Abstract

Different modelling techniques from different disciplines are needed to model complex socio-technical systems and their requirements. This paper describes the application of RESCUE, a process that integrates 4 modelling techniques to model and analyse stakeholder requirements for DMAN, a system for scheduling and managing the departure of aircraft from major European airports. It describes how human activity, use case and i* modelling were applied and integrated using synchronisation checks to model requirements on DMAN. Synchronisation checks applied at predefined stages in RESCUE revealed omissions and potential inconsistencies in the models and stakeholder requirements that, in turn, led to improvements to the models and resulting specification. The paper ends with implications for requirements model integration, and describes future work to extend and apply RESCUE.

Abstract

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work focuses on notifying the operator when an anomaly occurs with a probability of the event occurring. This additional information helps in accelerating the mitigation process. The model is trained and tested using a real-world dataset.

Abstract

With the purpose of automatic detection of crowd patterns including abrupt and abnormal changes, a novel approach for extracting motion “textures” from dynamic Spatio-Temporal Volume (STV) blocks formulated by live video streams has been proposed. This paper starts from introducing the common approach for STV construction and corresponding Spatio-Temporal Texture (STT) extraction techniques. Next the crowd motion information contained within the random STT slices are evaluated based on the information entropy theory to cull the static background and noises occupying most of the STV spaces. A preprocessing step using Gabor filtering for improving the STT sampling efficiency and motion fidelity has been devised and tested. The technique has been applied on benchmarking video databases for proof-of-concept and performance evaluation. Preliminary results have shown encouraging outcomes and promising potentials for its real-world crowd monitoring and control applications.

Abstract

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities.

Abstract

A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction.\\ud \\ud We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.

Abstract

In this paper, we present our experience of using rich and detailed models of human activity in an existing socio-technical system in the domain of air traffic control to inform a use case-based specification of an enhanced future system, called DMAN. This work was carried out as part of a real project for Eurocontrol, the European Organisation for the Safety of Air Navigation. We describe, in outline, the kinds of models we used, and present some examples of the ways in which these models influenced the specification of use cases and requirements for the future system. We end with a discussion of lessons learnt.

Abstract

This paper describes our experience of integrating HCI concepts and techniques into a concurrent requirements engineering process called RESCUE. We focus on the use of a model of current human activity to inform specification of a future system. We show how human activity descriptions, written using a specially designed template, can facilitate the authoring of use case descriptions to be used in the elicitation of requirements for complex socio-technical systems. We describe our experience of using descriptions of human activity, written using the template, to support specification of operational requirements for DMAN, a system to support air traffic controllers in managing the departure of aircraft from airports. We end with a discussion of lessons learnt from our experience and present some ideas for future development of work in this area.

Abstract

Our particular research in the Distributed Analytics\\ud and Information Science International Technology Alliance\\ud (DAIS ITA) is focused on ”Anticipatory Situational Understanding\\ud for Coalitions”.\\ud This paper takes the concrete example of detecting and\\ud predicting traffic congestion in the UK road transport network\\ud from existing generic sensing sources, such as real-time CCTV\\ud imagery and video, which are publicly available for this purpose.\\ud This scenario has been chosen carefully as we believe that in\\ud a typical city, all data relevant to transport network congestion\\ud information is not generally available from a single unified source,\\ud and that different organizations in the city (e.g. the weather office,\\ud the police force, the general public, etc.) have their own different\\ud sensors which can provide information potentially relevant to\\ud the traffic congestion problem. In this paper we are looking at\\ud the problem of (a) identifying congestion using cameras that,\\ud for example, the police department may have access to, and (b)\\ud fusing that with other data from other agencies in order to (c)\\ud augment any base data provided by the official transportation\\ud department feeds. By taking this coalition approach this requires\\ud using standard cameras to do different supplementary tasks like\\ud car counting, and in this paper we examine how well those tasks\\ud can be done with RNN/CNN, and other distributed machine\\ud learning processes.\\ud In this paper we provide details of an initial four-layer\\ud architecture and potential tooling to enable rapid formation of\\ud human/machine hybrid teams in this setting, with a focus on\\ud opportunistic and distributed processing of the data at the edge\\ud of the network. In future work we plan to integrate additional\\ud data-sources to further augment the core imagery data.

Abstract

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems. Document type: Article

Abstract

Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. Although there are several factors that may cause traffic congestion, inefficiency in traffic management is always the main issue. Additionally, the most traditional methods of resolving traffic congestion or rerouting algorithm are mainly designed for individuals' benefits, by simply planning a driver's route based on minimum travel time or shortest path accordingly. There is lack of consideration in group benefit or urban development. However, with the development of technologies in Internet of Things (IoT), vehicle to vehicle (V2V) or Vehicle to Infrastructure (V2I) communications, group based routing becomes achievable. Instead of optimizing the routing path for individual drivers, this paper studies how to develop a new method to provide new routing method based on vehicles' similarities in a specific urban's transportation environment.