Scipedia: Documents published in 2019

Using Multi-Agent Transport Simulations to Assess the Impact of EV Charging Infrastructure Deployment

Scipedia content — Tue, 02 Feb 2021 03:19:15 +0100

Over the last two decades, electrification has gained importance as a means to decarbonise the transport sector. As the number of Electric Vehicles (EVs)increases, it is important to consider broader system aspects as well, especially when deciding the type, coverage, size and location of the charging infrastructure required. In this article, a Multi-Agent model depicting long distance transport in Sweden is proposed, allowing to simulate different scenarios and enabling a more detailed analysis of the interaction between these vehicles and the charging infrastructure.

Cold Chain Strategies for Seaports

Scipedia content — Tue, 02 Feb 2021 03:17:35 +0100

The refrigerated (‘reefer’) container market and cold logistics chains creates opportunities as well as challenges for seaports. This high-value market grows rapidly, but places stringent demands on seaports’ logistics processes, infrastructure, and energy provision. This study addresses the question how port authorities address the challenges and in this dynamic market environment. While previous research has outlined developments in port governance paradigms and the strategic scope of port authorities, the academic literature still lacks a comprehensive understanding of the policy options available to port authorities to respond to arising challenges and opportunities. To provide this missing understanding, this study presents a new dataset of policies, implemented by world’s 50 largest container ports, addressing reefer transportation and cold chain logistics. Policy measures are classified according to content, goals and scope. The findings from this worldwide comparative analysis illustrate that port authorities often pursue policies extending far beyond their traditional ‘landlord’ responsibility. Most commonly still, the scope of port policy is limited to the port cluster, where ports (co)-invest in or aim for cluster formation around cold stores. When a port extends its strategic scope towards its foreland or hinterland, this is usually aligned with policy goals formulated at higher levels of governance, such as modal shift goals or the development of domestic post-harvest distribution systems. There is however little evidence of coherent and comprehensive cold chain strategies, addressing the logistics, marketing, technology, and sustainability dimensions. The paper outlines the general tenets such a strategy should contain as a consideration for policymakers.

A Cloud-based Framework for Implementing Portable Machine Learning Pipelines for Neural Data Analysis

Scipedia content — Tue, 02 Feb 2021 03:15:23 +0100

Cloud-based computing has created new avenues for innovative research. In recent years, numerous cloud-based, data analysis projects within the biomedical domain have been implemented. As this field is likely to grow, there is a need for a unified platform for the developing and testing of advanced analytic and modeling tools that enables those tools to be easily reused for the analysis of biomedical data by a broad set of users with diverse technical skills. A cloud-based platform of this nature could greatly assist future research endeavors. In this paper, we take the first step towards building such a platform. We define an approach by which containerized analytic pipelines can be distributed for use on cloud-based or on-premise computing platforms. We demonstrate our approach by implementing a portable biomarker identification pipeline using a logistic regression model with elastic net regularization (LR-ENR) and running it on the Google cloud. We used this pipeline for the diagnosis of Parkinson’s disease based on a combination of clinical, demographic, and MRI-based features and for the identification of the most predictive biomarkers.

Requirements for Electric Machine Design based on Operating Points from Real Driving Data in Cities

Scipedia content — Tue, 02 Feb 2021 03:10:15 +0100

Increasing environmental awareness leads to the necessity for more efficient powertrains in the future. However, the development of new vehicle concepts generates a trend towards ever shorter development cycles. Therefore, new concepts must be tested and validated at an early stage in order to meet the increasing time pressure. This requires the determination of real driving data in fleet tests in order to generate realistic driving cycles, which correspond as closely as possible to the actual driving behavior of the applications use case. Within the scope of this paper, real driving data are analyzed and used to create a representative driving cycle. The resulting driving cycle based on real driving characteristics is then used to investigate the impact of application-based design for powertrains on the design of electric machines, by illustrating the difference between synthetic operating points and real driving data.

Document type: Article

A Stochastic Optimization Model for Commodity Rebalancing Under Traffic Congestion in Disaster Response

Scipedia content — Tue, 02 Feb 2021 03:05:31 +0100

Part 1: Smart Supply Networks; International audience; After a large-scale disaster, the emergency commodity should be distributed to relief centers. However, the initial commodity distribution may be unbalanced due to the incomplete information and uncertain environment. It is necessary to rebalance the emergency commodity among relief centers. Traffic congestion is an important factor to delay delivery of the commodity. Neither the commodity rebalancing nor traffic congestion is considered in previous studies. In this study, a two-stage stochastic optimization model is proposed to manage the commodity rebalancing, where uncertainties of demand and supply are considered. The goals are to minimize the expected total weighted unmet demand in the first stage and minimize the total transportation time in the second stage. Finally, a numerical analysis is conducted for a randomly generated instance; the results illustrate the effectiveness of the proposed model in the commodity rebalancing over the transportation network with traffic congestion.

Document type: Conference object

User Profiling Based on Application-Level Using Network Metadata

Scipedia content — Tue, 02 Feb 2021 03:03:37 +0100

There is an increasing interest to identify users and behaviour profiling from network traffic metadata for traffic engineering and security monitoring. Network security administrators and internet service providers need to create the user behaviour traffic profile to make an informed decision about policing, traffic management, and investigate the different network security perspectives. Additionally, the analysis of network traffic metadata and extraction of feature sets to understand trends in application usage can be significant in terms of identifying and profiling the user by representing the user's activity. However, user identification and behaviour profiling in real-time network management remains a challenge, as the behaviour and underline interaction of network applications are permanently changing. In parallel, user behaviour is also changing and adapting, as the online interaction environment changes. Also, the challenge is how to adequately describe the user activity among generic network traffic in terms of identifying the user and his changing behaviour over time. In this paper, we propose a novel mechanism for user identification and behaviour profiling and analysing individual usage per application. The research considered the application-level flow sessions identified based on Domain Name System filtering criteria and timing resolution bins (24-hour timing bins) leading to an extended set of features. Validation of the module was conducted by collecting Net Flow records for a 60 days from 23 users. A gradient boosting supervised machine learning algorithm was leveraged for modelling user identification based upon the selected features. The proposed method yields an accuracy for identifying a user based on the proposed features up to 74%

Analyzing the spatio-temporal patterns and impacts of large-scale data production events in OpenStreetMap

Scipedia content — Tue, 02 Feb 2021 02:58:28 +0100

Grinberger et al. (2019). Analyzing the spatio-temporal patterns and impacts of large-scale data production events in OpenStreetMap In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 9-10. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387671

РОЗПОДІЛЕНА ДИНАМІЧНА PDE-МОДЕЛЬ ПРОГРАМНОГО КЕРУВАННЯ ЗАВАНТАЖЕННЯМИ ТЕХНОЛОГІЧНОГО ОБЛАДНАННЯ ВИРОБНИЧОЇ ЛІНІЇ

Scipedia content — Tue, 02 Feb 2021 02:57:56 +0100

Purpose. The article is aimed at designing a control system for the parameters of a production line for an enterprise with a straight flow method of organizing production. Methodology. The production line at the enterprise with a straight flow method of organizing production is a complex dynamic distributed system. The flow route for manufacturing a product for many modern enterprises contains several hundreds of technological operations, in the inter-operating reserve each of which there are thousands of products waiting to be processed. The flow routes of different parts of the same type of products intersect (re-entrant manufacturing systems). This leads to the fact that the distribution of subjects of labor along the technological route has a significant impact on the throughput capacity of the production line. To describe such systems, a new class of production line models (PDE-model) has been introduced. To describe the behavior of the flow parameters of the production line, a production line model containing partial differential equations (PDE model) was used. The PDE-model of the production line is built in the article, the flow parameters of which depend on the value of utilization rate of the technological equipment for each operation. Findings. The authors obtained the optimal control of the flow parameters of the production line, which is based on the algorithm for changing the utilization rate of the technological equipment of the production line. The single-shift working time pattern is considered as a basic regulatory treatment of the production line operation. To simulate the work of technological equipment after the shift, the generalized Dirac function was used. Originality consists in the development of a method for designing control systems for the parameters of the production line of enterprises with a straight flow method of organizing production based on the PDE-model of the control object. The authors proposed a method for constructing an optimal control of the parameters of the production line through the control of the utilization rate of the technological equipment. When designing a control system, the production line is represented by a dynamic system with distributed flow parameters. Practical value. The proposed method for designing a control system for the flow parameters of a production line can be used as the basis for designing highly efficient production flow control systems for enterprises manufacturing semiconductor products of the automobile industry.

Цель. В работе необходимо рассмотреть проектирование системы управления параметрами производственной линии для предприятия с поточным методом организации производства. Методика. Производственная линия предприятия с поточным методом организации производства – это сложная динамическая распределенная система. Технологический маршрут изготовления изделия для многих современных предприятий содержит несколько сотен технологических операций, в межоперационном заделе каждой из которых содержатся тысячи изделий, ожидающих обработку. Технологические маршруты разных деталей одного вида изделий пересекаются. Это приводит к тому, что распределение предметов труда вдоль технологического маршрута оказывает значительное влияние на пропускную способность производственной линии. Для описания таких систем введен новый класс моделей производственных линий (PDE-model). Модели этого класса используют уравнения в частных производных для описания поведения потоковых параметров производственной линии. В данной статье построена PDE-модель производственной линии, потоковые параметры которой зависят от величины коэффициента загрузки технологического оборудования для каждой операции. Результаты. Авторы получили оптимальное управление потоковыми параметрами производственной линии, в основу которого положен алгоритм изменения коэффициента загрузки технологического оборудования производственной линии. В качестве базового нормативного режима функционирования поточной линии рассмотрен односменный режим работы. Для моделирования работы технологического оборудования после смены использована обобщенная функция Дирака. Научная новизна заключается в разработке метода проектирования систем управления параметрами производственной линии предприятий с поточным методом организации производства, основанного на PDE-модели объекта управления. Авторы предложили метод построения оптимального управления параметрами поточной линии через управление коэффициентом загрузки технологического оборудования. При проектировании системы управления поточная линия представлена динамической системой с распределенными потоковыми параметрами. Практическая значимость. Предложенный метод проектирования системы управления потоковыми параметрами производственной линии может быть положен в основу проектирования высокоэффективных систем управления потоковыми параметрами производства для предприятий по изготовлению полупроводниковой продукции автомобильной отрасли.

