has a great potential to change our lives. Billions of heterogeneous, distributed, intelligent, and sometimes mobile devices, will be connected and offer new types of applications and ways to interact. The dynamic environment of the IoT, the involvement of the human in the loop, and the runtime interactions among devices and applications, put additional requirements on the systems' architecture. In this paper, we use the Emergent Configurations (ECs) concept as a way to engineer IoT systems and propose an architecture for ECs. More specifically, we discuss (i) how connected devices and applications form ECs to achieve users' goals and (ii) how applications are run and adapted in response to runtime context changes including, e.g., the sudden unavailability of devices, by exploiting the Smart Meeting Room case.

Systems of Systems (SoS) and the Internet of Things (IoT) have many common characteristics. For example, their constituents are heterogeneous, often autonomous, and distributed. Moreover, both IoT systems and SoS achieve their intended goals by means of the dynamic collaboration and coordination among their constituents. In this paper, by using the notion of Emergent Configurations (ECs) as a means to engineer IoT systems, we show how ECs in the IoT can be regarded both as systems and SoS by exploiting two scenarios.

Pervasive technologies permeating our immediate surroundings provide a wide variety of low-cost means of sensing and actuating in our environment. This paper presents an approach for leveraging insights onto the lifestyle and routines of the users in order to control heating in a smart home through the use of individual climate zones, while ensuring system efficiency at a grid-level scale. Organizing smart living spaces into controllable individual climate zones allows us to exert a more fine-grained level of control. Thus, the system can benefit from a higher degree of freedom to adjust the heat demand according to the system objectives. Whereas district heating planing is only concerned with balancing heat demand among buildings, we extend the reach of these systems inside the home through the use of pervasive sensing and actuation. That is to say, we bridge the gap between traditional district heating systems and pervasive technologies in the home designed to maintain the thermal comfort of the user, in order to increase efficiency. The objective is to automate heating based on the user's preferences and behavioral patterns. The control scheme proposed applies a learning algorithm to take advantage of the sensing data inside the home in combination with an optimization procedure designed to trade-off the discomfort undertaken by the user and heating supply costs. We report on preliminary simulation results showing the effectiveness of our approach and describe the setup of our forthcoming field study.

In this paper, we introduce a new approach for collecting data for transport simulation models that is using on-line services in order to outsource parts of the modeling and computation of simulation models. We describe our approach of using on-line services as part of a simulation model and we present our experiences of applying the approach to a case study using the ASIMUT model, where the travelers between two neighbour cities in Southern Sweden are modeled. The results from our case study shows that the use of on-line services for data collection in transport simulation can bring advantages to the simulation model, for example, in terms of reduced needs for modeling of the transport system as well as computation inside the simulation model and improved access to the most recent information. We also noticed some limitations, such as the inability to access to information regarding the future such as timetables and no control over data provided by third-party services. However, we argue that there are solutions for each of the identified limitations, and therefore we believe that the suggested approach might provide a unique opportunity for future transportation simulation models.

Understanding and managing complex systems has become one of the biggest challenges for research, policy and industry. Modeling and simulation of complex systems promises to enable us to understand how a human nervous system and brain not just maintain the activities of a metabolism, but enable the production of intelligent behavior, how huge ecosystems adapt to changes, or what actually influences climatic changes. Also man-made systems are getting more complex and difficult, or even impossible, to grasp. Therefore we need methods and tools that can help us in, for example, estimating how different infrastructure investments will affect the transport system and understanding the behavior of large Internet-based systems in different situations. This type of system is becoming the focus of research and sustainable management as there are now techniques, tools and the computational resources available. This chapter discusses modeling and simulation of such complex systems. We will start by discussing what characterizes complex systems.

Social phenomena simulation in the area of agent-based modeling and simulation concerns the emulation of the individual behavior of a group of social entities, typically including their cognition, actions, and interaction.

Systems of Systems(SoS) and theInternet of Things(IoT)have many common characteristics. For example, their constituents are heterogeneous, autonomous and often distributed. Moreover, both IoT and SoS achieve intended goals by means of the highly dynamic cooperation among their constituents. In this paper we study the relation between IoT and SoS. We discuss the characteristics of both concepts and highlight common aspects. Furthermore, we introduce the conceptSystem of Emergent Configurations (SoECs) to describe IoT-based SoS.

