Implementing an urban dynamic traffic model

The world of mobility is constantly evolving and proposing new technologies, such as autonomous driving, electromobility, shared-mobility or even new air transport systems. We do not know how people and things will be moving within cities in 30 years, but for sure we know that road network planning and traffic management will remain critical issues. The goal of our research is the implementation of a data-driven micro-simulation traffic model for computing everyday simulations of road traffic in a medium-sized city. A dynamic traffic model is needed in every urban area, we introduce an easy-to-set-up solution for cities that already have traffic sensors installed. Daily traffic flows are created from real data measured by induction loop detectors along the urban roads in Modena. The result of the simulation provides a set of "snapshots" of the traffic flow within the Modena road network every minute. The main contribution of the implemented model is the ability, starting from traffic punctual information on 400 locations, to provide an overview of traffic intensity on more than 800 km of roads

BlendSM-DDM: BLockchain-ENabled Secure Microservices for Decentralized Data Marketplaces

To promote the benefits of the Internet of Things (IoT) in smart communities and smart cities, a real-time data marketplace middleware platform, called the Intelligent IoT Integrator (I3), has been recently proposed. While facilitating the easy exchanges of real-time IoT data streams between device owners and third-party applications through the marketplace, I3 is presently a monolithic, centralized platform for a single community. Although the service oriented architecture (SOA) has been widely adopted in the IoT and cyber-physical systems (CPS), it is difficult for a monolithic architecture to provide scalable, inter-operable and extensible services for large numbers of distributed IoT devices and different application vendors. Traditional security solutions rely on a centralized authority, which can be a performance bottleneck or susceptible to a single point of failure. Inspired by containerized microservices and blockchain technology, this paper proposed a BLockchain-ENabled Secure Microservices for Decentralized Data Marketplaces (BlendSM-DDM). Within a permissioned blockchain network, a microservices based security mechanism is introduced to secure data exchange and payment among participants in the marketplace. BlendSM-DDM is able to offer a decentralized, scalable and auditable data exchanges for the data marketplace.Comment: Accepted and to be presented at the 2nd International Workshop on CLockchain Enabled Sustainable Smart Cities (BLESS 2019), held in conjunction with the 5th IEEE International Smart Cities Conference (ISC2 2019), Casablanca, Morocco, October 14 - 17, 2019. arXiv admin note: text overlap with arXiv:1902.1056

Real-Time Visual Analytics for Air Quality

Raise collective awareness about the daily levels of humans exposure to toxic chemicals in the air is of great significance in motivating citizen to act and embrace a more sustainable life style. For this reason, Public Administrations are involved in effectively monitoring urban air quality with high-resolution and provide understandable visualization of the air quality conditions in their cities. Moreover, collecting data for a long period can help to estimate the impact of the policies adopted to reduce air pollutant concentration in the air. The easiest and most cost-effective way to monitor air quality is by employing low-cost sensors distributed in urban areas. These sensors generate a real-time data stream that needs elaboration to generate adequate visualizations. The TRAFAIR Air Quality dashboard proposed in this paper is a web application to inform citizens and decision-makers on the current, past, and future air quality conditions of three European cities: Modena, Santiago de Compostela, and Zaragoza. Air quality data are multidimensional observations update in real-time. Moreover, each observation has both space and a time reference. Interpolation techniques are employed to generate space-continuous visualizations that estimate the concentration of the pollutants where sensors are not available. The TRAFAIR project consists of a chain of simulation models that estimates the levels of NO and NO2 for up to 2 days. Furthermore, new future air quality scenarios evaluating the impact on air quality according to changes in urban traffic can be explored. All these processes generate heterogeneous data: coming from different sources, some continuous and others discrete in the space-time domain, some historical and others in real-time. The dashboard provides a unique environment where all these data and the derived statistics can be observed and understood

Automatic Publication of Open Data from OGC Services: the Use Case of TRAFAIR Project

This work proposes a workflow for the publication of Open Spatial Data. The main contribution of this work is the automatic generation of metadata extracted from OGC spatial services providing access to feature types and coverages. Besides, this work adopts the GeoDCAT-AP metadata profile for the description of datasets because it allows for an appropriate crosswalk between the annotation requirements in the spatial domain and the metadata models accepted in general Open Data portals. The feasibility of the proposed workflow has been tested within the framework of the TRAFAIR project to publish monitoring and forecasting air quality data

Features of Physiologic Tremor in Diabetic Patients

In this paper, we estimate the effect of fatigue on physiological tremors in adults suffering from diabetes. We used a simple, wearable accelerometer to collect the acceleration data from 5 diabetic subjects with varying physical activity levels. Fatigue was induced via an intermittent submaximal isometric handgrip protocol, normalized for individual grip strength, until voluntary exhaustion. The overall results presented here show that the physiologic tremors in the range of 10-14 Hz are most noticeable under fatigue

Traffic analysis in a smart city

Urbanization is accelerating at a high pace. This places new and critical issues on the transition towards smarter, efficient, livable as well as economically, socially and environmentally sustainable cities. Urban Mobility is one of the toughest challenges. In many cities, existing mobility systems are already inadequate, yet urbanization and increasing populations will increase mobility demand still further. Understanding traffic flows within an urban environment, studying similarities (or dissimilarity) among weekdays, finding the peaks within a day are the first steps towards understanding urban mobility. Following the implementation of a micro-simulation model in the city of Modena based on actual data from traffic sensors, a huge amount of information that describes daily traffic flows within the city were available. This paper reports an in-depth investigation of traffic flows in order to discover trends. Traffic analyzes to compare working days, weekends and to identify significant deviations are performed. Moreover, traffic flows estimations were studied during special days such as weather alert days or holidays to discover particular tendencies. This preliminary study allowed to identify the main critical points in the mobility of the city

From Sensors Data to Urban Traffic Flow Analysis

By 2050, almost 70% of the population will live in cities. As the population grows, travel demand increases and this might affect air quality in urban areas. Traffic is among the main sources of pollution within cities. Therefore, monitoring urban traffic means not only identifying congestion and managing accidents but also preventing the impact on air pollution. Urban traffic modeling and analysis is part of the advanced traffic intelligent management technologies that has become a crucial sector for smart cities. Its main purpose is to predict congestion states of a specific urban transport network and propose improvements in the traffic network that might result into a decrease of the travel times, air pollution and fuel consumption. This paper describes the implementation of an urban traffic flow model in the city of Modena based on real traffic sensor data. This is part of a wide European project that aims at studying the correlation among traffic and air pollution, therefore at combining traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness about air quality where necessary

IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works