Мета. У роботі необхідно розглянути проектування системи управління параметрами виробничої лінії для підприємства з потоковим методом організації виробництва. Методика. Виробнича лінія підприємства з потоковим методом організації виробництва – це складна динамічна розподілена система. Технологічний шлях виготовлення продукції для багатьох сучасних підприємств містить кілька сотень технологічних операцій, в міжопераційному резерві кожної з яких є тисячі продуктів, що чекають на обробку. Технологічні маршрути різних деталей одного виду виробів перетинаються. Це призводить до того, що розподіл предметів праці уздовж технологічного маршруту значно впливає на пропускну здатність виробничої лінії. Для опису таких систем введений новий клас моделей виробничих ліній (PDE-model). Моделі цього класу використовують рівняння в частинних похідних для опису поведінки потокових параметрів виробничої лінії. У цій статті побудована PDE-модель виробничої лінії, потокові параметри якої залежать від величини коефіцієнта завантаження технологічного обладнання для кожної операції. Результати. Автори отримали оптимальне управління потоковими параметрами виробничої лінії, в основу покладено алгоритм зміни коефіцієнта завантаження технологічного обладнання виробничої лінії. Як базовий нормативний режим функціонування потокової лінії розглянуто однозмінний режим роботи. Наукова новизна полягає в розробці методу проектування систем управління параметрами виробничої лінії підприємств із потоковим методом організації виробництва, заснованого на PDE-моделі об’єкта управління. Автори запропонували метод побудови оптимального управління параметрами потокової лінії через управління коефіцієнтом завантаження технологічного обладнання. Під час проектування системи управління потокова лінія представлена динамічною розподіленою системою. Практична значимість. Запропонований метод проектування системи управління потоковими параметрами виробничої лінії може бути покладено в основу проектування високоефективних систем управління потоковими параметрами виробництва для підприємств із виготовлення напівпровідникової продукції автомобільної галузі.

UNICARagil - Where We Are and Where We Are Going

Scipedia content — Tue, 02 Feb 2021 02:45:25 +0100

Road Operations Orchestration Enhanced with Long-short-term Memory and Machine Learning (Position Paper)

Scipedia content — Tue, 02 Feb 2021 02:35:27 +0100

Road traffic management has been a priority for urban city planners to mitigate urban traffic congestion. In 2018, the economic impact to US due to lost productivity of workers sitting in traffic, increased cost of transporting goods through congested areas, and all of that wasted fuel amounted to US$87 billion, an average of US$1,348 per driver. In land scare Singapore, congestion not only translates to economic impact, but also strain to the infrastructure and city land use. While techniques for traffic prediction have existed for many years, the research effort has mainly been focused on traffic prediction. The downstream impact on how city administration should predict and react to incidents and/or events has not been widely discussed. In this paper, we propose Artificial Intelligence enabled Complex Event Processing to only identify and predict incidents, but also to enable a swift response through effective deployment of critical resources to ensure well-coordinated recovery action before any incident develop into crisis.

A methodology to design and assess scenarios within SULPs: the case of Bologna

Scipedia content — Tue, 02 Feb 2021 02:33:38 +0100

The paper, focusing on the importance to develop sustainable urban logistics plan (SULP) and to implement a demand model system for the assessment of future scenarios, presents a methodology for setting up a SULP modelling, using different sources of data (i.e. automatic traffic counts, floating car data, surveys with retailers and transport operators). The methodology is applied to the functional urban area (FUA) of Bologna (Italy). In particular, it was used for assessing the new city logistics scenarios of the Bologna’s SULP where a set of measures have been proposed for improving city sustainability and livability.

Bridging the Map? Exploring Interactions between the Academic and Mapping Communities in OpenStreetMap

Scipedia content — Tue, 02 Feb 2021 02:33:12 +0100

Grinberger et al. (2019). Bridging the Map? Exploring Interactions between the Academic and Mapping Communities in OpenStreetMap In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 1-2. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3408639

A Rule Based Reasoning System for Initiating Passive ADAS Warnings Without Driving Distraction Through an Ontological Approach

Scipedia content — Tue, 02 Feb 2021 02:32:27 +0100

DAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and comfort. Unlike active ADAS which provide direct intervention to avoid accidents, passive ADAS increase driver's awareness of hazardous situations by giving warnings in advance. It has been noted that these systems can cause distraction when the relevant HMIs (Human-Machine Interfaces) are poorly designed. Current research is limited to address this problem in specific settings which may not be applicable in wider context. This papers aims to provide a universal rule-based solution to allow passive ADAS to initiate warnings without triggering driver distraction through an ontological approach.

Determining optimal intervention programs for large railway infrastructure networks using a genetic algorithm

Scipedia content — Tue, 02 Feb 2021 02:30:46 +0100

A multi-criteria decision making approach for the evaluation of roads and streets system in Gniezno

Scipedia content — Tue, 02 Feb 2021 02:30:03 +0100

The article presents the application of the MCDM methods, belonging to the PROMETHEE family, for the evaluation of potential solutions of the road system (RS) in the selected area located in Gniezno, historical capital of Poland. The proposed set of heuristics variants of RS were assessed by a coherent family of criteria taking into account different groups of stakeholders. The decision problem was defined as an issue of prioritising a finite number of variants of road-rail system reconstruction. The proposed model of decision-maker’s preferences was developed based on the results of surveys conducted during public consultations with the residents of the area. The originality of the study consists in that the model became the basis for the final variants ranking that was subsequently compared with the results obtained using another MCDM method – ELECTRE III, where the decision-maker's preference model was developed on the basis of information obtained from independent experts.

Dwell Time Delays for Commuter Trains in Stockholm and Tokyo

Scipedia content — Tue, 02 Feb 2021 02:25:48 +0100

The paper analyses dwell time delays for commuter trains in Stockholm andTokyo. In both cities, small dwell time delays of at most five minutes makeup around 90% of the total delays. Therefore, it is valuable to understandand deal with these disturbances. To this end, we use high resolution data ondwell times and passenger counts from both countries over the last severalyears. We find that these data alone can explain about 40% of the variationin dwell time delays and produce simple models which can be used inpractice to assign more appropriate dwell times. A change of 15 passengersper car, in Tokyo translates to a delay of about one second. For every 10remaining passengers per door in Stockholm, the delay increases by aboutone second, and one boarding or alighting passenger per door correspondsto about 0.4 seconds of delay. We also find that trains in Tokyo are muchmore congested than in Sweden, and that at most stations in the latter, theexchange of passengers is modest. In both cities, the range of dwell timedelays is quite narrow, with between 40 and 50 seconds separating the 5thand 95th percentiles. This indicates further that most delays, by far, are verysmall, and that even small adjustments to dwell times can make a bigdifference in the overall picture. To facilitate such improvements, keystakeholders and practitioners are closely involved with the research.

FIRE AND EXPLOSION MODELLING FOLLOWING THE ACCIDENTAL FAILURE OF HIGH PRESSURE ETHYLENE TRANSPORTATION PIPELINES

Scipedia content — Tue, 02 Feb 2021 02:16:58 +0100

This paper presents the development and application of a failure consequence mathematical model for predicting the incident heat flux and explosion over-pressure following the accidental rupture of high pressure ethylene transportation pipelines. The transient discharge rate and the fluid phase at the pipe breach are determined based on the numerical solution of the conservation equations using the Method of Characteristics. The flow model accounts for the important processes taking place during the depressurization process; these include real fluid behaviour, fluid/wall heat transfer and frictional effects. To model the immediate ignition of the escaping high pressure ethylene released, the transient outflow model serving as the source term is linked to the widely established Chamberlain semi-empirical jet fire model to predict the resulting jet flame characteristics including its dimensions and incident heat flux as function of time and distance from the breach location. To deal with a delayed ignition, the source term flow model is linked to the TNO Multi-Energy Vapour Cloud Explosion model to predict the resulting explosion over-pressure and hence the subsequent harm to people and surrounding structures. Simulation results using the model are presented and discussed for the full rupture of a typical 20 km long, 250 mm i.d steel pipeline transporting ethylene at 50 bar and 5 oC.

Experimental investigation of dynamic deformation in main gas pipelines

Scipedia content — Tue, 02 Feb 2021 02:14:46 +0100

Main gas pipelines are critical engineering structures, and, therefore, their safe operation is an urgent scientific and technical problem. This paper describes a series of experimental studies conducted in one section of an actual main gas pipeline. The goal is to analyze oscillatory wave processes taking place in a gas pipeline in response to various external effects, such as impact loads applied to the ground in the vicinity of the pipeline, operation of the stop valves (valve switching from the full open to full closed position, or vice versa), and gas release through the holes of different diameters simulating the appearance of a fistula in a pipeline wall. Based on the obtained experimental results, the specific features of the propagation of oscillatory processes are established, and the conditions of a possibility of recording these processes by fiber-optic accelerometers are determined.Main gas pipelines are critical engineering structures, and, therefore, their safe operation is an urgent scientific and technical problem. This paper describes a series of experimental studies conducted in one section of an actual main gas pipeline. The goal is to analyze oscillatory wave processes taking place in a gas pipeline in response to various external effects, such as impact loads applied to the ground in the vicinity of the pipeline, operation of the stop valves (valve switching from the full open to full closed position, or vice versa), and gas release through the holes of different diameters simulating the appearance of a fistula in a pipeline wall. Based on the obtained experimental results, the specific features of the propagation of oscillatory processes are established, and the conditions of a possibility of recording these processes by fiber-optic accelerometers are determined.

Achieving Sustainable Public Transportation in the Era of Online Transportation

Scipedia content — Tue, 02 Feb 2021 02:13:38 +0100

Data-driven Conflict Detection Enhancement in Closest Point of Approach Problem

Scipedia content — Tue, 02 Feb 2021 02:11:28 +0100

International audience; Closest Point of Approach (CPA) is one of the main problems in aircraft Conflict Detection (CD). It aims to find out the minimum distance and the associated time between two aircraft. Conventional CPA calculation algorithm generally assumes that the speed and heading of aircraft are constant. But the uncertainties in real operational scenarios lead to inaccuracy of CPA calculation. This project presents a novel data-driven CD framework with Machine Learning (ML) algorithms. The proposed framework provides a promising solution for improving the CPA prediction accuracy with the help of real trajectory data. It contributes to not only reduce the number of fault Short-mid term conflict alert for air traffic controllers, but also support the implementation of future free flight concept, so as to reduce fuel consumption and emission.

From Shared electric Mobility Providers (SeMPs) to electric Mobility as a Service (eMaaS) players

Scipedia content — Tue, 02 Feb 2021 02:09:19 +0100

In this paper we present an approach to evaluate to what extent Mobility Service Providers (MSPs) can be considered (e)MaaS players. Following that approach, we conduct an analysis of 128 MSPs, specifically Shared electric Mobility Providers (SeMPs), currently operating in the European market. The goal of the analysis is twofold. Firstly, it aims at demonstrating the applicability of the proposed approach. Secondly, it aims at offering an overview of the current state of the market concerning the Technical Level of Inte- gration (TLI) of European SeMPs. Our results show that, on the one hand, most of the SeMPs currently operating in Europe have a medium to high TLI. However, those levels are mostly not applicable for multi- modal (i.e. for multiple modes of transport or multiple MSPs) interfaces but for single-mode interfaces. On the other hand, our results also show that there are already some SeMPs in the current European market that have fully integrated functionalities, in that case, SeMPs mostly have multimodal interfaces. Based on the analysis and discussion presented in this paper, we concluded that the TLI approach offers an effective technique to determine, and easily visualize, the level of integration of the technical functionalities of MSPs.

