Multi-Agent Vehicle Share System

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

The share of electric vehicles (EVs) is expected to increase significantly in the vehicle market sales in the forthcoming years. Uncontrolled charging of EVs can affect adversely the normal operation of the power system, especially at the distribution level. This paper proposes a distributed, multi-agent EV charging control method based on the Nash Certainty Equivalence Principle that considers network impacts. Convergence of the method when the EVs control agents are uncoupled and weakly-coupled is discussed. The efficiency of the proposed management system is evaluated through simulation of a realistic, urban distribution network.

The agent concept is one of the very few concepts which have been able to cross the borders of scientific disciplines, finding applications not only in computer science and artificial intelligence, but also in biology, economics, engineering, and the social sciences. Thus it is only natural that the research frontiers in this fast growing field develop along interdisciplinary problems. These include the ability of agents to evolve, e.g. through adaptation and learning, but also the emergence of systemic properties which result from the interplay of large numbers of agents, rather than from carefully designed single agent behavior. This topical issue combines original scientific papers selected from two satellite workshops that were held in conjunction with AAMAS 2010, the Ninth International Conference on Autonomous Agents and Multi-Agent Systems. Each of these workshops had its own focus. WEIN’2010 — The Fifth International Workshop on Emergent Intelligence on Networked Agents has put forward the complex networks approach as a framework to model multi-agent systems, i.e. agents are represented by nodes and their interactions are represented by links. Thus, the underlying network structure of a multiagent system plays a crucial role in explaining emergent properties. Networked agents, on the other hand, may be able to actively change this structure by forming new links or cutting existing ones. The selected contributions from this workshop

The formation control problem of intelligent driving vehicles originates from the research on task planning and cooperation of multi-agent system, which is mainly aimed at how multi-intelligent driving vehicles cooperate to complete multi-task team driving behaviors such as formation preparation, formation maintenance, formation change and obstacle avoidance in traffic environment. From the control point of view, The vehicle queue is controlled by a plurality of single vehicle nodes, and individual vehicles are controlled through information interaction among the nodes, and then coupled with each other to form a dynamic system, so that the vehicle queue forms a multi-agent system, which is modeled and analyzed to realize an adaptive formation control model based on MPC and particle swarm optimization algorithm. Finally, Collaborative control of intelligent vehicle formation is simulated to achieve coordinated control of vehicle queue based on multi-agent system.

The urban traffic system is one of the most critical infrastructures. It can be abstracted into a complex network composed of strongly connected groups. Understanding the composition of such networks requires a decomposition of the network into modules, taking into account the way in which network links are distributed among the nodes, which is not an easy task. In such networks, there generally exists for each set of highly interlinked nodes little connectivity with the next set of highly interlinked nodes. This property of nodes makes community detection an important approach for analyzing the structure of complex networks. Today the emerging of big data traffic and the growing availability of smart connected devices [1] such as GPS and other sensors contribute a lot to defining the daily travel activity of individuals and giving a description for the spatial structure of travel modes in a city. In this paper, we propose a solution for exploiting urban big data traffic collected from smart devices, in order to make it available for high level applications. This will be achieved by identifying the underlying structures of the vehicles, the travellers and the stations networks and detecting the multi-layer communities in these networks. We present a global architecture for an intelligent transport network with some details of each its components. The main contribution of the proposed architecture is the Community Analysis component, which allows us to analyse communities with different types of nodes: travelers with their preferences, public transport stations and public vehicles. Then using the community detection method and the network analysis method, we present one application of the proposed system that is related to public transportation optimization. To take into account travelers' preferences, we propose a traveler's profile model and we show how it can help to detect travelers' communities.

The ecological impact of shared light electric vehicles (LEV) such as kick scooters is still widely discussed. Especially the fact that the vehicles and batteries are collected using diesel vans in order to charge empty batteries with electricity of unclear origin is perceived as unsustainable. A better option could be to let the users charge the vehicles themselves whenever it is necessary. For this, a decentralized, flexible and easy to install network of off-grid solar charging stations could bring renewable electricity where it is needed without sacrificing the convenience of a free float sharing system. Since the charging stations are powered by solar energy the most efficient way to utilize them would be to charge the vehicles when the sun is shining. In order to make users charge the vehicle it is necessary to provide some form of benefit for them doing so. This could be either a discount or free rides. A particularly robust and well-established mechanism is controlling incentives via means of blockchain-based crypto-tokens. This paper demonstrates a crypto-token based scheme for incentivizing users to charge sharing vehicles during times of considerable solar irradiation in order to contribute to more sustainable mobility services.