The ubiquity of sensor infrastructures in urban environments poses new challenges in managing the vast amount of data being generated and even more importantly, deriving insights that are relevant and actionable to its users and stakeholders. We argue that understanding the context in which people and things are connected and interacting is of key importance to this end. In this position paper, we present ongoing work in the design of a multiagent model based on immunity theory concepts with the scope of enhancing sensor-driven architectures with context-aware capabilities. We aim to demonstrate our approach in a real-world scenario for processing streams of sensor data in a smart building

Smart homes have become increasingly popular for IoT products and services with a lot of promises for improving the quality of life of individuals. Nevertheless, the heterogeneous, dynamic, and Internet-connected nature of this environment adds new concerns as private data becomes accessible, often without the householders’ awareness. This accessibility alongside with the rising risks of data security and privacy breaches, makes smart home security a critical topic that deserves scrutiny. In this paper, we present an overview of the privacy and security challenges directed towards the smart home domain. We also identify constraints, evaluate solutions, and discuss a number of challenges and research issues where further investigation is required.

We investigate the opportunities and challenges of the forth wave of digitalization, also referred to as the Internet of Things (IoT), with respect to public transport and how it can support sustainable development of society. Environmental, economical, and social perspectives are considered through analysis of the existing literature and explorative studies. We conclude that there are great opportunities for both transport operators and planners, as well as for the travelers. We describe and analyze a number of concrete opportunities for each of these actors. However, in order to realize these opportunities, there are also a number of challenges that needs to be addressed. There are both technical challenges, such as data collection issues, interoperability, scalability and information security, and non-technical challenges such as business models, usability, privacy issues, and deployment.

The durability of perishable food varies due to different storage and handling conditions during the supply chain as well as final consumer activities. If the durability of the individual products can be estimated, dynamic expiry dates may be developed and used to prevent food waste, ensure quality, and improve supply chain activities etc. Depending on the system architecture used for such a service, different qualities can be obtained in terms of usability, accuracy, security etc. This paper presents a novel approach for how to identify and select the most suitable system architectures of a dynamic expiry date service. The approach is illustrated by focusing on one of the potential user groups, the supply chain managers. The approach consists of three steps: (i) identify the potential architectures, (ii) filter out the least relevant candidates by applying a specified set of principles, and (iii) perform an analytic hierarchy process (AHP) based on a set of quality attributes.

The recent advent of ’Internet of Things’ technologies is set to bring about a plethora of heterogeneous data sources to our immediate environment. In this work, we put forward a novel concept of dynamic intelligent virtual sensors (DIVS) in order to support the creation of services designed to tackle complex problems based on reasoning about various types of data. While in most of works presented in the literature virtual sensors are concerned with homogeneous data and/or static aggregation of data sources, we define DIVS to integrate heterogeneous and distributed sensors in a dynamic manner. This paper illustrates how to design and build such systems based on a smart building case study. Moreover, we propose a versatile framework that supports collaboration between DIVS, via a semantics- empowered search heuristic, aimed towards improving their performance.

We present a role-based method for structured and uniform analysis of supply chain models. The method is based on a framework of supply chain roles, responsibilities, and interactions, which can be used to represent different types of organizations involved in providing and using products and transport services. To show the applicability, validity and generality of the method, we have used it to analyze five different supply chain models; three agent-based models, one model based on discrete event simulation, and one based on system dynamics.

We are presently seeing a rapid increase of tools for tracking and analyzing activities, from lifelogging in general to specific activities such as exercise tracking. Guided by the perspectives of collection, procedural, and analysis support, this paper presents the results from a review of 71 existing tools, striving to capture the design choices within personal informatics that such tools are using. The classification system this creates is a contribution in three ways: as a standalone state-of-practice representation, for assessing individual tools and potential future design directions for them, and as a guide for new development of personal informatics tools

This special section on "Engineering Agent-Based Social Simulations" aims to represent the current state of the art in using Software Engineering (SE) methods in ABSS. It includes a mixture of theoretically oriented papers that describe frameworks, notations and methods adapted from SE and practice-oriented papers that demonstrate the application of SE methods in real world ABSS projects.