Current situation and some problem of BRT Jakarta

Scipedia content — Tue, 02 Feb 2021 02:06:23 +0100

Traffic congestion was the main problem in major cities in Indonesia, including DKI Jakarta, Bogor, Oepok, Tangerang, and Bekasi. To solve such a problem, the government initiated to build a BRT (Bus Rapid Transit) as known as Transjakarta. Transjakarta was the first Bus Rapid Transit transportation system in South East and South Asia. The main focus for this research was The Corridor 13 Transjakarta because of it was the only one which had a Busway Special Fly Over for 9,3 km known as a JLKB (Busway Special Flyway) connecting The Ciledug Raya and Tendean street. This research aimed to measure the customer’s satisfaction towards the service of The Corridor 13 Transjakarta. The analytical method used in this research was the customer satisfaction index. The research indicated people still not satisfied yet towards the service of Corridor 13 according to the result of CSI was only 44%, and there were unsatisfying-variables which found on The Corridor 13 Transjakarta that needs to be improved.Traffic congestion was the main problem in major cities in Indonesia, including DKI Jakarta, Bogor, Oepok, Tangerang, and Bekasi. To solve such a problem, the government initiated to build a BRT (Bus Rapid Transit) as known as Transjakarta. Transjakarta was the first Bus Rapid Transit transportation system in South East and South Asia. The main focus for this research was The Corridor 13 Transjakarta because of it was the only one which had a Busway Special Fly Over for 9,3 km known as a JLKB (Busway Special Flyway) connecting The Ciledug Raya and Tendean street. This research aimed to measure the customer’s satisfaction towards the service of The Corridor 13 Transjakarta. The analytical method used in this research was the customer satisfaction index. The research indicated people still not satisfied yet towards the service of Corridor 13 according to the result of CSI was only 44%, and there were unsatisfying-variables which found on The Corridor 13 Transjakarta that needs to be improved.

Continuous Flight Rescheduling Problem Resolution Based on Genetic Algorithms

Scipedia content — Tue, 02 Feb 2021 02:04:53 +0100

This present paper deals with air traffic management problem for the continuous flights with stopover and returning at initial airport. The initial scheduling is disrupted by poor weather conditions, which may change over time. For this problem, we consider the air traffic as a discrete event system where the rescheduled flights are modelled by time Petri net tool. As a resolution approach for this problem, a genetic algorithm is introduced where a new encoding of flight plans is proposed. The feasibility of generated solutions, by genetic algorithm, is checked by means of our recently approach so-called Time Reduced Ordered Binary Decision Diagrams (TROBDDs). A numerical example is provided to show that the proposed genetic algorithm exhibits a much better quality of routing solution and a much higher rate of convergence than other algorithms.

Context Aware Road-user Importance Estimation (iCARE)

Scipedia content — Tue, 02 Feb 2021 01:50:46 +0100

Road-users are a critical part of decision-making for both self-driving cars and driver assistance systems. Some road-users, however, are more important for decision-making than others because of their respective intentions, ego vehicle's intention and their effects on each other. In this paper, we propose a novel architecture for road-user importance estimation which takes advantage of the local and global context of the scene. For local context, the model exploits the appearance of the road users (which captures orientation, intention, etc.) and their location relative to ego-vehicle. The global context in our model is defined based on the feature map of the convolutional layer of the module which predicts the future path of the ego-vehicle and contains rich global information of the scene (e.g., infrastructure, road lanes, etc.), as well as the ego vehicle's intention information. Moreover, this paper introduces a new data set of real-world driving, concentrated around inter-sections and includes annotations of important road users. Systematic evaluations of our proposed method against several baselines show promising results.

Comment: Published in: IEEE Intelligent Vehicles (IV), 2019

Looking into the future: weaving the threads of vehicle automation: weaving the threads of vehicle automation

Scipedia content — Tue, 02 Feb 2021 01:47:48 +0100

utomated driving is one of the most discussed disruptive technologies of this decade. It promises to increase drivers’ safety and comfort, improve traffic flow, and lower fuel consumption. This has a significant impact on our everyday life and mobility behavior. Beyond the passengers of the vehicle, it also impacts others, for example by lowering the barriers to visit distant relatives. In line with theCHI2019 conference theme, our aim is to weave the threads of vehicle automation by gathering people from different disciplines, cultures, sectors, communities, and backgrounds (designers, researchers,and practitioners) in one community to look into concrete future scenarios of driving automation and its impact on HCI research and practice. Using design fiction, we will look into the future and use this fiction to guide discussions on how automated driving can be made a technology that works for people and society.

Flambé: A Customizable Framework for Machine Learning Experiments

Scipedia content — Tue, 02 Feb 2021 01:47:28 +0100

Flambe is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambe’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flambe achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.

TEAVAR

Scipedia content — Tue, 02 Feb 2021 01:46:46 +0100

To keep up with the continuous growth in demand, cloud providers spend millions of dollars augmenting the capacity of their wide-area backbones and devote significant effort to efficiently utilizing WAN capacity. A key challenge is striking a good balance between network utilization and availability, as these are inherently at odds; a highly utilized network might not be able to withstand unexpected traffic shifts resulting from link/node failures. We advocate a novel approach to this challenge that draws inspiration from financial risk theory: leverage empirical data to generate a probabilistic model of network failures and maximize bandwidth allocation to network users subject to an operator-specified availability target. Our approach enables network operators to strike the utilization-availability balance that best suits their goals and operational reality. We present TEAVAR (Traffic Engineering Applying Value at Risk), a system that realizes this risk management approach to traffic engineering (TE). We compare TEAVAR to state-of-the-art TE solutions through extensive simulations across many network topologies, failure scenarios, and traffic patterns, including benchmarks extrapolated from Microsoft's WAN. Our results show that with TEAVAR, operators can support up to twice as much throughput as state-of-the-art TE schemes, at the same level of availability.

Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1

Scipedia content — Tue, 02 Feb 2021 01:45:24 +0100

We participated in Task 1 of the Social Media Mining for Health Applications (SMM4H) 2019 Shared Tasks on detecting mentions of adverse drug events (ADEs) in tweets. Our approach relied on a text processing pipeline for tweets, and training traditional machine learning and deep learning models. Our submitted runs performed above average for the task.

A Dynamic Grid-Based Algorithm for Taxi Ridesharing in Multiple Road Condition

Scipedia content — Tue, 02 Feb 2021 01:40:54 +0100

the way of easing urban traffic congestion, taxi ridesharing can effectively protect the environment and solve the difficulty of passengers taking taxis when taxi demand is high. In this paper, we formally define the dynamic ride-sharing problem and propose a taxi candidates-reduction ride-sharing scheduling algorithm based on dynamic grid. Regarding the congestion situation of the multiple road condition, the concept of speed decay zone was purposed to simulate this area. To solve the problem of low satisfaction in the congested situation, we devise a dynamic grid division strategy that reduces the grid size of the hotspot area to satisfy the specific needs of passengers in rush hour, and efficiently screen candidate taxis by dynamic grid index. We perform the experiments using the request dataset generated by the taxi request simulator of Beijing Chaoyang district. The performance shows that our approach reduce 35.5% computation without losing average satisfaction compared with existing ridesharing algorithm.

Extremal thermal loading of a bifurcation pipe

Scipedia content — Tue, 02 Feb 2021 01:33:58 +0100

The subject of considerations is a spherical bifurcation pipe of a live steam made of steel P91, which is an element of a block of coal-fired power plant working with a 18K370 turbine. As experience shows, it is a very sensitive element of the boiler pipelines. An extreme work scenario for such a block has been adopted, in which the turbine is shutting down to a warm state three times in 24 hours. This is an action dictated by new challenges in the field of electricity network regulation, caused by increasing share of renewable energy sources. A one-sided numerical thermal-FSI analysis was performed. The focus was on hoop stresses as the most significant for the bifurcation pipe durability. The daily runs of these stresses at three points of the thickness of the bifurcation pipe sphere have been presented. Mechanical stresses derived from pressure and thermal stresses derived from temperature changes have been isolated. It has been shown that depending on the thermal load, some areas of the cross section are compressed while adjacent ones are stretched and vice versa. Thus, the mechanical stresses can be reduced under thermal conditions by thermal stresses. It has been proven that the bifurcation pipe is able to withstand the given extreme loads with stresses more than twice smaller than the yield point at a given operating temperature.

Investigating Initial Driver Intention on Overtaking on Rural Roads

Scipedia content — Tue, 02 Feb 2021 01:32:23 +0100

Driver intention recognition is essential to the development of advanced driver assistance systems providing real-time support. Current approaches for the recognition of overtaking intentions focus on drivers’ observable behaviors, neglecting the fact that the intention to overtake a slower lead car emerges earlier than the resulting behavior. This paper aims to distinguish the "intention emerging process", when drivers form the initial intention to overtake, from the "action executing process", when drivers execute the overtaking maneuver. A driving simulator study has been conducted to investigate the influence of the lead vehicle type and lead vehicle speed on initiating driver’ intention on overtaking on rural roads, and the effect of the complexity of the oncoming traffic on executing overtaking. The results show that the initial driver intention to overtake appears much earlier than the execution of the overtaking maneuver. The lead vehicle speed has a significant influence on initial driver intention in the "intention emerging process", while time to overtake increases with the number of the oncoming vehicles in the "action execution process". These results can contribute to the development of models for driver intention recognition by extending the prediction horizon from the recognition to a prediction of driving maneuvers.

Document type: Conference object

Unsupervised Traffic Accident Detection in First-Person Videos

Scipedia content — Tue, 02 Feb 2021 01:27:30 +0100

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly detection suffers from two crucial drawbacks. First, they assume cameras are fixed and videos have static backgrounds, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, they pose the problem as one-class classification, relying on arduously hand-labeled training datasets that limit recognition to anomaly categories that have been explicitly trained. This paper proposes an unsupervised approach for traffic accident detection in first-person (dashboard-mounted camera) videos. Our major novelty is to detect anomalies by predicting the future locations of traffic participants and then monitoring the prediction accuracy and consistency metrics with three different strategies. We evaluate our approach using a new dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as another publicly-available dataset. Experimental results show that our approach outperforms the state-of-the-art.

Comment: Accepted to IROS 2019

Study Of The Implementation Of Regulations In The Framework For Sustainable Transportation

Scipedia content — Tue, 02 Feb 2021 01:23:43 +0100

L-band Digital Aeronautical Communications System (LDACS) – Technical Validations in SESAR2020

Scipedia content — Tue, 02 Feb 2021 01:17:34 +0100

Long-term air traffic management (ATM) concepts for Europe and other regions with rapidly increasing number of aircraft movements require modern and performant mobile data links for safety-related air-ground communications. L-band Digital Aeronautical Communications System (LDACS) is one of the new technologies, which is tailored to the specific needs of the ATM community and developed in the Single European Sky ATM Research (SESAR) program. This paper illustrates results of the technical validations of LDACS carried-out in the SESAR2020 project “PJ.14 02 01 Future Communication Infrastructure (FCI) Terrestrial Data Link”, focusing on aspects related to LDACS physical layer and data link layer.

