Social Software, Groups, and Governance

Formal groups play an important role in the law. Informal groups largely lie outside it. Should the law be more attentive to informal groups? The paper argues that this and related questions are appearing more frequently as a number of computer technologies, which I collect under the heading social software, increase the salience of groups. In turn, that salience raises important questions about both the significance and the benefits of informal groups. The paper suggests that there may be important social benefits associated with informal groups, and that the law should move towards a framework for encouraging and recognizing them. Such a framework may be organized along three dimensions by which groups arise and sustain themselves: regulating places, things, and stories

Congestion-Clearing Payments to Passengers

Peak period motor vehicle traffic volume congests roads all over the world. This project hypothesizes implementing congestion- clearing payments to passengers as a permanent congestion-management solution. Ongoing congestion-free travel would be achieved by removing existing congestion, and absorbing (re)generated demand, at costs that would be expected to increase as the total number of travelers increases over time. The project develops a comprehensive, step-by-step methodology to calculate the benefits and costs of paying for drivers to become passengers at a congestion-clearing level and to maintain this level over time. The method is derived from the literature, analysis by the project team, and development of a case study. The case study, based on a long-standing bottleneck location in California, enabled the project team to think through the real challenges of developing and evaluating such a solution. The project finds that the conceptual underpinning of the solution is sound. Based on a survey, the case study finds that there is a level of payment that could clear congestion and maintain free-flow for twenty years, with benefits that outweigh costs on a net present value basis by about four to one—though calibration is required. After the initial reward clears the queue at the bottleneck, a significant intra-peak demand shift would occur as existing and new travelers depart home at times that are more to their liking, potentially causing the queue to re-form. A second incentive manages time of travel, rewarding people for traveling as passengers earlier (or later) than the preferred high demand peak-of-the-peak. In the case study, the high proportion of people who say they will only drive alone would eventually result in some periods of single-occupant-vehicle-only traffic during peak, which is an unintended and undesirable consequence. For the case study route, a limit on single-occupant-vehicle travel during the peak- of-the-peak would ensure that high-occupancy-vehicle travel is given preference and would reduce the overall cost of the solution. For the case study, the cost of the congestion-clearing payments-to-passengers solution on a net present value basis is within the estimated range of costs of the alternative of expanding the facility, and the benefits are expected to be greater than for facility expansion. Congestion-clearing payments to passengers can be implemented much sooner and will have greater positive long-term economic impacts. Facility expansion would provide lower and shorter-term benefits and would be expected to return to congested conditions within a year. The project team proposes a pilot project on the case study route to test and calibrate the solution, as well as recommending development of further case study routes to find out how different routes vary and determine the causes of any variations

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system

IoT analytics and agile optimization for solving dynamic team orienteering problems with mandatory visits

Transport activities and citizen mobility have a deep impact on enlarged smart cities. By analyzing Big Data streams generated through Internet of Things (IoT) devices, this paper aims to show the efficiency of using IoT analytics, as an agile optimization input for solving real-time problems in smart cities. IoT analytics has become the main core of large-scale Internet applications, however, its utilization in optimization approaches for real-time configuration and dynamic conditions of a smart city has been less discussed. The challenging research topic is how to reach real-time IoT analytics for use in optimization approaches. In this paper, we consider integrating IoT analytics into agile optimization problems. A realistic waste collection problem is modeled as a dynamic team orienteering problem with mandatory visits. Open data repositories from smart cities are used for extracting the IoT analytics to achieve maximum advantage under the city environment condition. Our developed methodology allows us to process real-time information gathered from IoT systems in order to optimize the vehicle routing decision under dynamic changes of the traffic environments. A series of computational experiments is provided in order to illustrate our approach and discuss its effectiveness. In these experiments, a traditional static approach is compared against a dynamic one. In the former, the solution is calculated only once at the beginning, while in the latter, the solution is re-calculated periodically as new data are obtained. The results of the experiments clearly show that our proposed dynamic approach outperforms the static one in terms of rewardsThis project has received the support of the Ajuntament of Barcelona and the Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001)Peer ReviewedPostprint (published version

