Abstract

JODE purposes to deliver international readers with high quality peer-reviewed academic articles on a wide variety of issues related to open and distance learning, in areas such as technology-assisted learning, computer based training, computer-aided instruction or computer assisted instruction, internet-based training, web-based training, online training, virtual training, virtual learning environments, m-learning, digital education, education on social networking, massive online open courses, game-based learning, active learning, content design, e-assessment, mobile applications, e-video, the quality of open and distance education and accreditation etc. Document type: Part of book or chapter of book

Abstract

International audience; Mapping applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline or fork graphs. Several antagonist criteria should be optimized for workflow applications, such as throughput and latency (or a combination). In this paper, we consider a simplified model with no communication cost, and we provide an exhaustive list of complexity results for different problem instances. Pipeline or fork stages can be replicated in order to increase the throughput by sending consecutive data sets onto different processors. In some cases, stages can also be data-parallelized, i.e. the computation of one single data set is shared between several processors. This leads to a decrease of the latency and an increase of the throughput. Some instances of this simple model are shown to be NP-hard, thereby exposing the inherent complexity of the mapping problem. We provide polynomial algorithms for other problem instances. Altogether, we provide solid theoretical foundations for the study of mono-criterion or bi-criteria mapping optimization problems. Document type: Report

Abstract

We consider the assignment of gates to arriving and departing flights at a large hub airport. This problem is highly complex even in planning stage when all flight arrivals and departures are assumed to be known precisely in advance. There are various considerations that are involved while assigning gates to incoming and outgoing flights (such a flight pair for the same aircraft is called a turn) at an airport. Different gates have restrictions, such as adjacency, last-in first-out gates and towing requirements, which are known from the structure and layout of the airport. Some of the cost components in the objective function of the basic assignment model include notional penalty for not being able to assign a gate to an aircraft, penalty for the cost of towing an aircraft with a long layover, and penalty for not assigning preferred gates to certain turns. One of the major contributions of this paper is to provide mathematical model for all these complex constraints that are observed at a real airport. Further, we study the problem in both planning and operations modes simultaneously, and such an attempt is, perhaps, unique and unprecedented. For planning mode, we sequentially introduce new additional objectives to our gate assignment problem that have not been studied in the literature so far(i) maximization of passenger connection revenues, (ii) minimization of zone usage costs, and (iii) maximization of gate plan robustnessand include them to the model along with the relevant constraints. For operations mode, the main objectives studied in this paper are recovery of schedule by minimizing schedule variations and maintaining feasibility by minimal retiming in the event of major disruptions. Additionally, the operations mode models must have very, very short run times of the order of a few seconds. These models are then applied to a functional airline at one of its most congested hubs. Implementation is carried out using Optimization Programming Language, and computational results for actual data sets are reported. For the planning mode, analyst perception of weights for the different objectives in the multi-objective model is used wherever actual dollar value of the objective coefficient is not available. The results are also reported for large, reasonable changes in objective function coefficients. For the operations mode, flight delays are simulated, and the performance of the model is studied. The final results indicate that it is possible to apply this model to even large real-life problems instances to optimality within short run times with clever formulation of conventional continuous time assignment model. Copyright (c) 2013 John Wiley & Sons, Ltd. Document type: Report

Abstract

Advanced Driver Assistance Systems (ADAS) already make a major contribution to driving safety. To further increase this contribution, it is, however, vital that future intelligent vehicles perceive, predict, and assess their environment more comprehensively. In this context, the present dissertation approaches the questions i) how to represent the driving environment adequately within an environment model, ii) how to obtain such a representation, and iii) how to predict the future traffic scene evolution for proper criticality assessment. Bayesian inference provides the common theoretical framework of all designed methods. Based on the shortcomings of existing environment representations, a novel parametric representation of general driving environments is first introduced in this work. It consists of a combination of dynamic object maps for moving objects and so-called Parametric Free Space (PFS) maps for static environment structures. PFS maps model the environment by a closed curve around the vehicle, which encloses relevant drivable free space. The representation compactly describes all essential information contained in common occupancy grid maps, suppresses irrelevant details, and consistently separates between static and dynamic environment objects. A novel method for grid mapping in dynamic road environments provides the basis to realize this representation. Therein, dynamic cell hypothesis are detected, clustered, and subsequently tracked and classified with an adaptive Bayesian multiple model filter for jump Markov nonlinear systems – the so-called Interacting Multiple Model Unscented Kalman Probabilistic Data Association Filter (IMM-UK-PDAF). The intermediate result is a dynamic object map and an optimized grid of the static driving environment. From the optimized grid, relevant free space is then extracted by methods of image analysis, and robustly converted to a PFS map in a final B-Spline contour tracking step. Evaluations and experiments, which were performed with an experimental vehicle equipped with radars and a stereo camera in real driving environments, confirm the advantages of the real-time capable approach. The so-obtained representation additionally forms the basis of a novel method for long-term trajectory prediction and criticality assessment. Therein, a three-layered Bayesian network is used to infer current driving maneuvers of traffic participants initially. A trash maneuver class allows the detection of irrational driving behavior and the seamless application from highly-structured to non-structured environments. Subsequently, maneuver-based prediction models in form of stochastic processes are presented and employed to predict the vehicle configurations under consideration of uncertainties in the maneuver executions. Finally, the criticality time metric Time-To-Critical-Collision-Probability (TTCCP) is introduced as a generalization of the time metric Time-To-Collision (TTC) for arbitrary, uncertain, multi-object driving environments and longer prediction horizons. The TTCCP considers all uncertain, maneuver-based predictions and is estimated via Monte Carlo simulations. Simulations confirm its potential to suppress false warnings, to generate timely true warnings, and to generate warnings in critical almost-collision situations effectively. All methods are part of the driver assistance system PRORETA 3, which has been co-developed in the context of this thesis. It constitutes a novel, integrated approach to collision avoidance and vehicle automation and thereby makes a valuable contribution to realize the Vision Zero – the vision of a future without traffic deaths.

