Remote health monitoring is a trend for better health management which necessitates the need for secure monitoring and privacy-preservation of patient data. Moreover, accurate and continuous monitoring of personal health status may require expert validation in an active learning strategy. As a result, this paper proposes a Federated Interactive Learning IoT-based Health Monitoring Platform (FIL-IoT-HMP) which incorporates multi-expert feedback as ‘Human-in-the-loop’ in an active learning strategy in order to improve the clients’ Machine Learning (ML) models. The authors have proposed an architecture and conducted an experiment as a proof of concept. Federated learning approach has been preferred in this context given that it strengthens privacy by allowing the global model to be trained while sensitive data is retained at the local edge nodes. Also, each model’s accuracy is improved while privacy and security of data has been upheld.

In this paper, we propose a method for supporting the process of designing Intelligent Transportation System (ITS) services, which utilizes on primarily functional synergies with already existing services. Using synergies between services will enable sharing of resources, such as, information entities, functions and technical resources, which in turn may lead to reduced costs for implementing services. The method is built around an existing service description framework, which is used to describe both existing services and the service to be designed. In order to illustrate the usage of the suggested method, we have applied it for designing a new ITS service, i.e., the Liability Intelligent Transport System (LITS) service. The purpose of the LITS service is to support the process of identifying when, where and why freight has been damaged, and which actor was responsible when the freight was damaged. The LITS service may lead to better quality control of consignments and may also facilitate the identification of which actor was responsible for the freight damage, which is of particular interest in multi-modal transport. By applying our service design method we were able to identify that three out of four functions of the LITS service already exist in other existing ITS services. Therefore, the LITS service can be designed based on synergies with these services.

We present a study on information synergy between an electronic waybill (e-Waybill) and intelligent transportation system (ITS) services. A waybill is an important transport document, and it contains essential information about a consignment. We consider five e-Waybill solutions, which differ in where the e-Waybill information is stored, read, and written. We analyse 20 ITS services, and for each of them, the required input entities that can be provided by an e-Waybill are identified, and the synergy with each of the e-Waybill solutions is determined based on the location of the e-Waybill information. The analysis shows that e-Waybill solutions with storage at the freight-level enable the highest information synergy with ITS services. Our result may support the implementation of practical e-Waybill systems that provide high synergy with ITS services, which may lead to higher utilisation of ITS services and more sustainable transport, e.g., in terms of reduced congestion and emissions

An electronic waybill (e-Waybill) is a service whose purpose is to replace the paper waybill, which is a paper documents that traditionally follows a consignment during transport. An important purpose of the e-Waybill is to achieve a paperless flow of information during freight transport. In this paper, we investigate five e-Waybill solutions, that is, system design specifications for the e-Waybill, regarding their non-functional (technical) requirements. In addition, we discuss how well existing technologies are able to fulfil the identified requirements. We have identified that information storage, synchronization and conflict management, access control, and communication are important categories of technical requirements of the e-Waybill service. We argue that the identified technical requirements can be used to support the process of designing and implementing the e-Waybill service.

The focus of this paper is to present potential electronic waybill (e-waybill) solutions for a traditional waybill with the potential for supporting different Intelligent Transport System (ITS) services, such as, identification of freight, automating the exchange of content-related data for regulatory or commercial purposes, etc. At present there are some initiatives, for instance, by the International Air Transport Association (IATA) and the e-Freight framework, for achieving solutions that can handle e-waybills. Both these solutions focus on actor-to-actor communication, however we hypothesize that the storage of a waybill’s data both locally and centrally (i.e. actor-to-actor as well as goods-to-actor communication) can support more services than only central storage of the waybill’s data. Further we look at the information required and possible communication links between different actors in order to identify different ITS services that can be achieved through an e-waybill solution. We then map this information on to the e-waybill solutions that we have proposed, which will allow us to identify which services are supported by the different e-waybill solutions.

A major concern in the use of Internet of Things (IoT) technologies in general is their reliability in the presence of security threats and cyberattacks. Particularly, there is a growing recognition that IoT environments featuring virtual sensing and interactive machine learning may be subject to additional vulnerabilities when compared to traditional networks and classical batch learning settings. Partly, this is as adversaries could more easily manipulate the user feedback channel with malicious content. To this end, we propose a data-centric anomaly-based detection system, based on machine learning, that facilitates the process of identifying anomalies, particularly those related to poisoning integrity attacks targeting the user feedback channel of interactive machine learning setups. We demonstrate the capabilities of the proposed system in a case study involving a smart campus setup consisting of different smart devices, namely, a smart camera, a climate sensmitter, smart lighting, a smart phone, and a user feedback channel over which users could furnish labels to improve detection of correct system states, namely, activity types happening inside a room. Our results indicate that anomalies targeting the user feedback channel can be accurately detected at 98% using the Random Forest classifier.