An Artificial Intelligence Approach to Estimate Travel Time along Public Transportation Bus Lines

Scipedia content — Tue, 02 Feb 2021 01:13:43 +0100

Public transportation sectors have played significant roles in accommodating passengers and commodities efficiently and effectively. The modes of public transportation often follow pre-defined operation schedules and routes. Therefore, planning these schedules and routes requires extensive efforts in analyzing the built environment and collecting demand data. Once a transit route is operational as an example, collecting and maintaining real-life information becomes an important task to evaluate service quality using different Key Performance Indicators (KPIs). One of these KPIs is transit travel time along the route. This paper aims to develop a transit travel time prediction model using an artificial intelligence approach. In this study, 12 public bus routes serving the Greater City of Doha were selected. While the ultimate goal is to predict transit travel time from the start to the end of the journeys collected over a period of one-year, routespecific inputs were used as inputs for this prediction. To develop a generalized model, the input variables for the transit route included the number and type of intersections, number of each type of turning movements and the built environment. An Artificial Neural Networks (ANN) model is used to process 78,004 valid datasets. The results indicate that the ANN model is capable of providing reliable and accurate transit travel time estimates, with a coefficient of determination (R2) of 0.95. Transportation planners and public transportation operators can use the developed model as a tool to estimate the transit travel time.

Joint route guidance and demand management for multi-region traffic networks

Scipedia content — Tue, 02 Feb 2021 01:00:50 +0100

Traffic congestion occurs as demand surpasses the available capacity of a road network, resulting to lower speeds and longer journey times; with route guidance constituting the primary control strategy to alleviate the problem. However, the effectiveness of route guidance is limited in high-demand conditions. In this work, we proposed a Model Predictive Control (MPC) framework that combines multi-regional route guidance with a novel demand management method. Route guidance is used to minimize the network's density imbalance while demand management is utilized to reduce the conditions that cause congestion. This can be achieved by manipulating vehicle routes (i.e., using route guidance) and/or by instructing a portion of the vehicles to wait at their origin before commencing their journey (demand management). Simulations are conducted to evaluate the performance of the proposed MPC optimization indicating the substantial improvements that can be achieved in traffic flow performance. Cyprus Research Promotion Foundation the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development and through the Research Promotion Foundation (Project: CULTURE/BR-NE/0517/14) © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works. Menelaou, C., Timotheou, S., Kolios, P. and Panayiotou, C.G., 2019, June. "Joint route guidance and demand management for multi-region traffic networks," 2019 18th European Control Conference (ECC), Naples, pp. 2183-2188, IEEE. doi:10.23919/ECC.2019.8795819

Document type: Conference object

Explorative Model and Case Study of the Province of North-Holland

Scipedia content — Tue, 02 Feb 2021 00:59:24 +0100

This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the author’s knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the impact mechanisms and the direct and indirect mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied tin a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.

Data Analysis and Model Validation of Natural Gas Transmission Pipeline With Compressor Station

Scipedia content — Tue, 02 Feb 2021 00:58:05 +0100

"jats:p"Data from the distributed control system (DCS) or supervisory control and data acquisition (SCADA) system provide useful information critical to the evaluation of the performance and transportation efficiency of a gas pipeline system with compressor stations. The pipeline performance data provide correction factors for compressors as part of the operation optimization of natural gas transmission pipelines. This paper presents methods, procedures, and an example of model validation-based performance analysis of a gas pipeline based on actual system operational data. An analysis approach based on statistical methods is demonstrated with actual DCS gas pipeline measurement data. These methods offer practical ways to validate the pipeline hydraulics model using the DCS data. The validated models are then used as performance analysis tools in assessing the pipeline hydraulics parameters that influence the pressure drop in the pipeline such as corrosion (inside diameter change), roughness changes, or basic sediment and water deposition.

Towards Executing Computer Vision Functionality on Programmable Network Devices

Scipedia content — Tue, 02 Feb 2021 00:52:24 +0100

By offering the possibility to already perform processing as packets traverse the network, programmable data planes open up new perspectives for applications suffering from strict latency and high bandwidth requirements. Real-time Computer Vision (CV), with its high data rates and often mission- and safety-critical roles in the control of autonomous vehicles and industrial machinery, could particularly benefit from executing parts of its logic within network elements. In this paper, we thus explore what it takes to bring CV to the network. We present our work-in-progress efforts of implementing a line-following algorithm based on convolution filters on a P4-programmable NIC. We find that by appropriately identifying regions of interest in the image data and by diligently distributing the necessary calculations among the various match/action stages of the ingress- and egress pipelines of the NIC, our prototypical implementation can achieve over 19 decisions per second on 640x480 px grayscale images with filters large enough to guide a small autonomous car through various courses.

Performance analysis and optimization of automotive GPUs

Scipedia content — Tue, 02 Feb 2021 00:49:09 +0100

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) have drastically increased the performance demands of automotive systems. Suitable highperformance platforms building upon Graphic Processing Units (GPUs) have been developed to respond to this demand, being NVIDIA Jetson TX2 a relevant representative. However, whether high-performance GPU configurations are appropriate for automotive setups remains as an open question. This paper aims at providing light on this question by modelling an automotive GPU (Jetson TX2), analyzing its microarchitectural parameters against relevant benchmarks, and identifying specific configurations able to meaningfully increase performance within similar cost envelopes, or to decrease costs preserving original performance levels. Overall, our analysis opens the door to the optimization of automotive GPUs for further system efficiency.

Peer Reviewed

Shared Autonomous Vehicles Effect on Vehicle-Km Traveled and Average Trip Duration

Scipedia content — Tue, 02 Feb 2021 00:42:41 +0100

Intermediate modes of transport, such as shared vehicles or ride sharing, are starting to increase their market share at the expense of traditional modes of car, public transport, and taxi. In the advent of autonomous vehicles, single occupancy shared vehicles are expected to substitute at least in part private conventional vehicle trips. The objective of this paper is to estimate the impact of shared autonomous vehicles on average trip duration and vehicle-km traveled in a large metropolitan area. A stated preference online survey was designed to gather data on the willingness to use shared autonomous vehicles. Then, commute trips and home-based other trips were generated microscopically for a synthetic population in the greater Munich metropolitan area. Individuals who traveled by auto were selected to switch from a conventional vehicle to a shared autonomous vehicle subject to their willingness to use them. The effect of shared autonomous vehicles on urban mobility was assessed through traffic simulations in MATSim with a varying autonomous taxi fleet size. The results indicated that the total traveled distance increased by up to 8% after autonomous fleets were introduced. Current travel demand can still be satisfied with an acceptable waiting time when 10 conventional vehicles are replaced with 4 shared autonomous vehicles.

Document type: Article

Are You Responsible for Traffic Congestion? A Systematic Review of the Socio-technical Perspective of Smart Mobility Services

Scipedia content — Tue, 02 Feb 2021 00:36:56 +0100

Large-scale text processing pipeline with Apache Spark

Scipedia content — Tue, 02 Feb 2021 00:32:05 +0100

In this paper, we evaluate Apache Spark for a data-intensive machine learning problem. Our use case focuses on policy diffusion detection across the state legislatures in the United States over time. Previous work on policy diffusion has been unable to make an all-pairs comparison between bills due to computational intensity. As a substitute, scholars have studied single topic areas. We provide an implementation of this analysis workflow as a distributed text processing pipeline with Spark dataframes and Scala application programming interface. We discuss the challenges and strategies of unstructured data processing, data formats for storage and efficient access, and graph processing at scale.

Task Measures for Air Traffic Display Operations

Scipedia content — Tue, 02 Feb 2021 00:23:57 +0100

With rising growth in air traffic globally, advanced technologies are being developed to aid ATCOs in the managing and control of a foreseeable denser airspace. The need to perform holding stack management, a potential challenge to ATCO, especially during heavy traffic congestion owing to weather and runway conditions is expected to be more frequent. To mitigate this challenge, the use of 3D displays was suggested. This paper examines the performance impacts resulting from the adoption of 3D instead of 2D radar displays with regards to visual search and relative vertical positioning identification. Observations relating perceived increased in stress and workload by the participants are also made.

Estimating latent energy demand of buildings

Scipedia content — Tue, 02 Feb 2021 00:22:24 +0100

Milojevic-Dupont et al. (2019). Estimating latent energy demand of buildings In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 25-26. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387711

Exploring Variation in Built Environment Predictors of Ridership by Transit Mode

Scipedia content — Tue, 02 Feb 2021 00:20:23 +0100

Trans-Sense: Real Time Transportation Schedule Estimation Using Smart Phones

Scipedia content — Tue, 02 Feb 2021 00:19:32 +0100

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.

Comment: 8 pages, 11 figures,

JUST-IN-TIME RAIL SHUTTLE SERVICE FEASIBILITY STUDY AT THE PORT OF VALENCIA

Scipedia content — Tue, 02 Feb 2021 00:15:53 +0100

The growth in container transport volumes, the increase in the size of ships and the concentration of flows through a limited number of port hubs require higher capacity on hinterland connections. Road transport now accounts for most of the connections between ports and hinterland areas in Europe, resulting in port congestion associated with delays, waiting lines and increased permanence of ships and cargo in the port. This translates into additional costs and a negative environmental impact. In most cases, the development of rail transport becomes part of the solution to this problem. The current study contributes to the development of the strategy of the Port of Valencia in order to increase the rail modal share for import/export cargo through the definition and feasibility analysis of an innovative Just-In-Time (JIT) Rail Shuttle service for a key port-hinterland corridor in Spain connecting Valencia with Zaragoza. The proposed solution aims to directly unload the containers from the ship and load them onto trains, in order to minimize the movement of containers at the terminal and to operate as an “air bridge” at the airports, so that the shuttle makes round trips within one day and the containers are loaded on the first available JIT rail service. In order to minimize the cost per unit transported the feasibility study includes designing the operational solution, service characteristics, the requirements of the information system and the definition of the business models needed for its implementation.

How to Link Accident Data and Road Traffic Measurements to Enable ADAS/AS Simulation?

Scipedia content — Tue, 02 Feb 2021 00:10:30 +0100

The progress of safety technologies, based on the continuous advances in vehicle crash worthiness, restraint systems and active safety functions made traffic safer than ever before. Latest developments heading from assisted Advanced Driver Assistance System (ADAS) to Automated Driving (AD), lead to more and more complex real-world situations to be handled, going from standard driving tasks up to critical situations, which may cause a collision. Therefore, throughout the development process of such systems, it becomes common to use simulation technologies in order to assess these systems in advance. To gain results out of the simulation, input data are required; typically, from various sources, so the requirements can be covered. Thus, the challenge of scoping with different input sources arises. To come along with that problem, two main kinds of input data will be needed in general: (1) the descriptive parameter covering all border conditions, so called parameter room; (2) the system specifications for estimation. The quality of the results correlates strongly with the quality of inputs given. In case of ADAS systems and AD functions, the second kind of input data is very well known. Major challenges relate to the first kind of input data. Thus, the paper will describe a way to create input data that cover all descriptive parameters needed from normal driving up to the collision by the combination of accident analysis and real-world road traffic observations. The method aims at being applicable to different data sources and to different countries.

An improved cell transmission model of traffic considering electric vehicles and charging stations

Scipedia content — Tue, 02 Feb 2021 00:09:26 +0100

International audience; In this paper, we extend a previously introduced traffic cell transmission model (CTM) to take electric vehicles (EVs) and charging stations (CSs) into account. The CTM is improved in the following aspects: firstly, traffic demand of multiple origins-destinations (O-D) is considered, whereas the original CTM can not simulate the specific traffic demand of each O-D pair; secondly, CSs are integrated into CTM by developing new queueing cells and charging cells, which could also describe the specific configurations of each CS and model the queueing phenomenon in CSs; third, EVs at different states of charge are considered and, thus, the spatial-temporal charging demands in CSs can be estimated with good approximation.

