Visual analytics for spatio-temporal air quality data

Air pollution is the second biggest environmental concern for Europeans after climate change and the major risk to public health. It is imperative to monitor the spatio-temporal patterns of urban air pollution. The TRAFAIR air quality dashboard is an effective web application to empower decision-makers to be aware of the urban air quality conditions, define new policies, and keep monitoring their effects. The architecture copes with the multidimensionality of data and the real-time visualization challenge of big data streams coming from a network of low-cost sensors. Moreover, it handles the visualization and management of predictive air quality maps series that is produced by an air pollution dispersion model. Air quality data are not only visualized at a limited set of locations at different times but in the continuous space-time domain, thanks to interpolated maps that estimate the pollution at un-sampled locations

GIS-Based Geospatial Data Analysis: the Security of Cycle Paths in Modena

The use of fossil fuels is contributing to the global climate crisis and is threatening the sustainability of the planet. Bicycles are a vital component of the solution, as they can help mitigate the effects of climate change and improve the quality of life for all. However, cities need to be equipped with the necessary infrastructure to support their use guaranteeing safety for cyclists. Moreover, cyclists should plan their route considering the level of security associated with the different available options to reach their destination. The paper tests and presents a method that aims to integrate geographical data from various sources with different geometries and formats into a single view of the cycle paths in the province of Modena, Italy. The Geographic Information System (GIS) software functionalities have been exploited to classify paths in 5 categories: from protected bike lanes to streets with no bike infrastructure. The type of traffic that co-exists in each cycle path was analysed too. The main outcome of this research is a visualization of the cycle paths in the province of Modena highlighting the security of paths, the discontinuity of the routes, and the less covered areas. Moreover, a cycle paths graph data model was generated to perform routing based on the security level

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

Using real sensors data to calibrate a traffic model for the city of Modena

In Italy, road vehicles are the preferred mean of transport. Over the last years, in almost all the EU Member States, the passenger car fleet increased. The high number of vehicles complicates urban planning and often results in traffic congestion and areas of increased air pollution. Overall, efficient traffic control is profitable in individual, societal, financial, and environmental terms. Traffic management solutions typically require the use of simulators able to capture in detail all the characteristics and dependencies associated with real-life traffic. Therefore, the realization of a traffic model can help to discover and control traffic bottlenecks in the urban context. In this paper, we analyze how to better simulate vehicle flows measured by traffic sensors in the streets. A dynamic traffic model was set up starting from traffic sensors data collected every minute in about 300 locations in the city of Modena. The reliability of the model is discussed and proved with a comparison between simulated values and real values from traffic sensors. This analysis pointed out some critical issues. Therefore, to better understand the origin of fake jams and incoherence with real data, we approached different configurations of the model as possible solutions