The following paper deals with socialization of the Samara Region youth in the direction of the modern younger generation talents and abilities development. The authors consider modern social environment as a basis of a young person’s identity formation which directly influences his/her character. The authors offer to change methods of social interaction between public organizations and public authorities to increase their effective functioning. For this purpose, they analyze the work of public associations of educators of Samara. The authors offer to use a communicative (intersubjective) way of interaction of all participants of the process which is based on horizontal interaction where each interested party carries out a function within the competence by equal participation. As a result, joint activity gains a character of a community. This kind of interaction increases each participant’s ability to solve joint problems of any complexity, increasing at the same time abilities of others. Using a technology of multi-agent automated systems (MAS) in planning and management of the youth talents and abilities development trajectory, the most effective result in collaboration of public organizations and public authorities can be achieved.

Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

In the large cities, it remains difficult and expensive to create more parking spaces for vehicles since they have almost reached their full occupancy. The lack of available parking places leads to the problem of traffic congestion and consequently to pollution, since drivers will spend a lot of time looking for a vacant parking place. Hence the need to establish a parking management system able to improve the exploitation of the existent parking places at a city level. This paper proposes a new multi agent system for parking vacancies detection based on vision techniques. We explore a network of interconnected video surveillance cameras and parking stations to provide an intelligent service to the drivers in order to facilitate their task and improve the exploitation of the parking resources. The proposed system introduces a new approach for parking spaces modeling and elaborates an adaptive approach for vacancies estimation in order to supply the driver with reliable information about the vacant parking spaces in the city according to the size of his vehicle.

In this era of high health care cost and limited research resources, open access to de-identified clinical research study data may promote increased scientific transparency and rigor, allow for the combination and re-analysis of similar data sets, and decrease un-necessary replication of unpublished negative studies. Driven by expanded computing capabilities, advocacy for data sharing to maximize research value is growing in both translational and clinical research communities. The focus of this study is to report on the current status of publicly available research data from studies published in the top 40 neurology and neurosurgery clinical research journals by impact factor. The top journals were carefully reviewed for data sharing policies. Of the journals with data sharing policies, the 10 most current original research papers from December 2015 – February 2016 were reviewed for data sharing statements and data availability. A data sharing policy existed for 48% (19/40) of the 40 journals investigated. Of the 19 journals with an existing data sharing policy, 58% (11/19) of the policies stated that data should be made available to interested parties upon request and 21% (4/19) of these journals encouraged authors to provide a data sharing statement in the article of what data would be available upon request. Of the 190 articles reviewed for data availability, 21% (40/190) of these articles included some source data in the results, figures, or supplementary sections. This evaluation highlights opportunities for neurology and neurosurgery investigators and journals to improve access to study data and even publish the data prospectively for the betterment of clinical outcome analysis and patient care.

The year of 2017 starts with good news to JIS. It was classified as B3 in the Qualis index for the Computer Science area. Qualis is the journal classification index of CAPES (Brazilian governmental agency for improvement of higher education personnel), and the classification refers to the period of 2013-2016. In the previous period, JIS was classified as B4. This indicates that JIS is being increasingly recognized as an interesting publication vehicle. JIS is also included in Qualis for other areas, such as Engineering, Mathematics/Statistics, Biotechnology, Communication & Information, and Interdisciplinary area. This is an important achievement for us, and certainly motivates us to keep working hard to attract good quality papers and authors.Another important news in 2017 is the participation of Dr. Cléber Gimenez Corrêa, from University of São Paulo, who joint us as JIS Assistant Editor. His work has been fundamental to prepare this one and the following issues. I would like to warmly welcome him!This first issue of JIS in 2017 contains extended versions of five papers selected among the best full papers of SBGames 2014 --- XIII Brazilian Symposium on Games and Digital Entertainment. I would like to thank the authors and Esteban Clua, for acting as guest editor for this special issue, and invite you to read his editorial in the following.In line with the theme of the special issue, we also have in this issue an original paper by Marcelo Zamith, Luis Valente, Bruno Feijó, Mark Joselli, and Esteban Clua, entitled "Parallel game architectures with tardiness policy and workload balance". The authors developed an adaptive game loop architecture based on tardiness control to adapt task operation according to the current hardware load conditions at a given moment. Their adaptive game loop is able to create game task list scheduling, considering multi-core processors.I would like to thank the authors and reviewers that contributed to this issue of JIS, and I hope it fulfills your expectations. JIS Editorial Board is looking forward to receiving your contributions.