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 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.

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.

A novel approach to efficiently plan and execute effective transport solutions is presented. It 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, as well as the interaction between the involved actors. The approach is based on the FREIGHTWISE framework in which a minimal set of information packages is defined. The purpose is to capture all the information that needs to be communicated between the actors involved in a transport, such as, transport users, transport providers, and infrastructure managers, during the complete process from planning to termination. The approach is inspired by the concepts of virtual enterprises and breeding environments. We analyse the requirements of such an approach and describe a multi-agent system architecture meeting these requirements.

A set of important criteria to consider when evaluating potential road user charging system (RUCS) are identified. These criteria are grouped into five categories: charging precision, system costs & societal benefits, flexibility & modifiability, operational aspects, and security & privacy. The criteria are then used in a comparative analysis of five RUCS candidates for heavy goods vehicles. Two solutions are position-based systems and one is based on tachographs. The two remaining solutions are based on fuel taxes. For each of the solutions we estimate how well it fulfils each of the criteria. One way of making general comparisons of the approaches is to give each of the criteria a specific weight corresponding to how important it is. We show that these weights heavily influence the outcome of the comparison. We conclude by pointing out a number of important issues needing attention in the process of developing RUCS.

A novel application of agent-mediated electronic commerce is presented. It concerns developing and maintaining efficient and effective transport solutions. The suggested approach is inspired by the concepts of virtual enterprises and breeding environments, as well as peer-to-peer technology. We discuss the requirements of such an approach and outline a software architecture meeting these requirements.

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.

On-demand transport services are often envisioned as stand-alone modes or as a replacement for conventional public transport modes. This leads to a comparison of service efficiencies, or direct competition for passengers between them. The results of this work point to the positive effects of the inclusion of DRT into the public transport network. We simulate a day of operation of a DRT service in a rural area and demonstrate that a DRT system that focuses on increasing accessibility for travellers with poor public transport access can be quite efficient, especially for reducing environmental impact. We show that DRT, while it produces more vehicle kilometres than private cars would inside the DRT operating zone, can help to reduce the vehicle kilometres travelled for long-distance trips. The results of this study indicate the need for a more systemic evaluation of the impact of the new mobility modes.

For the provision of efficient and high-quality public transport services in rural areas with a low population density, the introduction of Demand Responsive Transport (DRT) services is reasonable. The optimal design of such services depends on various socio-demographical and environmental factors, which is why the use of simulation is feasible to support planning and decision-making processes. A key challenge for sound simulation results is the generation of realistic demand, i.e., requests for DRT journeys. In this paper, a method for modelling and simulating commuting activities is presented, which is based on statistical real-world data. It is applied to Sjöbo and Tomelilla, two rural municipalities in southern Sweden.

This article demonstrates an approach to the simulation of Demand Responsive Transport (DRT) – a flexible transport mode that typically operates as a combination of taxi and bus modes. Travellers request individual trips and DRT is capable of adjusting its routes or schedule to the needs of travellers. It has been seen as a part of the public transport network, which has the potential to reduce operational costs of public transport services, to provide better service quality for population groups with limited mobility and to improve transport fairness. However, a DRT service needs to be thoroughly planned to target the intended user groups, attract a sufficient demand level and maintain reasonable operational costs. As the demand for DRT is dynamic and heterogeneous, it is difficult to simulate it with a macro approach. To address this problem, we develop and evaluate an individual-based simulation comprising models of traveller behaviour for both supply and demand sides. Travellers choose a trip alternative with a mode choice model and DRT vehicle routing utilises a model of travellers’ mode choice behaviour to optimise routes. This allows capturing supply-side operational costs and demand-side service quality for every individual, what allows for designing a personalised service that can prioritise needy groups of travellers improving transport fairness. By simulating different setups of DRT services, the simulator can be used as a decision support tool.

In rural areas with low demand, demand responsive transport (DRT) can provide an alternative to the regular public transport bus lines, which are expensive to operate in such conditions. With simulation, we explore the potential effects of introducing a DRT service that replaces existing bus lines in Lolland municipality in Denmark, assuming that the existing demand remains unchanged. We set up the DRT service in such a way that its service quality (in terms of waiting time and in-vehicle time) is comparable to the replaced buses. The results show that a DRT service can be more cost efficient than regular buses and can produce significantly less CO2 emissions when the demand level is low. Additionally, we analyse the demand density at which regular buses become more cost efficient and explore how the target service quality of a DRT service can affect operational characteristics. Overall, we argue that DRT could be a more sustainable mode of public transport in low demand areas.

