A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

Guest Editorial: Introduction to the Special Issue on Advances in Smart and Green Transportation for Smart Cities

According to a recent UN report, continuing population growth and urbanization are expected to increase the world’s urban population by 2.5 billion people by 2050, with 2.9 billion extra vehicles. This massive growth in both population and number of vehicles, together with urban transformation and a trend toward mega cities, creates greater and more challenges for achieving smart transportation goals in smart cities. Therefore, new and more integrated modes of transportation, and environment friendly solutions are required to accommodate the rising demands of high liveability in smarter cities that offer safe, secure, affordable, reliable and sustainable transportation in old and new markets alike

Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City

Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications. Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics

Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City

Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications. Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics

Dynamic Vehicular Routing in Urban Environments

Traffic congestion is a persistent issue that most of the people living in a city have to face every day. Traffic density is constantly increasing and, in many metropolitan areas, the road network has reached its limits and cannot easily be extended to meet the growing traffic demand. Intelligent Transportation System (ITS) is a world wide trend in traffic monitoring that uses technology and infrastructure improvements in advanced communication and sensors to tackle transportation issues such as mobility efficiency, safety, and traffic congestion. The purpose of ITS is to take advantage of all available technologies to improve every aspect of mobility and traffic. Our focus in this thesis is to use these advancements in technology and infrastructure to mitigate traffic congestion. We discuss the state of the art in traffic flow optimization methods, their limitations, and the benefits of a new point of view. The traffic monitoring mechanism that we propose uses vehicular telecommunication to gather the traffic information that is fundamental to the creation of a consistent overview of the traffic situation, to provision real-time information to drivers, and to optimizing their routes. In order to study the impact of dynamic rerouting on the traffic congestion experienced in the urban environment, we need a reliable representation of the traffic situation. In this thesis, traffic flow theory, together with mobility models and propagation models, are the basis to providing a simulation environment capable of providing a realistic and interactive urban mobility, which is used to test and validate our solution for mitigating traffic congestion. The topology of the urban environment plays a fundamental role in traffic optimization, not only in terms of mobility patterns, but also in the connectivity and infrastructure available. Given the complexity of the problem, we start by defining the main parameters we want to optimize, and the user interaction required, in order to achieve the goal. We aim to optimize the travel time from origin to destination with a selfish approach, focusing on each driver. We then evaluated constraints and added values of the proposed optimization, providing a preliminary study on its impact on a simple scenario. Our evaluation is made in a best-case scenario using complete information, then in a more realistic scenario with partial information on the global traffic situation, where connectivity and coverage play a major role. The lack of a general-purpose, freely-available, realistic and dependable scenario for Vehicular Ad Hoc Networks (VANETs) creates many problems in the research community in providing and comparing realistic results. To address these issues, we implemented a synthetic traffic scenario, based on a real city, to evaluate dynamic routing in a realistic urban environment. The Luxembourg SUMO Traffic (LuST) Scenario is based on the mobility derived from the City of Luxembourg. The scenario is built for the Simulator of Urban MObiltiy (SUMO) and it is compatible with Vehicles in Network Simulation (VEINS) and Objective Modular Network Testbed in C++ (OMNet++), allowing it to be used in VANET simulations. In this thesis we present a selfish traffic optimization approach based on dynamic rerouting, able to mitigate the impact of traffic congestion in urban environments on a global scale. The general-purpose traffic scenario built to validate our results is already being used by the research community, and is freely-available under the MIT licence, and is hosted on GitHub

Linked data as medium for distributed Multi-Agent Systems

The conceptual design and discussion of multi-agents systems (MAS) typically focuses on agents and their models, and the elements and effects in the environment which they perceive. This view, however, leaves out potential pitfalls in the later implementation of the system that may stem from limitations in data models, interfaces, or protocols by which agents and environments exchange information. By today, the research community agrees that for this, that the environment should be understood as well as abstraction layer by which agents access, interpret, and modify elements within the environment. This, however, blurs the the line of the environment being the sum of interactive elements and phenomena perceivable by agents, and the underlying technology by which this information and interactions are offered to agents. This thesis proposes as remedy to consider as third component of multi agent systems, besides agents and environments, the digital medium by which the environment is provided to agents. "Medium" then refers to exactly this technological component via which environment data is published interactively towards the agents, and via which agents perceive, interpret, and finally, modify the underlying environment data. Furthermore, this thesis will detail how MAS may use capabilities of a properly chosen medium to achieve coordinating system behaviors. A suitable candidate technology for digital agent media comes from the Semantic Web in form of Linked Data. In addition to conceptual discussions about the notions of digital agent media, this thesis will provide in detail a specification of a Linked Data agent medium, and detail on means to implement MAS around Linked Data media technologies.Sowohl der konzeptuelle Entwurf von, als auch die wissenschaftliche Diskussion über Multi-Agenten-Systeme (MAS) konzentrieren sich für gewöhnlich auf die Agenten selbst, die Agentenmodelle, sowie die Elemente und Effekte, die sie in ihrer Umgebung wahrnehmen. Diese Betrachtung lässt jedoch mögliche Probleme in einer späteren Implementierung aus, die von Einschränkungen in Datenmodellen, Schnittstellen, oder Protokollen herrühren können, über die Agenten und ihre Umgebung Informationen miteinander austauschen. Heutzutage ist sich die Forschungsgemeinschaft einig, dass die Umgebung als solche als Abstraktionsschicht verstanden werden sollte, über die Agenten Umgebungseffekte und -elemente wahrnehmen, interpretieren, und mit ihnen interagieren. Diese Betrachtungsweise verschleiert jedoch die Trennung zwischen der Umgebung als die Sammlung interaktiver Elemente und wahrnehmbarer Phänomene auf der einen Seite, und der zugrundeliegenden Technologie, über die diese Information den Agenten bereitgestellt wird, auf der anderen. Diese Dissertation schlägt als Lösung vor, zusätzlich zu Agenten undUmgebung ein digitales Medium, über das Agenten die Umgebung bereitgestellt wird, als drittes Element von Multi-Agenten-Systemen zu betrachten. Der Begriff "Medium" bezieht sich dann genau auf diese technologische Komponente, über die Umgebungsinformationen Agenten interaktiv bereitgestellt werden, und über die Agenten die zugrundeliegenden Daten wahrnehmen, interpretieren, und letztendlich modifizieren. Desweiteren wird diese Dissertation aufzeigen, wie die Eigenschaften eines sorgfältig gewählten Mediums ausgenutzt werden können, um ein koordiniertes Systemverhalten zu erreichen. Ein geeigneter Kandidat für ein digitales Agentenmedium findet sich im Ökosystem des „Semantic Web”, in Form von „Linked Data”, wörtlich („verknüpfte Daten”). Zusätzlich zu einer konzeptionellen Diskussion über die Natur digitaler Agenten- Media, spezifiziert diese Dissertation „Linked Data” als Agentenmedium detailliert aus, und beschreibt im Detail die Mittel, wie sich MAS um Linked Data Technologien herum implementieren lassen

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains