Розробка методики оцінки рівня інформатизації транспортного сектору України як запорука його конкурентоспроможності

With the rapid spread of information and communication technologies in various sectors of economic activity it should increase the informatization level of transport sector, without which it will be difficult in the future to ensure its competitiveness in national and international transport market.In this regard, the object of research in the article is information support of transport sector in Ukraine.The study found that the level of implementation and development of ICT and information management systems in the transport sector does not meet modern requirements and needs of the economy. Existing industry informatization programs are unable to provide a high level of efficiency of transport interaction with other sectors of the national economy, not to mention the development of transport and economic links with countries near and far abroad.The author developed a method of assessing the level of transport informatization using the computer program «Statistical Package for the Social Sciences». Based on the calculation of the integral factor of the transport sector informatization determined that the most important influence on its have using different modes of transport on the Internet, particularly in obtaining banking and financial services and the exchange of data on logistics, production plans, forecast demand. The lowest level has using Internet for e-procurement.Presentation and promotion of transport services in social networks are also at the low level.To create a common information space for transport processes and ensure their safety; implementation of effective intersectoral and interdepartmental interaction of information resources it is offered to create a network of transport supermarkets, which specific features are use of advanced transportation, logistics, information and communication technologies to improve the competitiveness of transport sector in Ukraine.В статье приведен анализ развития информационно-коммуникационных технологий в Украине. Рассмотрена необходимость и проблемы информатизации транспортного сектора. Разработана методика оценки уровня информатизации транспорта с использованием компьютерной программы «Statistical Package for the Social Sciences». На основе рассчитанного интегрального коэффициента уровня информатизации транспортной сферы определены факторы, оказывающие влияние на него.У статті наведено аналіз розвитку інформаційно-комунікаційних технологій в Україні. Розглянуто необхідність і проблеми інформатизації транспортного сектору. Розроблено методику оцінки рівня інформатизації транспорту з використанням комп’ютерної програми «Statistical Package for the Social Sciences». На основі розрахованого інтегрального коефіцієнту рівня інформатизації транспортної сфери визначено фактори, що здійснюють вплив на нього

GUI: GPS-Less Traffic Congestion Avoidance in Urban Areas with Inter-Vehicular Communication

Abstract—Driving in an urban canyon can be frustrating when your GPS teller keeps telling you to make a turn at the place that you just passed, because the information transmission is deferred by the wireless signal reflecting off of buildings and other interfering objects. In this paper, we provide a practical solution for turn-to-turn guidance with inter-vehicle communication in vehicle ad-hoc networks (VANETs). Vehicles collect information from neighbors and catch the snapshot to describe the global impact of traffic congestions, in the presence of unpredictable changes of topology and vehicle trajectory. Without any central-ized control, the information can be aggregated along the traffic flow and be disseminated in a minimal area, while sufficiently guiding each vehicle to achieve a global optimization on its path, and to remain on a non-blocked route. The information constitution is implemented in the proactive model, saving the delay of reconstruction in the reactive model (on-demand). Its substantial improvement on the elapsed time will be shown in the experimental results, compared with the best results known to date in both proactive and reactive information models. Keywords—Information model, inter-vehicular wireless commu-nication, traffic congestion, vehicular ad-hoc network (VANET). I

An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario

An adaptive agent-based approach to traffic simulation

The aim of this work is to present the initial exploration of a behavioural Dynamic Traffic Assignment model, particularly suitable to be used and implemented in agent-based micro-simulations. The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time spent. The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents\u27 strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large networks. Numerical experiments are performed on various scenarios containing different proportions of trained strategic agents, agents with random strategies and non-strategic agents, to test the robustness and adaptability to new environments and varying network conditions. The methodology is also compared against MATSim and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results

