Abstract

Proceedings of the Academic Track at State of the Map 2020 - Online (originally planned in Cape Town, South Africa), July 4-6, 2020. Editors Marco Minghini – European Commission, Joint Research Centre (JRC), Ispra, Italy Serena Coetzee, Department of Geography, Geoinformatics and Meteorology, University of Pretoria Levente Juhász – GIS Center, Florida International University, Miami, FL, United States A. Yair Grinberger – Department of Geography, The Hebrew University of Jerusalem, Israel Peter Mooney – Department of Computer Science, Maynooth University, Maynooth, Ireland Godwin Yeboah – Institute for Global Sustainable Development, School of Cross-faculty Studies, University of Warwick, Coventry, United Kingdom

Abstract

The ability to predict correctly the future remaining life time of components is of paramount importance to improve the safety and reliability of systems and networks via an effective maintenance policy. However, simplifications and assumptions are usually adopted to compensate lack of data, imprecision and vagueness, which cannot be justified completely and may, thus lead to biased results. To overcome these issues, an imprecise probabilities approach is proposed for reliability analysis and risk-based maintenance strategy. A novel efficient computational approach is proposed for identifying robust maintenance strategies. The optimal solution is obtained through only one reliability assessment based on Advanced Line Sampling and reusing the outcome of maintenance activities in a force Monte Carlo approach. The proposed methodology remove the huge computational cost of reliability-base optimization making the analysis of industrial size problem feasible. The applicability of the approach is demonstrated by identifying the optimal maintenance policy of buried pipelines and it is shown how this approach can improve the current industrial practice. Document type: Part of book or chapter of book

Abstract

Motivated by the desire to explore future traffic flows that will consist of a mixture of classical vehicles and vehicles equipped with advanced driver assistance systems, new mathematical theories and models are developed. The basis for this theory was borrowed from the kinetic description of gas flows, where we replaced the behaviour of the molecules by typical human driving behaviour. From a methodological point of view, this 'human-kinetic' traffic flow theory provides two major improvements with respect to existing theory. Firstly, the model builds exclusively on a mathematical description of individual driver behaviour, whereas traditionally field measurements of traffic flow variables like flow rate and average speed of the flow are needed. This is of major importance for the exploration of future traffic flows with vehicles and equipment that are not yet on the market, and for which at best individual test results from driving simulator experiments or small scale field trials are available. Secondly, the model accounts for the more refined aspects of individual driver behaviour by considering the 'internal' state of the driver (active/passive, aware/unaware,...) and the variations of driving strategy that occur during driving. This is important when the ambition is to capture refined congestion patterns like the occurrence of stop-and-go waves, oscillating congestion and long jams, where the driving strategy may depend for instance on the motivation of the driver to follow closely. This new theory links together the worlds of traffic engineers and behavioural scientists. As such, it is a promising tool that increases the insight in the human behaviour as a basis of various dynamic congestion patterns, and it facilitates the design and evaluation of electronic systems in the vehicle that assist the driver to behave safer, more comfortable and more efficient in busy traffic flows. Herewith, the results of this research are relevant, both for the theoretical interest of the TRAIL research school, and for the more practically oriented work of TNO, who provided financing for this research in the joint T3 research program.

Abstract

The need to develop and deploy Beyond Visual Line of Sight (BVLOS) aerial vehicles has intensified over the last decade. As the demand for Unmanned Aircraft Systems (UAS) has increased, so too has the regulations that surrounds the industry. Strict regulations are currently in place but differ from country to country. Due to these regulations BVLOS innovators have been posed the task of exploring the means of operating flight missions with the UAV out of the sight of the pilot. Autonomous flight capability is not only fundamental to BVLOS operations for UAS but also likely to have a significant impact on the future development of passenger carrying autonomous aircraft. This review explores the technologies that have been developed to date that enable BVLOS applications. BVLOS flight operations have the potential to open a huge area of commercial opportunity however, there remain many concerns about the current capabilities of UAS to detect and avoid manned and unmanned airborne hazards that may pose a significant safety risk.