A method for assessing potential synergies among different sets of transport telematic services (TTSs) is suggested. An Intelligent Transport System enhances transport by delivering one or more TTSs. The ability to deliver multiple TTSs to address a wide range of stakeholder needs is gaining momentum, not only from a marketing perspective but also from a technological perspective. The total cost of TTSs can be reduced if they share functionalities (i.e., sub-services provided by telematic systems). We show how this synergy can be assessed with the help of clustering methods. Knowledge about possible synergies of functionalities is useful in the (re)design and eventual deployment of TTSs, especially when the underlying telematic systems are able to support multiple TTSs. To adapt the clustering method for this purpose, we suggest a mathematical formulation of synergy among functionalities of TTSs. By applying the method to a set of 32 TTSs, we obtain a cluster formation of these TTSs according to their synergy measures. Overall, the results suggest that the joint implementation of TTSs targeted toward some problem domains can lead to significant cost savings, for example, Road User Charging, Infrastructure Repair and Maintenance, and Information on the Transport of extra large goods for the management of road transport infrastructure.

he objective of this paper is to present the design of an information system using software agents. The features of the decision-making process concerning a working model of a hybrid energy grid and the functional requirements of the information system are described. Based on an analysis of the information flow occuring during the data processing for making appropriate decisions, a system architecture which combines a set of independent platforms, is proposed. Multi Agent System is used for the implementation and integration with other systems, such as a power system analysis tool, as well as presentation and storage tools.

The domain of smart home environments is viewed as a key element of the future Internet, and many homes are becoming “smarter” by using Internet of Things (IoT) technology to improve home security, energy efficiency and comfort. At the same time, enforcing privacy in IoT environments has been identified as one of the main barriers for realizing the vision of the smart home. Based on the results of a risk analysis of a smart home automation system developed in collaboration with leading industrial actors, we outline the first steps towards a general model of privacy and security for smart homes. As such, it is envisioned as support for enforcing system security and enhancing user privacy, and it can thus help to further realize the potential in smart home environments.

An agent-based simulation model for supporting the decision making in urban transport planning is presented. The model can be used to investigate how different transport infrastructure investments and policy instruments will affect the travel choices of passengers. We identified four main categories of factors influencing the choice of travel: cost, time, convenience, and social norm. However, travelers value these factors differently depending on their in-dividual characteristics, such as age, income, work flexibility and environmen-tal engagement, as well as on external factors, such as the weather. Moreover, instead of modeling the transport system explicitly, online web services are used to generate travel options. The model can support transport planners by providing estimations of modal share, as well as economical and environmental consequences. As a first step towards validation of the model, we have con-ducted a simple case study of three scenarios where we analyze the effects of changes to the public transport fares on commuters’ travel choices in the Malmö-Lund region in Sweden.

Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product.

The aim of this paper is to present a study on freight transport analysis models. The purpose is to identify different stakeholders’ perceptions of existing models, e.g., strengths and weaknesses, and of their requirements and views on future models. The study is based on a questionnaire and interviews with representatives of public authorities, consultancy companies, and universities in Sweden and Denmark. The study shows that there is a need for freight analysis models for supporting the transport planning in public authorities, including impact assessment of actions and estimation of freight flows. The respondents work mainly with macro-level models, whose main strength is their large geographic scopes, which allow comparative studies on, e.g., the national level using one model. Weaknesses include poor quality, missing functionality, and inadequate user-friendliness. In order to achieve improved freight transport analysis, the respondents wish to include more detailed logistics aspects in their analyses, which could possibly be achieved by combining macro-level and agent-based models. The outcome of our study might be used by researchers and public authorities in order to, e.g., guide the decision-making on future model development: the views of the model users and clients are important to consider in order to assure that the development and research efforts lead to fulfilling their needs. The presented work provides insight into the needs and attitudes of model users and clients involved in freight transport analysis. This knowledge is important, e.g., for researchers involved in model development. According to the best of our knowledge, there is no previous study like the one presented.