Abstract

The increasing need for mobility has brought about significant changes in transportation infrastructures. Inefficiencies cause enormous losses of time, decrease in the level of safety for both vehicles and pedestrians, high pollution, degradation of quality of life, and huge waste of nonrenewable fossil energy.The scope of this article is to introduce novel functionality for providing knowledge to vehicles, thus jointly managing traffic and safety. This will be achieved through the design of the proposed functionality, which, at a high level, will comprise (1) sensor networks formed by vehicles of a certain vicinity that exchange traffic-related information, (2) cognitive management functionality placed inside the vehicles for inferring knowledge and experience, and (3) cognitive management functionality in the overall transportation infrastructure. The goal of the aforementioned three main components shall be to issue directives to the drivers and the overall transportation infrastructure valuable in context handling.

Abstract

In transportation planning there can be long lead times to adapt capacity. This paper addresses two questions. First, in a one mode world (say rail or road), what is the optimal capacity choice when faced with uncertain demand, long lead times and congestion. Using a simple analytical model it is shown that when demand is inelastic, it is socially optimal to invest more than if only the expected level of demand is taken into account. In this case it may be beneficial to overinvest in capacity because congestion costs are a convex function of relative use. This result holds with or without optimal tolling. The second question deals with two competing modes and where only one mode has long lead times for capacity while the other has flexible capacity. This is typical for the competition between High Speed Rail and air for the medium distance trips (500 to 1000 km), or for the competition between inland waterways and trucks for freight. We find that overinvestment is less justified because the substitute mode can more easily absorb the high demand outcomes. ispartof: CES - Discussion paper series (DPS) 08.09 pages:1-20 status: published

Abstract

Because the payload that an airplane can carry is a function of the longest segment of its route and differs from route to route, the usual algorithms of network flow theory will not yield the optimum flow of payload. In this paper two network algorithms are described. They determine the route of maximum payload flow per hour of flight time and the maximum steady-state payload flow through a network with capacity constraints on the nodes.

Abstract

traffic control (ATC) simulator and on-the-job training requires a valid and reliable assessment system. Competence-based assessment results in more effective learning processes and better founded pass–fail decisions, which may contribute to an increased output of competent controllers from training. At Air Traffic Control The Netherlands (LVNL) we designed an assessment system based on a newly defined set of competences. The ATC Performance Model, derived from this competence analysis, has served as a framework for the design. This article describes the background, the design process, the properties, and the evaluation results of the competence-based assessment system in use by LVNL.

Abstract

Electric buses are particularly suitable for city and suburban routes due to zero local exhaust and noise emissions. The operation schedule interval defines the charging power, bus fleet size and total cost of ownership of a bus. We propose a novel cost-benefit method for the scheduling of an electric city bus fleet on a single route. Three different charging infrastructure scenarios were considered. In the first scenario, only one charging station was used. The second scenario considered two charging stations that were located at the same terminus. In the third scenario, two charging stations were located at opposite terminuses. The costs and utilization rates of the buses were analyzed with operation intervals up to 40 minutes. The first scenario with a single charging station had the lowest costs for the entire bus fleet system when the utilization rate was considered. Furthermore, the results show that certain schedule intervals are more cost-beneficial in terms of vehicle specific life-cycle costs than others. In the future, the proposed method is expanded to aid the design of bus network scheduling under energy demand uncertainty. Peer reviewed

Abstract

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts 1) archival and dissemination services for raw and curated data, together with their provenance graph, 2) modelling services and virtual machines, 3) tools for data analytics, and pre-/post-processing, and 4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of the FAIR principles that extends to computational workflows. Materials Cloud leverages the AiiDA framework to record the provenance of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow to retrace and reproduce any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain. Comment: 19 pages, 8 figure Document type: Article