Abstract

The well-known traffic congestion problem in urban environments has negative impact on many areas including economy, environment, health and lifestyle. Recently, a number of solutions based on vehicle-to-vehicle communications were proposed for traffic congestion detection and management. In this paper we present an algorithm designed to enable each vehicle in the network to detect and quantify the level of traffic congestion in completely distributed way, independent of any supporting infrastructure and additional information such as traffic data from local authorities. Based on observations of traffic congestion by every vehicle, and by adapting the broadcast interval, it enables dissemination of the traffic information to other vehicles. The algorithm also makes every vehicle aware about the congestion level on the streets that are spatially separated from their current location by several streets. Its robustness keeps the vehicle's overall knowledge about congestion consistent, despite the short-term changes in vehicle's motion. Since the quantification of congestion is based on per-vehicle basis, the algorithm is able to operate even when only 10% of vehicles in the network are VANET enabled. Data aggregation and adaptive broadcasting are used to ensure that vehicles do not send redundant information about the traffic congestion. The simulations are conducted in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator.

Abstract

The aim of this paper is to present and apply a new evaluation framework for traffic management and Intelligent Transport Systems, to assist urban transport authorities in assessing relevant policies and technologies as to their performance. The principles behind performance measures and indices are outlined, along with a description of theframework development methodology. Two Key Performance Indicators (KPIs) for the topics of mobility and traffic accidents respectively are formulated and operative definitions are presented. Then, the new KPIs are applied to a case study in the city of Paris, involving the introduction of a scheme granting priority to buses at signalised junctions. The results from the before- and after-analysis are reported and interpreted, not only in terms of the case study itself, but most importantly from the standpoint of the applicability of the evaluation framework. Document type: Conference object

Abstract

The analysis of driving behaviour is a challenging task in the transport field that has numerous applications, ranging from highway design to micro-simulation and the development of advanced driver assistance systems. There has been evidence suggesting changes in the driving behaviour in response to changes in traffic conditions, and this is known as adaptive driving behaviour. Identifying these changes and the conditions under which they happen, and describing them in a systematic way, contributes greatly to the accuracy of micro-simulation, and more importantly to the understanding of the traffic flow, and therefore paves the way for introducing further improvements with respect to the efficiency of the transport network. In this paper adaptive driving behaviour is linked to changes in the parameters of a given car-following model. These changes are tracked using a dynamic system identification method, called particle filtering. Subsequently, the dynamic parameter estimates are further processed to identify critical points where significant changes in the system take place.

Abstract

The paper investigates the efficiency of a recently developed signal control methodology, which offers a computationally feasible technique for real-time network-wide signal control in large-scale urban traffic networks and is applicable also under congested traffic conditions. In this methodology, the traffic flow process is modeled by use of the store-and-forward modeling paradigm, and the problem of network-wide signal control (including all constraints) is formulated as a quadratic-programming problem that aims at minimizing and balancing the link queues so as to minimize the risk of queue spillback. For the application of the proposed methodology in real time, the corresponding optimization algorithm is embedded in a rolling-horizon (model-predictive) control scheme. The control strategy's efficiency and real-time feasibility is demonstrated and compared with the Linear-Quadratic approach taken by the signal control strategy TUC (Traffic-responsive Urban Control) as well as with optimized fixed-control settings via their simulation-based application to the road network of the city centre of Chania, Greece, under a number of different demand scenarios. The comparative evaluation is based on various criteria and tools including the recently proposed fundamental diagram for urban network traffic. (C) 2009 Elsevier Ltd. All rights reserved.

Abstract

Vehicular traffic congestion is a well-known economic and social problem generating significant costs and safety challenges, and increasing pollution in the cities. Current intelligent transport systems and vehicular networking technologies rely heavily on the supporting network infrastructure which is still not widely available. This paper contributes towards the development of distributed and cooperative vehicular traffic congestion detection by proposing a new vehicle-to-vehicle (V2V) congestion detection algorithm based on the IEEE 802.11p standard. The new algorithm allows vehicles to be self-aware of the traffic in the street, performing congestion detection based on speed monitoring and cooperation with the surrounding vehicles. Cooperation is achieved using adaptive single-hop broadcasting which depends on the level of congestion. The paper presents the congestion detection algorithm and the cooperative communication in detail, and presents performance evaluation using large-scale simulation in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator. Results show that precise congestion detection and quantification can be achieved using a significantly decreased number of exchanged packets.

Abstract

dvanced urban traffic control systems are often based on feed-back algorithms. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. Therefore, we need self-managing systems that can plan and act effectively in order to restore an unexpected road traffic situations into the normal order. A significant step towards this is exploiting Automated Planning techniques which can reason about unforeseen situations in the road network and come up with plans (sequences of actions) achieving a desired traffic situation. In this paper, we introduce the problem of self-management of a road traffic network as a temporal planning problem in order to effectively navigate cars throughout a road network. We demonstrate the feasibility of such a concept and discuss our preliminary evaluation in order to identify strengths and weaknesses of our approach and point to some promising directions of future research.