This paper provides an analysis of the use of computational models for predicting the effects of different policy instruments on urban transport systems. We define a framework for describing and comparing current approaches that takes into account (i) what policy instruments are studied, (ii) what type of effects are predicted, (iii) which factors are modeled, (iv) the type of model used, and (v) what data sources are used. The main conclusions of the analysis are: (i) despite the recognized potential of agent-based modeling to study behavioural change of a population, it rarely has been used for ex-ante analysis of policy instruments using real-world data, (ii) some factors that influence travellers’ decisions, such as comfort and departure time, have not been considered much in the modeling, (iii) ex-ante analysis of economic instruments constitute the majority of studies, but informative instruments have been recently considered due to the increasing use of information technology in transportation.

Macro-level models is the dominating type of freight transport analysis models for supporting the decision-making in public authorities. Recently, also agent-based models have been used for this purpose. These two model types have complementing characteristics: macro-level models enable to study large geographic regions in low level of detail, whereas agent-based models enable to study entities in high level of detail, but typically in smaller regions. In this paper, we suggest and discuss three approaches for combining macro-level and agent-based modeling: exchanging data between models, conducting supplementary sub-studies, and integrating macro-level and agent-based modeling. We partly evaluate these approaches using two case studies and by elaborating on existing freight transport analysis approaches based on executing models in sequence.

We present TAPAS-Z, which is an agent-based freight transport analysis model for simulation of decision-making and transport activities. TAPAS-Z is a further development of a simulation model called TAPAS, and it has improved support for simulation of transport in large geographical regions. It is based on the principles that shipments are simulated for chosen supplier-consumer relations in a geographic region, and that the geographic locations of suppliers and consumers are randomly varied for each shipment. In TAPAS-Z, one supplier represents all real-world suppliers in a geographic zone, and one consumer represents all real-world consumers in a zone. In that way, TAPAS-Z is able to capture some of the diversity in freight transport that is caused by the varying geographic locations of senders and receivers, and which is important when assessing the impact of transport policy and infrastructural measures.

The use of agreement technologies in the planning and execution of goods transports is analyzed. We have previously suggested an approach called Plug and Play Transport Chain Management (PnP TCM) that provides agent-based support for key tasks, such as, finding the best sequence of transport services for a particular goods transport, monitoring the execution of the transport, and managing the interaction between the involved actors. In this paper we analyze five agreement technologies in the context of PnP TCM, i.e., semantics, norms, organizations, argumentation and negotiation, and trust. We conclude that all five technologies play a critical role in the realization of PnP TCM.

The basic prerequisite for methodological advance in Multi-Agent Based Modelling and Simulation is a clear, ideally formally-grounded, concept of our subject. A commonly accepted, implementation-independent meta-model may improve the status of MABS as a scientific field providing a solid foundation that can be used for describing, comparing, analysing, and understanding MABS models. In this contribution, we present an attempt formalizing a general view of MABS models by defining the AMASON meta-model that captures the basic structure and dynamics of a MABS model.

Intelligent Truck Parking (ITP) involves efficient management of information as a tool for addressing some of the problems related to truck parking, such as, the inability to navigate to, or reserve a parking space. Different telematic services (ITP core services) may be required to address different stakeholder needs, e.g., a navigation service for finding the way to a truck parking area, and a reservation service for assuring the availability of a parking space when in the parking area. In addition to ITP core services, there are several other telematic services that address transport problems in general. If the synergies of different services are taken into account during design and deployment of telematic services, cost reduction may be achieved through functionality sharing. In this article, we have identified services that are relevant for sharing functionalities with ITP core services, estimate functionality costs and perform a synergy analysis to assess the marginal cost savings as a result. Overall results indicates that Traffic Information Broadcasting (TIB), Estimated Time of Arrival (ETA) for goods, and Geo-fencing (GEO) can form clusters with ITP core services that could lead to better cost savings compared to the rest of the services studied.

We present an agent-based optimization approach that is built upon the principles of Dantzig-Wolfe column generation, which is a classic reformulation technique. We show how the approach can be used to optimize production, inventory, and transportation, which may result in improved planning for the involved supply chain actors. An important advantage is the possibility to keep information locally when possible, while still enabling global optimization of supply chain activities. In particular, the approach can be used as strategic decision support to show how the involved actors may benefit from applying Vendor Managed Inventory (VMI). In a case study, the approach has been applied to a real-world integrated production, inventory and routing problem, and the results from our experiments indicate that an increased number of VMI customers may give a significant reduction of the total cost in the system. Moreover, we analyze the communication overhead that is caused by using an agent-based, rather than a traditional (non agent-based) approach to decomposition, and some advantages and disadvantages are discussed.