Context-aware collaborative storage and programming for mobile users

Since people generate and access most digital content from mobile devices, novel innovative mobile apps and services are possible. Most people are interested in sharing this content with communities defined by friendship, similar interests, or geography in exchange for valuable services from these innovative apps. At the same time, they want to own and control their content. Collaborative mobile computing is an ideal choice for this situation. However, due to the distributed nature of this computing environment and the limited resources on mobile devices, maintaining content availability and storage fairness as well as providing efficient programming frameworks are challenging. This dissertation explores several techniques to improve these shortcomings of collaborative mobile computing platforms. First, it proposes a medley of three techniques into one system, MobiStore, that offers content availability in mobile peer-to-peer networks: topology maintenance with robust connectivity, structural reorientation based on the current state of the network, and gossip-based hierarchical updates. Experimental results showed that MobiStore outperforms a state-of-the-art comparison system in terms of content availability and resource usage fairness. Next, the dissertation explores the usage of social relationship properties (i.e., network centrality) to improve the fairness of resource allocation for collaborative computing in peer-to-peer online social networks. The challenge is how to provide fairness in content replication for P2P-OSN, given that the peers in these networks exchange information only with one-hop neighbors. The proposed solution provides fairness by selecting the peers to replicate content based on their potential to introduce the storage skewness, which is determined from their structural properties in the network. The proposed solution, Philia, achieves higher content availability and storage fairness than several comparison systems. The dissertation concludes with a high-level distributed programming model, which efficiently uses computing resources on a cloud-assisted, collaborative mobile computing platform. This platform pairs mobile devices with virtual machines (VMs) in the cloud for increased execution performance and availability. On such a platform, two important challenges arise: first, pairing the two computing entities into a seamless computation, communication, and storage unit; and second, using the computing resources in a cost-effective way. This dissertation proposes Moitree, a distributed programming model and middleware that translates high-level programming constructs into events and provides the illusion of a single computing entity over the mobile-VM pairs. From programmers’ viewpoint, the Moitree API models user collaborations into dynamic groups formed over location, time, or social hierarchies. Experimental results from a prototype implementation show that Moitree is scalable, suitable for real-time apps, and can improve the performance of collaborating apps regarding latency and energy consumption

Vehicle re-routing strategies for congestion avoidance

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This dissertation introduces a cost-effective and easily deployable vehicular re-routing system that reduces the effects of traffic congestion. The system collects real-time traffic data from vehicles and road-side sensors, and computes proactive, individually tailored re-routing guidance, which is pushed to vehicles when signs of congestion are observed on their routes. Subsequently, this dissertation proposes and evaluates two classes of re-routing strategies designed to be incorporated into this system, namely, Single Shortest Path strategies and Multiple Shortest Paths Strategies. These strategies are firstly implemented in a centralized system, where a server receives traffic updates from cars, computes alternative routes, and pushes them as guidance to drivers. The extensive experimental results show that the proposed strategies are capable of reducing the travel time comparable to a state-of-the-art Dynamic Traffic Assignment (DTA) algorithm, while avoiding the issues that make DTA impractical, such as lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows the system to be tuned to different levels of trade-off between re-routing effectiveness and computational efficiency. Also, the proposed traffic guidance system is robust even if many drivers ignore the guidance, or if the system adoption rate is relatively low. The centralized system suffers from two intrinsic problems: the central server has to perform intensive computation and communication with the vehicles in real-time, which can make such solutions infeasible for large regions with many vehicles; and driver privacy is not protected since the drivers have to share their location as well as the origins and destinations of their trips with the server, which may prevent the adoption of such solutions. To address these problems, a hybrid vehicular re-routing system is presented in this dissertation. The system off-loads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To make collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. The system is hybrid because it still uses a server to determine an accurate global view of the traffic. In addition, the user privacy is balanced with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy substantially, while the re-routing effectiveness is minimally impacted

A parallelized micro-simulation platform for population and mobility behavior. Application to Belgium.