Learn from IoT

Scipedia content — Tue, 02 Feb 2021 00:04:29 +0100

This paper explores the potential of machine learning (ML) systems which use data from in-vehicle sensors as well as external IoT data sources to enhance autonomous driving for efficiency and safety in urban environments. We propose a system which combines sensor data from autonomous vehicles and IoT data collected from pedestrians' mobile devices. Our approach includes two methods for vulnerable road user (VRU) detection and pedestrian movement intention prediction, and a model to combine the two outputs for potentially improving the autonomous decision-making. The first method creates a world model (WM) and accurately localizes VRUs using in-vehicle cameras and external mobile device data. The second method is a deep learning model to predict pedestrian's next movement steps using real-time trajectory and training with historical mobile device data. To test the system, we conduct three pilot tests at a university campus with a custom-built autonomous car and mobile devices carried by pedestrians. The results from our controlled experiments show that VRU detection can more accurately distinguish locations of pedestrians using IoT data. Furthermore, up to five future steps of pedestrians can be predicted within 2 m.

"Ohsome" OpenStreetMap Data Evaluation: Fitness of Field Papers for Participatory Mapping

Scipedia content — Mon, 01 Feb 2021 23:53:14 +0100

Klonner et al(2019). “Ohsome” OpenStreetMap Data Evaluation: Fitness of Field Papers for Participatory Mapping In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 35-36. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387725

Robust Temporal Difference Learning for Critical Domains

Scipedia content — Mon, 01 Feb 2021 23:52:59 +0100

We present a new Q-function operator for temporal difference (TD) learning methods that explicitly encodes robustness against significant rare events (SRE) in critical domains. The operator, which we call the $\\kappa$-operator, allows to learn a robust policy in a model-based fashion without actually observing the SRE. We introduce single- and multi-agent robust TD methods using the operator $\\kappa$. We prove convergence of the operator to the optimal robust Q-function with respect to the model using the theory of Generalized Markov Decision Processes. In addition we prove convergence to the optimal Q-function of the original MDP given that the probability of SREs vanishes. Empirical evaluations demonstrate the superior performance of $\\kappa$-based TD methods both in the early learning phase as well as in the final converged stage. In addition we show robustness of the proposed method to small model errors, as well as its applicability in a multi-agent context.

Comment: AAMAS 2019

Gaussian Model Trees for Traffic Imputation

Scipedia content — Mon, 01 Feb 2021 23:51:50 +0100

Traffic congestion is one of the most pressing issues for smart cities. Information on traffic flow can be used to reduce congestion by predicting vehicle counts at unmonitored locations so that counter-measures can be applied before congestion appears. To do so pricy sensors must be distributed sparsely in the city and at important roads in the city center to collect road and vehicle information throughout the city in real-time. Then, Machine Learning models can be applied to predict vehicle counts at unmonitored locations. To be fault-tolerant and increase coverage of the traffic predictions to the suburbs, rural regions, or even neighboring villages, these Machine Learning models should not operate at a central traffic control room but rather be distributed across the city. Gaussian Processes (GP) work well in the context of traffic count prediction, but cannot capitalize on the vast amount of data available in an entire city. Furthermore, Gaussian Processes are a global and centralized model, which requires all measurements to be available at a central computation node. Product of Expert (PoE) models have been proposed as a scalable alternative to Gaussian Processes. A PoE model trains multiple, independent GPs on different subsets of the data and weight individual predictions based on each experts uncertainty. These methods work well, but they assume that experts are independent even though they may share data points. Furthermore, PoE models require exhaustive communication bandwidth between the individual experts to form the final prediction. In this paper we propose a hierarchical Product of Expert model, which consist of multiple layers of small, independent and local GP experts. We view Gaussian Process induction as regularized optimization procedure and utilize this view to derive an efficient algorithm which selects independent regions of the data. Then, we train local expert models on these regions, so that each expert is responsible for a given region. The resulting algorithm scales well for large amounts of data and outperforms flat PoE models in terms of communication cost, model size and predictive performance. Last, we discuss how to deploy these local expert models onto small devices.

Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video

Scipedia content — Mon, 01 Feb 2021 23:47:01 +0100

In urban environments there are daily issues of traffic congestion which city authorities need to address. Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques. The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos. The relevant mobility metrics are calculated to conduct traffic analysis and measure the consequences of traffic congestion. The proposed approach is validated with the manual analysis results and the visualization results. The traffic analysis process is real-time in terms of the pre-trained model used.

Large-Scale VANET Simulations and Performance Analysis using Real Taxi Trace and City Map Data

Scipedia content — Mon, 01 Feb 2021 23:42:46 +0100

Wireless vehicular ad-hoc networks comprised solely of city taxis are investigated for their ability to deliver data across an urban environment. Openly available taxi trace datasets for Rome (Italy) and San Francisco (USA) are combined with respective building footprint and road network topology data from OpenStreetMap, to generate a realistic systems level model of a taxi V2V network. Analysis of LOS and NOLOS constraints on wireless transmission range suggests a minimum threshold of 50m is applicable to ensure LOS in over 90% of cases. Variations in taxi location sampling frequency and filtering techniques for the taxi trace datasets are also investigated. Overall vehicular network performance is computed for an all-to-one transmission scenario for both cities with varying taxi fleet size. Results suggest a non-linear relationship between increases in taxi fleet sizes and the reduction of end-to-end delay; doubling taxi fleet size (using a randomised data folding technique) reduces end-to-end delay by a factor of 0.6–0.7. However, doubling the fleet does not increase the fraction of delivered source messages, which saturates at 0.67–0.71 in most simulations. Finally it appears that taxi networks for delivering messages across urban environments are limited more by their routing than by the number of possible V2V exchanges. In a simulated one-to-all continuous V2V broadcast scenario, over 90% of the taxis within the fleet receive the source message within one hour of the original taxi passing the source node.

Diagnostics of car wheel bearings with the use of noise-acoustic control methods

Scipedia content — Mon, 01 Feb 2021 23:38:55 +0100

This paper presents a method of noise-acoustic non-destructive control during carrying out of diagnostics of bearings of mats rolling in car wheels. The proposed non-destructive method of control provides an opportunity to check the efficiency of the selected lubricant, thereby increasing the life and performance of the bearings. A laboratory installation for the diagnosis of roller bearings has been created, which allows to obtain their acoustic parameters depending on the load of the bearing unit, the time of application, and application of different types of lubricants in bearings. The theoretical and practical contribution of this proposition is that the mathematical model developed by the authors is aimed at determining the degree of wear of bearing shafts, which allows to predict their possible work life based on the received noise-acoustic parameters.

A safety oriented decision support tool for the remanufacturing and recycling of post-use H&EVs Lithium-Ion batteries

Scipedia content — Mon, 01 Feb 2021 23:28:36 +0100

The battery is a key component of electric vehicles. To reach the needed voltage and capacity, single Lithium-Ion cells are assembled into modules, then assembled into the pack. Their disassembly, which unlocks both remanufacturing or recycling and which nowadays is made mainly manually, has high electric hazards. Decision tools have not yet been developed to minimize these risks. This work presents a mathematical model to determine the disassembly sequence with the minimal exposure of the operator to hazardous voltages. The model considers the mechanical and electrical architecture of the battery and the tasks needed to reach the desired disassembly level.

APPLICATION OF SPLINES UNDER TENSION FOR SIMULATION OF THE STRESSED-DEFORMED CONDITION OF THE LINEAR PART OF THE MAIN PIPELINES

Scipedia content — Mon, 01 Feb 2021 23:25:41 +0100

Coalition Game for Emergency Vehicles Re-Routing in Smart Cities

Scipedia content — Mon, 01 Feb 2021 23:22:26 +0100

Traffic jam is considered as a difficult problem to deal with in many cities around the world due to the continuously increasing number of vehicles compared to the available infrastructure. Traffic congestion significantly influences drivers travel journey, fuel consumption and air pollution. However, the most important factor has affected the delay of emergency vehicles, such as ambulances and police cars, leading to increased road deaths and significant financial losses. To reduce this problem, we propose an advanced traffic control allows rapid emergency services response in smart cities. This can be achieved through a traffic management system capable of implementing path planning in road network monitoring and driving the emergency vehicle in the best possible way to reach the hazard zone. The performance of the proposed algorithm is compared with two other algorithms over Birmingham city centre test scenarios. Simulation results show that the proposed approach improves traffic efficiency of emergency vehicles by an overall average of 21.78%, 29.32%, 32.79% and 46.77% in terms of travel time, fuel consumption, CO 2 emission and average speed, respectively.

Green ITS: towards electric mobility

Scipedia content — Mon, 01 Feb 2021 23:20:22 +0100

The concept of sustainability and environmental impacts of the transportation system must be standardized for traffic participants, roads and infrastructure from planning to operation. With consideration of the road transport accounting for 75% of the world's total carbon dioxide emissions from fossil fuel combustion. There is a continuous growth of the world population around 1.2% per year, concentrating on large urban nuclei. In this context tries answer this next question. What should we conduct to balance the energy saving and the demand of mobility?. And the same time reduces CO2 emission. It is necessary to give a good management, intelligent, sustainable and with the possibility of acting in real time that reduces CO2 emissions. The green intelligent transport system is the way to give solutions. Focusing on transport by road, the proposal is based on increasing the use of transport that uses renewable fuels. New energy vehicle technology and applications are important topic in the green ITS. The fundamental objective is to promote the use of electric vehicles. Electric vehicles will be part of a sustainable mobility strategy in the future. The limitations of low autonomy and slow recharge time have already been overcome. Electric vehicles due to their connectivity, design and configuration are easier to integrate ITS applications. Currently, the only vehicles whose emission of CO2 is 0% during movement are electric vehicles and hydrogen vehicles. Finally it is important to consider that the energy that recharges the vehicles comes from a renewable source.

How knowing the purpose of mapping changes the map and the mappers themselves

Scipedia content — Mon, 01 Feb 2021 23:13:40 +0100

Solís (2019). How knowing the purpose of mapping changes the map and the mappers themselves In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 39-40. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387729

The RPL Load Balancing in IoT Network with Burst Traffic Scenarios

Scipedia content — Mon, 01 Feb 2021 23:09:36 +0100

In Low Power and Lossy Networks (LLNs) sensor nodes are deployed in various traffic load conditions such as, regular and heavy traffic load. The adoption of Internet-of-Things enabled devices in the form of wearables and ubiquitous sensors and actuators has demanded LLNs to handle burst traffic load, which is an event required by myriad IoT devices in a shared LLN. In the large events, burst traffic load requires a new radical approach of load balancing, this scenario causes congestion increases and packet drops relatively when frequent traffic burst load rises in comparison with regular and heavy loads. In this paper, we introduced a new efficient load balance mechanism for traffic congestion in IPv6 Routing Protocol for Low Power and Lossy Network (RPL). To measure the communication quality and optimize the lifetime of the network, we have chosen packet delivery ratio (PDR) and power consumption (PC) as our metrics. We proposed a traffic-aware metric that utilizes ETX and parent count metrics (ETXPC), where communication quality for LLNs with RPL routing protocol are playing an important role in traffic engineering. In addition, we provided analytical results to quantify the impact of Minimum Rank with Hysteresis Objective on Function (MRHOF) and Objective Function zero (OF0) to the packet delivery, reliability and power consumption in LLNs. The simulation results pragmatically show that the proposed load balancing approach has increased packet delivery ratio with less power consumption.