Experiences from different applications of agent technology aiming to make transport and energy systems more efficient are presented. The examples will cover real-time applications on the operational level, as well as, support for long-term planning and decision-making on the strategic level. Some general reflections and insights from the work on these applications conclude the paper.

Due to the continuous growth of traffic and transportation and a thus increased urgency to analyze resource usage and system behavior, the use of computer simulation within this area has become more frequent and acceptable. This chapter presents an overview of modeling and simulation of traffic and transport systems, and focuses in particular on the imitation of social behavior and individual decision making in these systems. We distinguish between transport and traffic. Transport is an activity where goods or people are moved between points A and B while traffic is referred to as the collection of several transports in a common network such as a road network. We investigate to what extent and how the social characteristics of the users of these different traffic and transport systems are reflected in the simulation models and software. Moreover, we highlight some trends and current issues within this field and provide further reading advice.

The aim of this work is to develop a new type of service for predicting and communicating urban activity. This service provides short-term predictions (hours to days), which can be used as a basis for different types of resource allocation and planning, e.g. concerning public transport, personnel, or marketing. The core of the service consists of a forecasting engine that based on a prediction model processes data on different levels of detail and from various providers. This paper explores the requirements and features of the forecast engine. We conclude that agent-based modeling seems as the most promising approach to meet these requirements. Finally, some examples of potential applications are described along with analyses of scientific and engineering issues that need to be addressed.

This chapter describes the main purposes of computer simulation and gives an overview of the main issues that should be regarded when developing computer simulations. While there are two basic ways of representing a system in a simulation model – the equation-based or macroscopic approach and the individual-based or microscopic approach – this chapter (as the rest of the handbook) focuses on the latter. It discusses the various options a modeller faces when choosing how to represent individuals, their interactions and their environment in a simulation model.

The focus of this article is to identify, characterise and qualitatively evaluate the existing measures for increasing the utilisation of loading space in intermodal road–rail freight line trains. Normally, these trains operate like intercity passenger trains, because they make short unit load transhipment stops along rail corridors in order to travel comparatively small and short flows. The measures for loading space utilisation include adapting the train’s capacity, changing the departure times, altering the train routes and sending trucks to different terminals as well as replacing rail transport with trucks. Some of the measures require improved information sharing, and executives can strengthen the effect of the measures by adding decision support systems and price incentives to transport buyers.

Information visualization, as a way of presenting different data types in an understandable form, has the potential to support the analysis of transportation data. Visualization can often help decision makers to efficiently analyse large amount of information. One application area of information visualization is to support the analysis of the transportation data and thus facilitate the decision-making process. Most of the previous studies in this area have focused on visualization of transportation infrastructures such as roads, bridges in order to enhance the public awareness regarding upcoming projects which makes it easier to reach a consensus on the high-level decisions. However, the main focus of this article is on methods for visualization of data generated by transportation simulation systems to support analysis of the consequences of applying different transport policy measures, such as the introduction of road user charging or investment in new infrastructure. In this work, we investigate how visualization techniques could address the challenges of transportation simulation data analysis in order to facilitate the decision-making process. For this purpose, we have applied the visualization methods to a real implemented agent-based transportation simulator called TAPAS. In this case study, we have analysed the visualization related requirements of users using a user-centric approach and the visualization tool has been designed and developed based on the identified requirements.

This paper presents a method for identifying possible architectural solutions for potential intelligent goods services. The solutions range from putting all intelligence at the goods level, to requiring no intelligence on the goods at all. The method is based on a general framework for describing intelligent goods systems, which involves several levels of intelligence related to both the goods and the local environments surrounding the goods. Furthermore, a number of quality attributes are identified, which may be used for evaluating and comparing the solutions. Based on these attributes, a quality evaluation of the architectural solutions related to a potential intelligent goods service is also provided, as an example.