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Our dependency on Global Navigation Satellite System (GNSS) for getting directions, tracking items, locating friends, or getting maps of the world has increased tremendously over the last decade. However, as soon as we enter a building, the signal strength of the satellites is too low, and we need to resort to other technologies to achieve the same goals. An Indoor Positioning System (IPS) may utilize a wide range of methods for positioning a device, such as fingerprinting, multilateration, or sensor fusion, while using one or several radio technologies to measure Received Signal Strength (RSS) or Time of Arrival(ToA). Sensor fusion is an efficient approach where an Inertial Measurement Unit (IMU) is combined with, e.g., RSS measurements converted to distances. But this approach has significant drawbacks in areas where, e.g., walls or large objects obstruct the signal path, which introduces bias in the distance estimates. This paper addresses the bias caused by signal path obstruction by compensating the measured RSS with localized RSS attenuation adjustments and thereby increasing the accuracy of the sensor fusion model significantly. We also show that a system can learn the compensation parameters over time, reducing the installationefforts and achieving higher accuracy than a fingerprinting-based system.

Machine Learning (ML) has been a prominent area of research within Artificial Intelligence (AI). ML uses mathematical models to recognize patterns in large and complex data sets to aid decision making in different application areas, such as image and speech recognition, consumer recommendations, fraud detection and more. ML systems typically go through a training period in which the system encounters and learns about the data; further, this training often requires some degree of human intervention. Interactive machine learning (IML) refers to ML applications that depend on continuous user interaction. From an HCI perspective, how humans interact with and experience ML models in training is the main focus of this workshop proposal. In this workshop we focus on the user experience (UX) of Interactive Machine Learning, a topic with implications not only for usability but also for the long-term success of the IML systems themselves.

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.

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.

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 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.

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.

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.

We present the Transportation And Production Agent-based Simulator (TAPAS), which is an agent-based model for simulation of transport chains that can be used, e.g., for analysis of transport-related policy and infrastructure measures. TAPAS is more powerful than traditional approaches to freight transport analysis, as it explicitly models production and customer demand, and it captures the interaction between individual transport chain actors, their heterogeneity and decision making processes, as well as time aspects. Whereas traditional approaches rely on assumed statistical correlation, TAPAS relies on causality, i.e., the focus is on the decisions and negotiations that lead to activities. TAPAS is composed of two connected layers, one that simulates the physical activities, e.g., production and transportation, and one that simulates the decision making and interaction between actors. We illustrate TAPAS with a scenario in which the consequences of three transport policy and infrastructure measures are 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.

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.

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.

The purpose of this paper is to present a framework for intelligent goods and illustrate how it can be applied when modeling intelligent goods as agents. It includes a specification of different levels of capability connected to the goods, which is based on a number of capability dimensions. Additionally, three specific intelligent goods services related to transport are presented. We show how these services can be modeled as agents and how they relate to the intelligent goods framework. A discussion of different physical locations of service information and processing is also included.

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 of this paper: This paper presents a method for identifying possible architectural solutions for potential intelligent goods services. The solutions range from implementing all service intelligence at the goods level to requiring no goods intelligence at all. The paper also shows how different solutions, identified by the method, can be compared and evaluated through quality analysis in order to determine when intelligent goods are beneficial. Design/methodology/approach: Literature and case studies, and theoretical analyses form the foundations of the paper. 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 entities surrounding the goods. Three specific services are used to illustrate the method. The quality analysis is based on the Analytic Hierarchy Process (AHP) and a number of proposed quality attributes. Findings: The paper illustrates how the framework can be used to identify relevant architectural solutions for a particular service, with different levels of intelligence on the goods. It also shows how to qualitatively analyze individual solutions and suggests an extension to the general framework for describing intelligent goods systems. Research limitations/implications: The paper does not contain measured data to base the quality analysis on. Testing the method and quality analysis on implemented cases is recommended for future work. What is original/value of paper: The novel method and approach for identifying and evaluating architectural solutions for potential intelligent goods services, are useful in general management when investigating the value of implementing intelligent goods. The paper also contributes to the description of intelligent goods systems.

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.