A QoS-Based Flow Assignment for Traffic Engineering in Software-Defined Networks

Scipedia content — Mon, 01 Feb 2021 23:05:51 +0100

In order to meet a tremendous amount of data storage requirement in next-generation wireless networks, an increasing number of cloud data centers has been deployed around the world. The underlying core networks are expected to provide the ability to store data in a dynamic and scalable computing environment. The traditional Internet Protocol (IP) has shown to be restricted due to its static architecture, which accordingly motivates the development of Software-Defined Networks (SDNs). In the SDNs, Traffic Engineering (TE) is simpler and programmable with a controller without the requirement of reconfiguration for all network devices. However, the existing TE algorithm of the SDNs rejects a number of requested flows caused by their undetermined routing paths where only flow bandwidth is considered in path determination. This paper proposes a Quality-of-Service (QoS) based Flow Assignment algorithm which enables the computation of end-to-end path for traffic flows guaranteeing the QoS requirements including bandwidth, end-to-end delay and packet loss probability. Through the Open Source Hybrid IP/SDNs platform, the proposed algorithm is validated and shown to significantly reduce flow rejection rate of up to 50% compared to the conventional approach, and therefore can be used to implement an effective DiffServ mechanism for flow allocation in the SDNs.

Algorithmic climate change functions for the use in eco-efficient flight planning

Scipedia content — Mon, 01 Feb 2021 23:02:18 +0100

Aviation contributes significantly to anthropogenic climate change, and one promising possibility for mitigation is eco-efficient flight planning by avoiding climate sensitive regions with only small changes in the aircraft trajectories. Climate sensitive regions result from strong spatial variation of the global climate impact of local non-CO2 emissions, which are expressed by so-called climate change functions. Previous research established high-fidelity climate change functions (CCFs) for aviation water vapour and NOx emissions, and contrail formation with a climate model as inputs for air traffic optimisation. The mitigation potential in this case study is promising but the climate change function simulations are too computationally intensive for real-time calculation and thus cannot be applied operationally. In this study we show for the first time that this problem can be overcome by formulating algorithmic approximations of the global climate impact. Here we approximate water vapour concentration changes from local aviation water vapour emissions, ozone changes from local NOx emissions and methane changes from local NOx emissions (i.e. algorithmic climate change functions; aCCFs) from instantaneous model weather data using regression analysis. Four candidate algorithms are formulated per chemical species and traded off. The final adjusted regression coefficients, indicating how well the aCCFs represent the CCFs, are 0.59, 0.42, and 0.17 for water vapour, ozone and methane. The results show that the meteorology at the time of emission largely controls the fate of the emitted species, where the quality of the aCCF degrades with increasing lifetime of the respective species.

Autonomous Unmanned Ground Vehicles for Urban Logistics: Optimization of Last Mile Delivery Operations

Scipedia content — Mon, 01 Feb 2021 22:59:32 +0100

DQMA-Fuzzy: Predição de Falhas em Redes de Grades OBS com Plano de Controle GMPLS

Scipedia content — Mon, 01 Feb 2021 22:59:03 +0100

This paper presents a proposal for predict failures in OBS grid network to assist applications in collaborative environments, like E-Science. Agents (DQMA-Fuzzy) monitoring traffic for QoS parameters related and others related to imperfections in optical links. By presenting a solution faster and easily implementable, a system based on fuzzy logic has been developed to give more robustness and flexibility in decisionmaking agents. NS-2 (Network Simulator – 2) simulations show that the proposed Fuzzy-DQMA is able to minimize blockages and balancing the use of grid resources, ensuring well-defined service levels, assisting in traffic engineering and fault prediction. Keywords— optical network, OBS, GMPS, Fuzzy Logic.

Identification of Test Cases for Automated Driving Systems Using Bayesian Optimization

Scipedia content — Mon, 01 Feb 2021 22:52:01 +0100

With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomous driving systems are highly safety critical in nature and need to be thoroughly tested for a diverse set of conditions before being commercially deployed. Due to the huge complexities involved with Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traditional software testing methods have well-known limitations. They also fail to cover the infinite number of adverse conditions that can occur due to a slight change in the interactions between the environment and the system. Hence, it is important to identify test conditions that push the vehicle under test to breach its safe boundaries. Hazard Based Testing (HBT) methods, inspired by Systems-Theoretic Process Analysis (STPA), identify such parameterized test conditions that can lead to system failure. However, these techniques fall short of discovering the exact parameter values that lead to the failure condition. The presented paper proposes a test case identification technique using Bayesian Optimization. For a given test scenario, the proposed method learns parameter values by observing the system's output. The identified values create test cases that drive the system to violate its safe boundaries. STPA inspired outputs (parameters and pass/fail criteria) are used as inputs to the Bayesian Optimization model. The proposed method was applied to an SAE Level-4 Low Speed Automated Driving (LSAD) system which was modelled in a driving simulator.

Defect Detection using Power Spectrum of Torsional Waves in Guided-Wave Inspection of Pipelines

Scipedia content — Mon, 01 Feb 2021 22:46:58 +0100

Ultrasonic Guided-wave (UGW) testing of pipelines allows long-range assessment of pipe integrity from a single point of inspection. This technology uses a number of arrays of transducers separated by a distance from each other to generate a single axisymmetric (torsional) wave mode. The location of anomalies in the pipe is determined by inspectors using the received signal. Guided-waves are multimodal and dispersive. In practical tests, nonaxisymmetric waves are also received due to the nonideal testing conditions, such as presence of variable transfer function of transducers. These waves are considered as the main source of noise in the guided-wave inspection of pipelines. In this paper, we propose a method to exploit the differences in the power spectrum of the torsional wave and flexural waves, in order to detect the torsional wave, leading to the defect location. The method is based on a sliding moving window, where in each iteration the signals are normalised and their power spectra are calculated. Each power spectrum is compared with the previously known spectrum of excitation sequence. Five binary conditions are defined; all of these need to be met in order for a window to be marked as defect signal. This method is validated using a synthesised test case generated by a Finite Element Model (FEM) as well as real test data gathered from laboratory trials. In laboratory trials, three different pipes with defects sizes of 4%, 3% and 2% cross-sectional area (CSA) material loss were evaluated. In order to find the optimum frequency, the varying excitation frequency of 30 to 50 kHz (in steps of 2 kHz) were used. The results demonstrate the capability of this algorithm in detecting torsional waves with low signal-to-noise ratio (SNR) without requiring any change in the excitation sequence. This can help inspectors by validating the frequency response of the received sequence and give more confidence in the detection of defects in guided-wave testing of pipelines.

Exploring Flexible Communications for Streamlining DNN Ensemble Training Pipelines

Scipedia content — Mon, 01 Feb 2021 22:40:53 +0100

Parallel training of a Deep Neural Network (DNN) ensemble on a cluster of nodes is a common practice to train multiple models in order to construct a model with a higher prediction accuracy, or to quickly tune the parameters of a training model. Existing ensemble training pipelines perform a great deal of redundant operations, resulting in unnecessary CPU usage, or even poor pipeline performance. In order to remove these redundancies, we need pipelines with more communication flexibility than existing DNN frameworks can provide. This project investigates a series of designs to improve pipeline flexibility and adaptivity, while also increasing performance. We implement our designs using Tensorflow with Horovod, and test it using several large DNNs in a large scale GPU cluster, the Titan supercomputer at Oak Ridge National Lab. Our results show that with the new flexible communication schemes, the CPU time spent during training is reduced by 2-11X. Furthermore, our implementation can achieve up to 10X speedups when CPU core limits are imposed. Our best pipeline also reduces the average power draw of the ensemble training process by 5--16% when compared to the baseline.

An Empirical Study on V2X Enhanced Low-Cost GNSS Cooperative Positioning in Urban Environments

Scipedia content — Mon, 01 Feb 2021 22:37:10 +0100

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm&rsquo

s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments.

Document type: Article

Flexible and efficient model-based congestion detection approach

Scipedia content — Mon, 01 Feb 2021 22:36:04 +0100

This paper addresses the problem of road traffic congestion detection. We propose an effective approach to detect traffic congestion by combining the piecewise switched linear traffic (PWSL) and Shewhart control scheme. This approach uses PWSL model to describe the evolution of traffic density, and Shewhart chart to detect traffic congestions based on residuals obtained from PWSL model. The PWSL-Shewhart approach is evaluated using traffic data from the four-lane State Route 60 (SR-60) freeway in California. Results indicate that our approach accomplished reliable detection of traffic congestion.

Sensitivity analysis of optimal routes, departure times and speeds for fuel-efficient truck journeys

Scipedia content — Mon, 01 Feb 2021 22:29:15 +0100

Embedded within the vehicle "routing" problem of determining the order in which customers are served, is the route choice problem of which sequence of roads to use between a pair of pick-up/drop-off locations, and this latter is the focus of the paper. When the objective is something other than travel time, such as fuel consumption, an additional control dimension is that of speed, and in a time-varying context the question of optimal speed determination is no longer a local one, due to potential downstream interactions. This also brings in the possibility to adjust departure times. Recently this problem, of joint route, departure time and speed determination for fuel minimization in a time-varying network, was shown to be efficiently solvable using a Space-Time Extended Network (STEN). In the present paper, we explore the sensitivity of the optimal solutions produced to: i) the fidelity of the within-day traffic information; ii) the currency of between-day traffic information in comparison with historical mean conditions; iii) the availability of historical information on variability for risk-averse routing; and iv) competition from other equally-optimal or near equally-optimal solutions. We set out the methods by which each of these tests may be achieved by adaptation of the underlying STEN, taking care to ensure a consistent reference basis, and describe the potential real-life relevance of each test. The results of illustrative numerical experiments are reported from interfacing the methods with real-time data accessed through the Google Maps API.

MARITIME DATA INTEGRATION AND ANALYSIS: RECENT PROGRESS AND RESEARCH CHALLENGES

Scipedia content — Mon, 01 Feb 2021 22:18:24 +0100

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

Contextualizing OpenStreetMap in Mapping Favelas in Brazil

Scipedia content — Mon, 01 Feb 2021 22:17:54 +0100

Bortolini and Camboim (2019). Contextualizing OpenStreetMap in Mapping Favelas in Brazil In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 37-38. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387727

Road Infrastructure Requirements for Improved Performance of Lane Assistance Systems

Scipedia content — Mon, 01 Feb 2021 22:17:41 +0100

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the road infrastructure requirements that would lead to safe operation of automated vehicles. In this context, a field test with Lane Departure Warning and Lane Keeping Systems-enabled vehicles was conducted in the province of North Holland, The Netherlands. The performance of these automated systems was evaluated using performance indicators such as Mean Lateral Position and Standard Deviation of Lane Position. In this study, the Systems Theoretic Accident Modelling and Processes (STAMP) model was adopted to understand the relationships between the various components of the “Road System”, which in this study include the road authority, the automated vehicle system, elements of the road infrastructure, and weather conditions. Empirical data from the experiment is used to estimate the relationships between the different components, followed by the assessment of their impact on the performance of the automated vehicles. It was found that visibility conditions have a significant effect on detection performance, which worsens in rainy conditions especially under streetlights. It has been also observed that there is a significant difference in Lane Position between Left Curves and Straight sections, and between lane widths less than 250 cms and those that have larger widths. These findings are combined with the results from the STAMP analysis to formulate a set of road infrastructure requirements that would lead to safe performance of Lane Assistance Systems.