Purpose The primary objective of this paper is to propose a framework for specifying and analyzing Intelligent Transport System (ITS) services. Design/methodology/approach Essential service information are identified and stated in a uniform way. A separation into an abstract description, focused on what the service does, and a concrete description, focused on how to perform the service tasks, is made. Findings The proposed a framework has been used for specifying and distinguishing ITS services and for different types on analyses, e.g. service composition/decomposition analyses and architecture analyses. Original/value The usefulness is illustrated by an architecture choice analysis applied on a specific intelligent goods service. Apart from ITS services, the framework has the potential to also apply to other types of information services.

In this work, we investigate how visualization techniques could address the challenges of transportation simulation data analysis in order to facilitate the decision-making process for transportation simulation users. For this purpose, we have applied the visualization methods in a real implemented agent-based transportation simulator called TAPAS as a case. We have analyzed the visualization related requirements of users using a user-centric approach and an interactive visualization toolkit has been designed and developed based on the identified requirements. This paper presents a description of the visualization toolkit which will be used to investigate how the actors in a transport chain are expected to act when different types of governmental control policies are applied, such as, fuel taxes, road tolls, vehicle taxes and requirements on vehicles.

Combined heat and power (CHP) generation is often used when building new district heating production. CHP makes it possible to simultaneously produce electricity and heat, thus maximizing the energy efficiency of the primary fuel. The heat is used in the connected district heating system while the electricity is sold on the local power market. In a CHP plant it is not possible to separate the physical process of producing heat and electricity, which may cause suboptimal behaviour when high spot prices for power do not coincide with high heat load demand. This paper presents the design and implementation of a system which makes it possible to control the heat load demand in a district heating network in order to optimize the CHP production. By using artificial intelligence technology in order to automate the run‐time coordination of the thermal inertia in a large amount of buildings, it is possible to achieve the same operational benefits as using a large storage tank, albeit at a substantially less investment and operational cost. The system continuously considers the climate in each participating building in order to dynamically ensure that only the best suited buildings at any given time are actively participating in load control. Based on the dynamic indoor climate in each individual building the system automatically controls and coordinates the charging and discharging of the buildings thermal buffer without affecting the quality of service. This paper describes the overall function of the system and presents an algorithm for coordinating the thermal buffer of a large amount of buildings in relation to heat load demand and spot price projections. Operational data from a small district heating system in Sweden is used in order to evaluate the financial and environmental impact of using this technology. The results show substantial benefits of performing such load control during times of high spot price volatility.

This study describes transport telematic services (TTSs) for road-based heavy goods vehicle (HGV) transport and suggests a method for assessing the societal value of different TTSs. For decision making related to the selection of services to promote by potential investors, for example, governmental organisations and service providers, quantified service value can simplify the decision process by enabling comparison between TTSs. Moreover, these values can serve as inputs to quantitative analysis of service architectural system designs. The authors suggest a method for assessing the societal values of TTSs using potential saving indicators (PSIs), estimated in the context of Swedish HGV freight transport. To illustrate the proposed method, 32 services are analysed, and their societal values were quantified and compared for the Swedish HGV market. Results based on estimated values of PSIs and potential percentage savings indicate the following HGV-based TTSs to be of high societal potential: transport resource optimisation, dynamic traffic information, navigation, road hindrance warning, theft alarm and recovery, accident warning information, intelligent speed adaptation, eCall, en-route driver information, transport order handling, road user charging and sensitive goods monitoring.

In this paper we elaborate on the usage of multi-agent-based simulation (MABS) for quantitative impact assessment of transport policy and infrastructure measures. We provide a general discussion on how to use MABS for freight transport analysis, focusing on issues related to input data management, validation and verification, calibration, output data analysis, and generalization of results. The discussion is built around an agent-based transport chain simulation tool called TAPAS (Transportation And Production Agent-based Simulator) and a simulation study concerning a transport chain around the Southern Baltic Sea.

Transport telematic systems, also known as intelligent transportation systems, can be expensive to implement but the services they provide may offer substantial benefits. However, what services the system can provide depends on the architectural choices made, which also affects the cost of the system. We propose an optimization model to support a more informed decision before investing in a multiple service transport telematic system. The model evaluates the possible choices of services and architectures, and aims to maximize the total net societal benefit. We argue that the optimization model can provide support for strategic decisions by highlighting the consequences of adopting different system architectures, including both societal value and cost. This can be useful for decision makers, such as governments, road transport telematic service providers, and commercial road freight transport operators.