This paper explores the possibilities and challenges of realizing a context-aware travel planner with bidirectional information exchange between the actor and the traveller during unplanned traffic disturbances. A prototype app is implemented and tested to identify potential benefits. The app uses data from open APIs, and beacons to detect the traveller context (which train or train platform the traveller is currently on). Alternative travel paths are presented to the user, and each alternative is associated with a certainty factor reflecting the reliability of the travel time prognoses. The paper also presents an interview study that investigates PT actors’ views on the potential use for actors and travellers of new information about certainty factors and travellers’ contexts, during unplanned traffic disturbances. The results show that this type of travel planner can be realized and that it enables travellers to find ways to reach their destination, in situations where the public t ravel planner only suggests infeasible travel paths. The value for the traveller of the certainty factors are also illustrated. Additionally, the results show that providing actors with information about traveller context and certainty factors opens up for the possibility of more advanced support for both the PT actor and the traveller.

This paper presents and evaluates a new method for how emissions from freight transport routes with single or several points of loading and unloading, can be allocated to individual consignments. The method, called Dedicated Distance Proportional Allocation (DDPA), has been developed based on a literature review, discussions with logistics providers, and analysis. DDPA is designed to have low data processing requirements and be easy to explain to actors involved. Furthermore, it supports several levels of information availability, and accounts for any set of vehicle-limiting factors, as well as prepositioning/repositioning. DDPA has been evaluated in simulations with different levels of information availability, together with three existent allocation methods: the Equal profit method (EPM), the CEN EN16258:2011 standard and the Greenhouse gas (GHG) protocol. The simulations show that the GHG protocol under-allocates the total amount of emissions, on average. EPM and DDPA achieve equal relative savings, whereas for CEN EN16258:2011 and the GHG protocol, relative savings vary, on average. When DDPA is used with low level of information availability, an error is introduced which can be reduced by applying compensation factors. Since DDPA accepts low information availability, the Intelligent Products concept can be applied for computing and storing emissions allocations, at the time of unloading. The results from this study can be used for further development and implementation of consignment allocation methods. Furthermore, by combining DDPA with other environmental load approaches for other parts of a product’s life cycle, a complete life cycle assessment of the product’s environmental impact can be obtained.

New and emerging technologies, such as connected sensors, smartphones and smart cards, offer new possibilities to collect rich real-time information about travelers. Moreover, smartphones also enable travelers to actively share information, for instance, about their intended travel plans. This type of information can be used to improve public transport disturbance management. In this paper, the potential gain of collecting different types of information about travelers is explored to support action decisions made by public transport actors, during unplanned disturbances. Based on interviews and workshops, the paper provides a mapping between different information types and possible action decisions that can be supported. Furthermore, based on a literature review focused on current and potential technical solutions, a guidance to which solutions support which type of action decisions, is also provided. Amongst others, the results show that automated fare collection, which is one of the most commonly implemented systems providing real-time information about the traveler, can support a large number of action decisions relevant in unplanned disturbance scenarios. The technical solution providing the most extensive information, and thereby providing the best support for the action decisions, involves smartphone apps delivering user-generated information. The drawback with this solution is that it might violate privacy, and that it typically relies on the travelers providing relevant information voluntarily.

Travel support for public transport today usually takes no or little account of the traveler’s personal needs and current context. Thereby, travelers are often suggested irrelevant travel plans, which may force them to search for information from other sources. In particular, this is a problem during unplanned disturbances. By incorporating the traveler’s context information into the travel support, travelers could be provided with individually tailored information. This would especially benefit travelers who find it more difficult than others to navigate the public transport system. Furthermore, it might raise the accessibility and general attractiveness of public transport. This paper contributes with an understanding of how information about the traveler’s context can enhance the support provided by travel planners, in the case of disturbances in public transport. In particular, the paper includes a high-level analysis of how and in which situations context information can be useful. The analysis shows how information about the traveler’s context can improve travel planners, as well as highlights some risks in relation to some identified scenarios. Several technologies for retrieving information about the physical context of the traveler are also identified. The study is based on a literature review, a workshop, and interviews with domain experts.

Modern Information and Communication Technology (ICT)-based applications utilize currenttechnological advancements for purposes of streaming data, as a way of adapting to the ever-changingtechnological landscape. Such efforts require providing accurate, meaningful, and trustworthy output fromthe streaming sensors particularly during dynamic virtual sensing. However, to ensure that the sensingecosystem is devoid of any sensor threats or active attacks, it is paramount to implement secure real-timestrategies. Fundamentally, real-time detection of adversarial attacks/instances during the User FeedbackProcess (UFP) is the key to forecasting potential attacks in active learning. Also, according to existingliterature, there lacks a comprehensive study that has a focus on adversarial detection from an activemachine learning perspective at the time of writing this paper. Therefore, the authors posit the importance ofdetecting adversarial attacks in active learning strategy. Attack in the context of this paper through a UFPThreat driven model has been presented as any action that exerts an alteration to the learning system ordata. To achieve this, the study employed ambient data collected from a smart environment human activityrecognition from (Continuous Ambient Sensors Dataset, CASA) with fully labeled connections, where weintentionally subject the Dataset to wrong labels as a targeted/manipulative attack (by a malevolent labeler)in the UFP, with an assumption that the user-labels were connected to unique identities. While the dataset’sfocus is to classify tasks and predict activities, our study gives a focus on active adversarial strategies froman information security point of view. Furthermore, the strategies for modeling threats have been presentedusing the Meta Attack Language (MAL) compiler for purposes adversarial detection. The findings fromthe experiments conducted have shown that real-time adversarial identification and profiling during the UFPcould significantly increase the accuracy during the learning process with a high degree of certainty and pavesthe way towards an automated adversarial detection and profiling approaches on the Internet of CognitiveThings (ICoT).