FEUPooling: Carpooling Platform

Atualmente, os automóveis são um dos mais populares meios de transporte, sendo que a maioria apenas é utilizado por uma pessoa. Isto faz com que em zonas mais urbanas se crie muito trânsito, o que leva, por exemplo, ao aumento da poluição atmosférica e sonora. Para além disso, o preço de combustíveis, portagens e estacionamentos tem vindo a aumentar gradualmente. Em muitos casos, os transportes públicos não são opção devido à falta de conforto, liberdade e flexibilidade de horários. Assim sendo, a ideia de carpooling surgiu com objetivo de minimizar estes problemas. Carpooling é a partilha de viagens em veículos privados, onde se pretende que viaje mais que uma pessoa por viatura.Contudo, nem todos são muito recetivos a esta ideia, sendo que é apontado como um dos principais entraves a falta de segurança e confiança em partilhar transporte com desconhecidos. Porém, no contexto específico de uma organização como a FEUP, já existe uma comunidade de utilizadores conhecida e de confiança, o público alvo deste projeto, sendo que este problema não se aplica. Nesta comunidade podem antecipar-se vários comportamentos e rotinas, viagens e horários. Isto pode ajudar a simplificar vários aspetos do carpooling, tornando assim a adoção mais fácil. Adicionalmente, isto permite uma integração da plataforma com os existentes serviços de informação organizacionais da FEUP, o que aumenta o fator de segurança e confiança no sistema.Com este tipo de partilha de transporte, há a possibilidade de poupar dinheiro ao dividir custos de combustível, estacionamento e portagens pelos passageiros envolvidos. Para além disso, é uma forma mais ecológica e sustentável de viajar pois reduz a densidade do trânsito, diminuindo assim a emissão dos gases com efeito de estufa. Mais ainda, a zona envolvente à FEUP carece de estacionamento gratuito, daí que a introdução de um serviço deste tipo seja benéfica.O projeto proposto pelo Comissariado para a Sustentabilidade da FEUP consistiu em desenvolver aplicações móveis para a comunidade descrita. O sistema, que é o cerne deste projeto, tinha como objetivo ser o mais simples de usar possível de forma a obter a máxima adesão alcançável. Os utilizadores - neste caso, os estudantes - podem publicar anúncios de ofertas de partilha de transporte como condutores ou procurar por viagens como passageiros. Adicionalmente, um sistema de pontos foi adicionado. Este sistema pode ser considerado um mecanismo simples de gamification, de forma a motivar e recompensar utilizadores que usem o serviço. Um sistema de back-end verifica se uma viagem foi considerada válida, calculando a similaridade das rotas dos passageiros antes de recompensar os participantes na viagem com pontos. Estes pontos podem posteriormente ser trocados por recompensas a serem definidas pela faculdade.Para além disso, uma aplicação web para propósitos administrativos foi desenvolvida para que o sistema possa ser controlado e gerido. O(s) administrador(es) pode(m) ver informação relacionada com viagens e utilizadores com o objetivo de garantir que não existem comportamentos irregulares. A plataforma também permite que um administrador troque os pontos dos utilizadores por recompensas.Finalmente, quanto a resultados, espera-se que no início do próximo ano letivo a plataforma esteja pronta a ser testada, sob forma de protótipo, por uma pequena parte da comunidade estudantil da FEUP. Se tudo correr como esperado, o produto estará então pronto para ser testado por um mais elevado número de utilizadores. O objetivo final é que todos os estudantes interessados possam aceder a esta aplicação de forma a poder partilhar transporte de forma facilitada e sem preocupações acerca da segurança.Nowadays, automobiles are one of the most popular means of transportation and the majority are used by only one person. This leads to a lot of traffic in the urban areas, which causes, for example, an increase in atmospheric and sound pollution. Besides that, the price of fuel, tolls and parking has been gradually increasing. In many cases, public transportation is not an option because of the lack of comfort, freedom and flexibility of schedules. Therefore, the idea of carpooling emerged with the goal of minimizing these problems. Carpooling is the sharing of private vehicle journeys so that more than one person can travel in the same car.However, not everyone is receptive to this idea, and the main obstacles stated are the lack of security and trust in sharing a ride with strangers. Nevertheless, in the specific context of an organization like FEUP, the community of users is known and trusted, so this problem does not apply. On this community, several behaviors, routines, travels and schedules can be anticipated. This can help simplify several aspects of carpooling, making the adoption easier. In addition, this allows for an integration of the platform with the existing organizational information system of FEUP which in turn increases the security and trustability of the system.With this kind of ride sharing, money can be saved by splitting costs of fuel, parking and tolls by the passengers involved. Apart from that, it is a more ecological and sustainable way of commuting because it reduces the traffic density, consequently reducing the emission of greenhouse gases. Furthermore, the surrounding area of FEUP lacks free parking, so the introduction of such a service would be beneficial.The project proposed by the Comissariado para a Sustentabilidade da FEUP consisted on the development of mobile applications for the described community. This system, which is the core of the project, aimed to be as easy to use as possible in order to have the maximum possible adoption. The users - in this case, the students - are able to post ride sharing offers as drivers or search for these as passengers. Additionally, a points system was added. This system can be considered a simple gamification mechanism, in order to motivate and reward users who use the service. A back-end system then verifies if a trip was valid, by calculating the similarity of the passenger's routes before rewarding the trip's participants with points. These points can then be exchanged later by rewards to be defined by the faculty.Besides that, a web application for administrative purposes was also developed so that the system can be controlled and managed. The administrator(s) can view information about trips and users in order to ensure that there are no irregular behaviors. The platform also allows an administrator to exchange a user's points for rewards.Finally, in terms of results, at the beginning of the next school year the platform is expected to enter a wider testing phase, in the form of a prototype, by a small part of the FEUP student community. If all goes as expected, the product will then be ready to be tested by a larger number of users. The goal is for all interested students to be able to access this application in order to share their commute in an easy way without being concerned about security