Estratégias de Sondagem para Remapeamento Eficiente de Eventos de Roteamento na Internet

Scipedia content — Mon, 01 Feb 2021 22:06:27 +0100

Mudanças de caminho causadas por eventos como engenharia de tráfego, alteração de parcerias de troca de tráfego, ou falhas de enlace impactam vários caminhos na Internet. Plataformas de monitoramento topológico realizam medições periódicas usando traceroute para um grande número de destinos. Esta abordagem, porém, é inadequada para identificar precisamente a extensão do impacto de eventos de roteamento. Por exemplo, uma falha de enlace pode ser restaurada antes que todas as rotas sejam medidas. Neste trabalho apresentamos estratégias de medição que minimizam o custo de sondagem para identificar caminhos impactados por um evento de roteamento. Nossos resultados mostram que é possível identificar o conjunto de caminhos impactados por um evento de forma eficiente. Nossos resultados indicam ainda que, quando integradas a um sistema estado-da-arte de rastreamento de mudanças de caminhos, nossas estratégias mais que dobram o número de mudanças detectadas.

Dissecting global air traffic data to discern different types and trends of transnational human mobility

Scipedia content — Mon, 01 Feb 2021 22:04:09 +0100

Human mobility across national borders is a key phenomenon of our time. At the global scale, however, we still know relatively little about the structure and nature of such transnational movements. This study uses a large dataset on monthly air passenger traffic between 239 countries worldwide from 2010 to 2018 to gain new insights into (a) mobility trends over time and (b) types of mobility. A time series decomposition is used to extract a trend and a seasonal component. The trend component permits—at a higher level of granularity than previous sources—to examine the development of mobility between countries and to test how it is affected by policy and infrastructural changes, economic developments, and violent conflict. The seasonal component allows, by measuring the lag between initial and return motion, to discern different types of mobility, from tourism to seasonal work migration. Moreover, the exact shape of seasonal mobility patterns is extracted, allowing to identify regular mobility peaks and nadirs throughout the year. The result is a unique classification of trends and types of mobility for a global set of country pairs. A range of implications and possible applications are discussed. Open-Access-Publikationsfonds 2019

Document type: Article

Designing a fusion of visible and infra-red camera streams for remote tower operations

Scipedia content — Mon, 01 Feb 2021 21:54:08 +0100

The research project INVIDEON evaluated requirements, technical solutions and the benefit of fusing visible (VIS) and infra-red (IR) spectrum camera streams into a single panorama video stream. In this paper, the design process for developing a usable and accepted fusion is described. As both sensors have strengthens and weaknesses, INVIDEON proposes a fused panorama optimized out of both sensors to be presented to the ATC officer (ATCO). This paper gives an overview of the project and reports results of acceptance and usability of the INVIDEON solution. The process of supporting the definition of requirements by means of rapid prototyping and taking a user-centered approach is described. Main findings of requirements for fusing VIS and IR camera data for remote tower operations are highlighted and set into context with the air traffic controller's tasks. A specific fusion approach was developed within the project and evaluated by means of recorded IR and VIS data. For evaluation, a testbed was set up at a regional airport and data representing different visibility conditions were selected out of 70 days data recordings. Five air traffic controllers participated in the final evaluation. Subjective data on perceived usability, situational awareness and trust in automation was assessed. Furthermore, qualitative data on HMI design and optimization potential from debriefings and comments was collected and clustered.

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Scipedia content — Mon, 01 Feb 2021 21:49:17 +0100

Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk or uncertainty. In this paper, we propose a dynamic pricing strategy for Shared Mobility on Demand Services (SMoDSs) using a passenger behavioral model based on CPT. This dynamic pricing strategy together with dynamic routing via a constrained optimization algorithm that we have developed earlier, provide a complete solution customized for SMoDS of multi-passenger transportation. The basic principles of CPT and the derivation of the passenger behavioral model in the SMoDS context are described in detail. The implications of CPT on dynamic pricing of the SMoDS are delineated using computational experiments involving passenger preferences. These implications include interpretation of the classic fourfold pattern of risk attitudes, strong risk aversion over mixed prospects, and behavioral preferences of self reference. Overall, it is argued that the use of the CPT framework corresponds to a crucial building block in designing socio-technical systems by allowing quantification of subjective decision making under risk or uncertainty that is perceived to be otherwise qualitative.

Comment: 17 pages, 6 figures, and has been accepted for publication at the 58th Annual Conference on Decision and Control, 2019

Options to Continue the EU ETS for Aviation in a CORSIA-World

Scipedia content — Mon, 01 Feb 2021 21:48:56 +0100

From 2021, an increasing percentage of the carbon emission growth in international air transport will be subject to offsetting under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Presently, it is still unclear if, and how, the existing EU emissions trading scheme (ETS) for aviation will continue. We assess the environmental impacts of different options (not) to continue with the EU ETS for aviation alongside CORSIA, and also discuss resulting monitoring, reporting, and verification (MRV) requirements. Our results indicate that any form of continuation of the EU ETS would have positive environmental effects especially in the early 2020s, when the coverage and environmental impact of CORSIA, which only tackles any post-2020 emission growth in international aviation, will still be low. If, moreover, a certain failure of CORSIA Certified Emission Reductions (CERs) to actually achieve emission reduction elsewhere is assumed, the environmental net benefit of CORSIA will be even lower. From both the policy and economic perspectives, these aspects may further strengthen the need to continue with the EU ETS for aviation. Possible options are to maintain the EU ETS in operation for domestic flights only, as a complement to CORSIA, or to keep it alive even for international flights within the European Economic Area (EEA), replacing CORSIA there as an equivalent measure. Another option to increase the environmental effectiveness of CORSIA, at least to some extent, could be to voluntarily extend it to domestic EEA flights. Administrative-wise, the CORSIA MRV system could be applied to a continued EU ETS to reduce transaction costs and to assure globally similar or even identical MRV standards, e.g., with regard to exemptions and eligible fuel monitoring methods.

Document type: Article

Simulation assessment of the selected combination of road and rail infrastructure in the aspect of choosing the route of road transport means

Scipedia content — Mon, 01 Feb 2021 21:44:56 +0100

Innovative development of domestic transport logistics

Scipedia content — Mon, 01 Feb 2021 21:43:22 +0100

Fault Injection on Hidden Registers in a RISC-V Rocket Processor and Software Countermeasures

Scipedia content — Mon, 01 Feb 2021 21:42:07 +0100

To protect against hardware fault attacks, developers can use software countermeasures. They are generally designed to thwart software fault models such as instruction skip or memory corruption. However, these typical models do not take into account the actual implementation of a processor. By analyzing the processor microarchitecture, it is possible to bypass typical software countermeasures. In this paper, we analyze the vulnerability of a secure code from FISSC (Fault Injection and Simulation Secure Collection), by simulating fault injections in a RISC-V Rocket processor RTL description. We highlight the importance of hidden registers in the processor pipeline, which temporarily hold data during code execution. Secret data can be leaked by attacking these hidden registers. Software countermeasures against such attacks are also proposed.

Coordinated Sequencing of Traffic on Multiple En-route Constraint Points

Scipedia content — Mon, 01 Feb 2021 21:39:03 +0100

International audience; Air transportation traffic is progressively increasing over the years and dealing with it is an essential task to guarantee fluid flights in the future. Several works already indexed multiple aspects of aviation, among them, the E-MAN system. It introduced the sequencing of arriving traffic, starting from early stages of the En-route phase. This change facilitated the work for the approach controllers but also increased the workload of the En-route controllers. To handle that workload, controllers are now assisted by tools that consider the new constraints introduced by the arrival management system and propose advisories. From that same perspective, our project focuses on an algorithm for a helper tool that will combine both aspects of traffic sequencing in the En-route phase and conflict resolution. With this novel approach, we automatically generate near-to-optimal flight decisions, given that we can modify the speed and the flight level to respect the sequencing constraints and cut down potential conflicts. We categorize the problem as a mathematical optimization case. Thus, we describe a detailed mathematical model which covers all the aspects of the problem. This model gives a basis for the implementation of the flight optimizer. Later, we propose a solution based on a sliding window simulated annealing algorithm which reduces the complexity and takes into account uncertainties. Finally, we successfully test an implementation of the solution with real-life traffic data. It corresponds to flights within France going towards Paris CDG airport over a period of 24 hours. The results demonstrate a total conflicts resolution with satisfying compliance with sequencing constraints.

Investigation of tram movement indicators in general structure of traffic flow

Scipedia content — Mon, 01 Feb 2021 21:38:45 +0100

In the work, the average operating speed of the tram is investigated on the sections with the high density of the road network. Such peculiarities are inherent to the cities where its configuration has developed historically, and trams move in the general structure of traffic flow which is predetermined by the absence of traffic capacity reserves in the old, as a rule, central part of the city. It frequently causes the reduction of the whole traffic flow speed of movement, in particular on the intersections and within public transport stops. Determination of the mutual impact of automobile movement and trams is topical because, on the one hand, trams, taking into account their dynamic and technological movement peculiarities, worsen traffic flow indicators, and on the other hand, vast traffic intensity causes downtime of the trams rolling stock in the queues before the intersection that decrease passenger transportation quality. As a result of the research reported in this paper it was managed to determine the amount of change of the average tram operating speed for different methods of traffic flow control for different times of day.

Optimising and future-proofing the design of major urban routes for all street users

Scipedia content — Mon, 01 Feb 2021 21:21:20 +0100

CAN MACHINE LEARNING IMPROVE THE ACCURACY OF WATER LEVEL FORECASTS FOR INLAND NAVIGATION? CASE STUDY: RHINE RIVER BASIN, GERMANY

Scipedia content — Mon, 01 Feb 2021 21:20:44 +0100

Exploring the Effects of Pokémon Go Vandalism on OpenStreetMap

Scipedia content — Mon, 01 Feb 2021 21:18:26 +0100

Juhász et al (2019). Exploring the Effects of Pokémon Go Vandalism on OpenStreetMap In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 3-4. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3386533

Statistical Characterization of Round-Trip Times with Nonparametric Hidden Markov Models

Scipedia content — Mon, 01 Feb 2021 21:04:01 +0100

International audience; The study of round-trip time (RTT) measurements on the Internet is of particular importance for improving real-time applications, enforcing QoS with traffic engineering, or detecting unexpected network conditions. On large timescales, from 1 hour to several days, RTT measurements exhibit characteristic patterns due to inter and intra-AS routing changes and traffic engineering, in addition to link congestion. We propose the use of a nonparametric Bayesian model, the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), to characterize RTT timeseries. The parameters of the HMM, including the number of states, as well as the values of hidden states are estimated from delay observations by Gibbs sampling. No assumptions are made on the number of states, and a nonparametric mixture model is used to represent a wide range of delay distribution in each state for more flexibility. We validate the model through three applications: on RIPE Atlas measurements we show that 80% of the states learned on RTTs match only one AS path; on a labelled delay changepoint dataset we show that the model is competitive with state-of-the-art changepoint detection methods in terms of precision and recall; and we show that the predictive ability of the model allows us to reduce the monitoring cost by 90% in routing overlays using Markov decision processes.