Whereas data labelling in IoT applications is costly, it is also time consuming to train a supervised Machine Learning (ML) algorithm. Hence, a human oracle is required to gradually annotate the data patterns at run-time to improve the models’ learning behavior, through an active learning strategy in form of User Feedback Process (UFP). Consequently, it is worth to note that during UFP there may exist malicious content that may subject the learning model to be vulnerable to adversarial attacks, more so, manipulative attacks. We argue in this position paper, that there are instances during incremental learning, where the local data model may present wrong output, if retraining is done using data that has already been subjected to adversarial attack. We propose a Distributed Interactive Secure Federated Learning (DISFL) framework that utilizes UFP in the edge and fog node, that subsequently increases the amount of labelled personal local data for the ML model during incremental training. Furthermore, the DISFL framework addresses data privacy by leveraging federated learning, where only the model's knowledge is moved to a global unit, herein referred to as Collective Intelligence Node (CIN). During incremental learning, this would then allow the creation of an immutable chain of data that has to be trained, which in its entirety is tamper-free while increasing trust between parties. With a degree of certainty, this approach counters adversarial manipulation during incremental learning in active learning context at the same time strengthens data privacy, while reducing the computation costs.

The increasing popularity of e-commerce requires efficient solutions for the provision of last mile logistics. There are different approaches for delivering parcels, e.g., home delivery, service points, or parcel lockers, which have different advantages and disadvantages for customers and logistics providers in terms of flexibility, accessibility, and operating costs. We have studied a novel transportation solution where electric vehicles dynamically set up smart cargo boxes, from which customers can fetch their delivery at any time of the day. This provides customers with a more flexible access to their packages and allows the service provider to deliver the parcels more efficiently. In this article, we present the results of a feasibility study conducted in Västra Hamnen, Malmö (Sweden). The developed simulation model shows that smart boxes not only are a viable approach for efficient last mile deliveries, but also result in considerably smaller travel distances compared to conventional package delivery.

Self-driving cars enable dynamic shared mobility, where customers are independent of schedules and fixed stops. This study aims to investigate the potential effects shared mobility can have on future transportation. We simulate multiple scenarios to analyze the effects different service designs might have on vehicle kilometers, on the required number of shared vehicles, on the potential replacement of private cars, and on service metrics such as waiting times, travel times, and detour levels. To demonstrate how simulation can be used to analyze future mobility, we present a case study of the city of Gothenburg in Sweden, where we model travel demand in the morning hours of a workday. The results show that a significant decrease of vehicle kilometers can be achieved if all private car trips are replaced by rideshare and that shared vehicles can potentially replace at least 5 private cars during the morning peak.

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.

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.

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.

This article uses analytical methods to assess reductions in total costs of telematic systems that can result from common infrastructure utilization. Analytical methods based on clustering and K-minimum spanning tree can be adopted for finding clusters or sets which maximize reductions in total system costs due to infrastructure sharing between telematic systems. Efficient integration of telematic systems through infrastructure sharing can positively influence telematic service interoperability while reducing costs. Results show the measure of synergy for each K-value, as well as total cost savings of up to 2%.

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.

Recent proliferation of surveillance systems is mostly attributed to advances in both image-processing techniques and hardware enhancement of smart cameras, as well as the ubiquity of sensor-driven architectures. Owing to these capabilities, new aspects are coming to the forefront. This paper addresses the current state-of-the-art and provides researchers with an overview of existing surveillance solutions, analyzing their properties as a system and drawing attention to relevant challenges when developing, deploying and managing them. Also, some of the more prominent application domains are highlighted here. In an effort to understand the development of the advanced solutions, based on their most distinctive characteristics, we propose a taxonomy for surveillance systems to help classify them and reveal gaps in existing research. We conclude by identifying promising future research lines.

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