In this book we aim at developing an agent-based micro-simulation framework for (large) population evolution and mobility behaviour. More specifically we focus on the agents generation and the traffic simulation parts of the platform, and its application to Belgium. Hence we firstly develop a synthetic population generator whose main characteristics are its sample-free nature, its ability to cope with moderate data inconsistencies and different levels of aggregation. We then generate the traffic demand forecasting with a stochastic and flexible activity-based model relying on weak data requirements. Finally, a traffic simulation is completed by considering the assignment of the generated demand on the road network. We give the initial developments of a strategic agent-based alternative to the conventional simulation-based dynamic traffic assignment models

Proactive Vehicle Re-routing Strategies for Congestion Avoidance

International audienceTraffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents three traffic re-routing strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces the effect of traffic congestions. This system collects real-time traffic data from vehicles and road-side sensors and computes proactive, individually-tailored re-routing guidance which is pushed to vehicles when signs of congestion are observed on their route. Extensive simulation results over two urban road networks show that all three strategies, namely multipath load balancing considering future vehicle positions (EBkSP), random multipath load balancing (RkSP), and dynamic shortest path (DSP), significantly decrease the average travel time. EBkSP is the best, with as much as 104% improvement compared to the "no re-routing" baseline. Additionally, it lowers with 34% the re-routing frequency compared to the other strategies. Finally, all strategies offer good improvements even when many drivers ignore the guidance or when the system adoption rate is relatively low

Préserver la vie privée des individus grâce aux Systèmes Personnels de Gestion des Données

Riding the wave of smart disclosure initiatives and new privacy-protection regulations, the Personal Cloud paradigm is emerging through a myriad of solutions offered to users to let them gather and manage their whole digital life. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. Our work addresses these questions on three levels. First, we review, compare and analyze personal cloud alternatives in terms of the functionalities they provide and the threat models they target. From this analysis, we derive a general set of functionality and security requirements that any Personal Data Management System (PDMS) should consider. We then identify the challenges of implementing such a PDMS and propose a preliminary design for an extensive and secure PDMS reference architecture satisfying the considered requirements. Second, we focus on personal computations for a specific hardware PDMS instance (i.e., secure token with mass storage of NAND Flash). In this context, we propose a scalable embedded full-text search engine to index large document collections and manage tag-based access control policies. Third, we address the problem of collective computations in a fully-distributed architecture of PDMSs. We discuss the system and security requirements and propose protocols to enable distributed query processing with strong security guarantees against an attacker mastering many colluding corrupted nodes.Surfant sur la vague des initiatives de divulgation restreinte de données et des nouvelles réglementations en matière de protection de la vie privée, le paradigme du Cloud Personnel émerge à travers une myriade de solutions proposées aux utilisateurs leur permettant de rassembler et de gérer l'ensemble de leur vie numérique. Du côté positif, cela ouvre la voie à de nouveaux services à valeur ajoutée lors du croisement de plusieurs sources de données d'un individu ou du croisement des données de plusieurs personnes. Cependant, ce changement de paradigme vers la responsabilisation de l'utilisateur soulève des questions fondamentales quant à l'adéquation des fonctionnalités et des techniques de gestion et de protection des données proposées par les solutions existantes aux utilisateurs lambda. Notre travail aborde ces questions à trois niveaux. Tout d'abord, nous passons en revue, comparons et analysons les alternatives de cloud personnel au niveau des fonctionnalités fournies et des modèles de menaces ciblés. De cette analyse, nous déduisons un ensemble général d'exigences en matière de fonctionnalité et de sécurité que tout système personnel de gestion des données (PDMS) devrait prendre en compte. Nous identifions ensuite les défis liés à la mise en œuvre d'un tel PDMS et proposons une conception préliminaire pour une architecture PDMS étendue et sécurisée de référence répondant aux exigences considérées. Ensuite, nous nous concentrons sur les calculs personnels pour une instance matérielle spécifique du PDMS (à savoir, un dispositif personnel sécurisé avec un stockage de masse de type NAND Flash). Dans ce contexte, nous proposons un moteur de recherche plein texte embarqué et évolutif pour indexer de grandes collections de documents et gérer des politiques de contrôle d'accès basées sur des étiquettes. Troisièmement, nous abordons le problème des calculs collectifs dans une architecture entièrement distribuée de PDMS. Nous discutons des exigences d'architectures système et de sécurité et proposons des protocoles pour permettre le traitement distribué des requêtes avec de fortes garanties de sécurité contre un attaquant maîtrisant de nombreux nœuds corrompus