Power and area efficient clock stretching and critical path reshaping for error resilience

Scipedia content — Mon, 01 Feb 2021 21:03:15 +0100

Process, voltage and temperature variations are on the rise with technology scaling. Nano-scale technology requires huge design margins to ensure reliable operation. Worst case design margining consumes significant amount of circuits and systems resources. In-situ error detection or correction is an alternative method for cost effective variation tolerance. However, existing in-situ error detection and correction circuits are power and area hungry since they use speculative error management, which gives less power savings at higher error rates. This paper proposes an error resilience technique utilizing available slack in the design. The proposed method uses a clock stretching circuit to relax timing margins on selected critical paths that has sufficient consecutive stage slack. We also propose a power optimization method which reshapes the critical path logic proportionate to the consecutive stage slack. Experimental results show that the proposed method achieves the power and area savings of 40% and 8% respectively compared to the worst case design approach. When compared to the TIMBER error resilience approach, the proposed method saves power more than 74% and area more than 13% at design time.

Document type: Article

Early Identification of Recurrent Congestion in Heterogeneous Urban Traffic

Scipedia content — Mon, 01 Feb 2021 20:57:55 +0100

Urban traffic congestion has become a critical issue that not only affects the quality of daily lives but also harms the environment and economy. traffic patterns are recurrent in nature, so is congestion. However, little attention has been paid to the development of methods that would enable early warning of the formation of congestion and its propagation. This paper proposes a method for automated early congestion detection operating over time horizons ranging from half an hour to three hours. The method uses a deep learning technique, Convolutional Neural Networks (CNN), and adapts it to the specific context of urban roads. Empirical results are reported from a busy traffic corridor in the city of Bath. Comprehensive evaluation metrics, including Detection Rate, False Positive Rate and Mean Time to Detection, are used to evaluate the performance of the proposed method compared to more conventional machine learning methods including Feed-forward Neural Network and Random Forest. The results indicate that recurrent congestion can indeed be predicted before it occurs and demonstrates that CNN based method offers superior detection accuracy compared to the conventional machine learning methods in this context.

On the Evaluation of a Cluster-based Reputation Assessment Mechanism for Carpooling Applications

Scipedia content — Mon, 01 Feb 2021 20:53:51 +0100

Carpooling is a mobility concept that appears to be the answer when it comes to challenges in urban mobility derived by population growth. In carpooling, the same amount of people move with fewer vehicles leading to reduced traffic congestion and consequently to less CO2 emissions, fuel consumption and drivers frustration. However, there has always been scepticism around carpooling due to the inherent mistrust between drivers and passengers. In recent years, some reputation systems have been proposed to reduce the impact of mistrust on carpooling applications. Among them, the work of Salamanis et al. (Salamanis, 2018), in which a reputation assessment mechanism based on clustering users travel preferences, was introduced. In this paper, we provide an extended version of the previous mechanism and we thoroughly evaluate its robustness in relation with different types of malicious attacks and clustering algorithms. In addition, we compare our mechanism with a benchmarking reputation system that utilizes the simple arithmetic mean to calculate reputation values based on users ratings. The evaluation results indicate that the extended reputation assessment mechanism exhibits more robust behavior compared to the benchmarking system in all types of attacks when using the hierarchical clustering algorithm.

Online rheology monitoring of a thickener underflow

Scipedia content — Mon, 01 Feb 2021 20:51:25 +0100

The perceived need for accurate and reliable methods of measuring suspension rheology in real time arises from the greater demands being placed on mineral processing operations. To extend mine life and reduce TSF footprint the adoption of finer grinds, higher solids concentration and high clay ores result in complex multiphase suspensions that need close monitoring to optimise thickener performance, pipeline transport and tailings deposition. Often the control of the processing or transport of these suspensions can be related to its rheology. However, due to the involved nature of rheological measurement for suspensions and the nuanced interpretation of data necessary to produce useful decisions, rheometry has only seen limited application in process monitoring. A robust unit that can measure, analyse and interpret the rheology of a process stream continuously and unattended is needed. The CSIRO has developed an online rheometer to address this problem. This paper describes the process prior to the deployment of the online rheometer to an Australian goldfield site, comparing online rheological measurement to benchmark laboratory values.

STRATEGIC IMPORTANCE OF AIRPORT SLOTS IN AVIATION: SECONDARY SLOT MARKET AT LONDON HEATHROW AIRPORT

Scipedia content — Mon, 01 Feb 2021 20:50:49 +0100

orts are essential parts of the air transport system. As the gateways to aviation, they play a key role in facilitating tourism, travel, global trade and regional welfare. In 2016, airlines worldwide carried 3.8 billion people with an increase of 7% according to previous year. IATA?s 20-year forecast states that air passenger numbers will reach to seven billion annually by 2034. The demand growth for air transport services is much higher than the growth of airport infrastructure. Shortage in airport capacity is one of the most important pressing issues affecting world air mobility today.If capacity is less than demand, the demand needs to be managed. To use an airport at a specific time, an airline must have a slot. A Slot is most commonly known as landing or take-off right at airports during a specified period of time. The distribution of slots is carried by an independent ?Slot co-ordinator?. London Heathrow Airport is the most heavily slot restricted airport in the world, and the slots are very valuable. At Heathrow, the slots in the slot pool is very limited. In 2016 only 3 pair of new slots given to airlines by the slot coordinator. Because of the unavailability and lack of new slots, Heathrow has a premium secondary slot market, that makes it unique in the world. In 1999, the UK High Court gave a historic judgement about the question of buying and selling of slots and approved a slot deal between British Airways and KLM. By this approval, airlines are allowed to pay money to other airlines for slot transactions at UK airports. Although slot trading is still uncommon and technically illegal at EU airports, slots traded freely at Heathrow airport. In 2016, a gulf carrier, Oman Air purchased a prime slot pair from KLM for 75 million USD. The previous record was 60 million USD for a slot pair American Airlines bought from SAS a year before.Airport slots are not always used by airlines that attach the highest value to them on behalf of airport side. The efficient use of airport capacity means uses of larger aircraft, longer average flight lengths, and higher passenger numbers for allocated slots. If there is an inadequate use of a slot by a short-haul and less passenger flight, buying of this slot by another airline for a long-haul flight can create additional value and efficiency to that slot time.

Fast Session Resumption in DTLS for Mobile Communications

Scipedia content — Mon, 01 Feb 2021 20:47:56 +0100

DTLS is a protocol that provides security guarantees to Internet communications. It can operate on top of both TCP and UDP transport protocols. Thus, it is particularly suited for peer-to-peer and distributed multimedia applications. The same holds if the endpoints are mobile devices. In this scenario, mechanisms are needed to surmount possible network disconnections, often arising due to the mobility or the scarce resources of devices, that can jeopardize the quality of the communications. Session resumption is thus a main issue to deal with. To this aim, we propose a fast reconnection scheme that employs non-connected sockets to quickly resume DTLS communication sessions. The proposed scheme is assessed in a performance evaluation that confirms its viability.

Comment: Proceedings of the IEEE Consumer Communications and Networking Conference 2020 (CCNC 2020)

Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language

Scipedia content — Mon, 01 Feb 2021 20:46:56 +0100

End-to-end speech synthesis is a promising approach that directly converts raw text to speech. Although it was shown that Tacotron2 outperforms classical pipeline systems with regards to naturalness in English, its applicability to other languages is still unknown. Japanese could be one of the most difficult languages for which to achieve end-to-end speech synthesis, largely due to its character diversity and pitch accents. Therefore, state-of-the-art systems are still based on a traditional pipeline framework that requires a separate text analyzer and duration model. Towards end-to-end Japanese speech synthesis, we extend Tacotron to systems with self-attention to capture long-term dependencies related to pitch accents and compare their audio quality with classical pipeline systems under various conditions to show their pros and cons. In a large-scale listening test, we investigated the impacts of the presence of accentual-type labels, the use of force or predicted alignments, and acoustic features used as local condition parameters of the Wavenet vocoder. Our results reveal that although the proposed systems still do not match the quality of a top-line pipeline system for Japanese, we show important stepping stones towards end-to-end Japanese speech synthesis.

Comment: to be appeared at ICASSP 2019

System of urban unmanned passenger vehicle transport

Scipedia content — Mon, 01 Feb 2021 20:45:52 +0100

This article describes the system of unmanned urban passenger transport based on mobile autonomous robotic vehicles. The article describes an algorithm for drawing up a non-conflicting plan of passenger transportation on demand. The relevance of this system is conditioned changing social and economic conditions in the large cities, as well as modern features of scientific and technical progress. This paper presents a study aimed at improving the quality and efficiency of passenger transport in the "loaded" urban transport environment. It describes a mathematical model of computer control unmanned transport system. As a result of the study an algorithm for organization passenger transportations using unmanned vehicles has been proposed. The proposed model is adaptive to changes of road conditions and is intended to enhance the mobility and flexibility of passenger transport in the context of high traffic flows. The value of the study is that it brings economic and environmental benefits, since the method of transporting passengers by unmanned vehicles provides a high throughput of urban transport systems with a high level of passenger comfort.

Specialization Opportunities in Graphical Workloads

Scipedia content — Mon, 01 Feb 2021 20:40:10 +0100

Computer games are complex performance-critical graphical applications which require specialized GPU hardware. For this reason, GPU drivers often include many heuristics to help optimize throughput. Recently however, new APIs are emerging which sacrifice many heuristics for lower-level hardware control and more predictable driver behavior. This shifts the burden for many optimizations from GPU driver developers to game programmers, but also provides numerous opportunities to exploit application-specific knowledge."br/""br/"This paper examines different opportunities for specializing GPU code and reducing redundant data transfers. Static analysis of commercial games shows that 5-18% of GPU code is specializable by pruning dead data elements or moving portions to different graphics pipeline stages. In some games, up to 97% of the programs’ data inputs of a particular type, namely uniform variables, are unused, as well as up to 62% of those in the GPU internal vertex-fragment interface. This shows potential for improving memory usage and communication overheads. Insome test scenarios, removing dead uniform data can lead to 6x performance improvements."br/""br/"We also explore the upper limits of specialization if all dynamic inputs are constant at run-time. For instance, if uniform inputs are constant, up to 44% of instructions can be eliminated in some games, with a further 14% becoming constant-foldable at compile time. Analysis of run-time traces, reveals that 48-91% of uniform inputs are constant in real games, so values close to the upper limit may be achieved in practice.

Development and Application of Online Simulation System for Western Products Oil Pipeline

Scipedia content — Mon, 01 Feb 2021 20:32:33 +0100