Gestione e Analisi dei Dati di Mobilità: Gemelli Digitali per la Mobilità Urbana

Con l’intensificarsi dell’urbanizzazione le sfide legate alla pianificazione, al monitoraggio e alla gestione della mobilità urbana diventano sempre più complesse. I dati in tempo reale catturati dai dispositivi IoT (Internet of Things) e dai sistemi di gestione del traffico forniscono informazioni sulla mobilità urbana. I gemelli digitali o Digital Twin (DT) urbani consentono di analizzare questi dati per individuare zone trafficate, ottimizzare la scelta dei percorsi e simulare scenari futuri. Questa tesi descrive due diversi DT di mobilità urbana e la loro implementazione. Il progetto europeo Trafair è un'iniziativa pionieristica finalizzata al monitoraggio della mobilità urbana e della qualità dell'aria in sei città europee. Il progetto ha come scopo lo sviluppo di un sofisticato DT del Traffico e della Qualità dell'Aria (TAQ) che fornisce informazioni sulle condizioni di traffico, i livelli di emissione e l'impatto del traffico sulla qualità dell'aria. Il TAQ DT integra sensori IoT posizionati strategicamente in tutta l'area urbana che monitorano continuamente flussi di traffico e la concentrazione di inquinanti. Nuove metodologie per il processo di pulizia dei dati provenienti dai sensori di traffico e la calibrazione dei sensori low-cost per la qualità dell'aria sono discusse e confrontate. Attraverso i dati raccolti dai sensori di traffico nelle 24 ore precedenti viene generata una simulazione giornaliera del traffico nell’area urbana e un modello di dispersione predice la concentrazione di Nox derivante dal traffico nelle 48 ore successive. I dati raccolti e generati vengono presentati tramite visualizzazioni user-friendly attraverso dashboard dedicate per consentire alla pubblica amministrazione di identificare le aree più congestionate e osservare l'impatto della composizione della flotta di veicoli sulle condizioni di qualità dell'aria della città. Fornendo informazioni in tempo reale, consentendo analisi basate su simulazioni e facilitando decisioni informate, il TAQ DT permette alle città di monitorare il flusso del traffico e stimare le emissioni per creare una città più sostenibile. La complessa natura delle reti di trasporto rappresenta una sfida significativa per la loro analisi e modellazione. Questa tesi propone un approccio innovativo per modellare la rete di trasporto che sfrutta la potenza dei database a grafo: un DT della Mobilità Multimodale Basato su Grafi (GBMMM). Questa soluzione suddivide la complessa rete di trasporto in una serie di livelli più semplici, ognuno rappresentante una modalità di trasporto specifica. Questi livelli sono interconnessi, per consentire l’analisi delle interazioni tra le diverse modalità. Questa rappresentazione granulare migliora significativamente la gestione e l'analisi dei dati di mobilità. La struttura intrinseca, ottimizzata per rappresentare entità interconnesse del database a grafo offre una soluzione convincente per la modellazione e l'analisi delle reti di mobilità urbana perché si allinea perfettamente con la topologia delle reti di mobilità. Integrando i database a grafo con strumenti di analisi e librerie per dati geospaziali, possiamo ottenere una comprensione più approfondita dei percorsi ottimali, individuare le aree congestionate e indagare la relazione tra la topologia della rete e il traffico. Queste informazioni possono supportare le decisioni delle pubbliche amministrazioni, ottimizzare il flusso del traffico, migliorare l'accessibilità e promuovere una mobilità urbana sostenibile. Il modello GBMMM della città di Modena (Italia) è presentato come un caso d'uso per la simulazione di scenari di chiusura delle strade e per sperimentare nuove metodologie di routing basate sulle esigenze dei ciclisti e dei pedoni (es. la sicurezza).As urbanization and population growth continue to escalate, the challenges of planning, monitoring, and managing urban mobility are becoming increasingly complex. The real-time data captured by IoT (Internet of Things) devices and traffic management systems provides insights into urban mobility patterns. Urban Digital Twins can analyse this data to identify traffic congestion hotspots, optimize route planning, and simulate future scenarios. This thesis describes two different Urban Mobility Digital Twins and their implementation. The Trafair European project is a pioneering initiative aimed at monitoring urban mobility and air quality in six European cities. At the heart of this project lies the development of a sophisticated Traffic and Air Quality (TAQ) Digital Twin (DT), a powerful tool that provides comprehensive insights into traffic patterns, emission levels, and the impact of traffic-related factors on air quality. To capture the city's traffic dynamics, the TAQ DT integrates Internet of Things (IoT) sensors strategically positioned across the urban landscape. These sensors continuously monitor traffic flows, vehicle emissions, and other relevant parameters, providing real-time data that feeds the simulation models. New methodologies for the data cleaning process for traffic sensors and the calibration of the low-cost air quality sensors employed are deeply discussed. The TAQ DT simulates the traffic environment through a daily simulation that incorporates data collected by the sensors over the previous 24 hours and the pollutant concentrations for the next 48 hours are estimated through a dispersion model. User-friendly visualizations available in dedicated dashboards are derived from the data generated by the TAQ DT. This intuitive interface enables decision-makers to identify the most congested areas and observe the impact of the vehicle fleet composition on the air quality condition of the city. The Trafair project and its TAQ DT demonstrate the transformative potential of Digital Twins in addressing the challenges of urban mobility and air quality monitoring. By providing real-time insights, enabling simulation-based analysis, and facilitating informed decision-making, the TAQ DT empowers cities to analyse traffic flow and estimate emissions for a healthier and more sustainable urban environment. The intricate nature of urban mobility networks poses a significant challenge to efficient analysis and decision-making. To address this challenge, we propose a Graph-Based Multi-Modal Mobility (GBMMM) model of the urban mobility network that breaks down the complex network into a series of simpler layers, each representing a specific transportation mode. These layers are interconnected, allowing us to analyse the interactions and influence of different modes on overall mobility patterns. This granular representation significantly improves the manageability and analysis of mobility data. Graph databases offer a compelling solution for modelling and analysing urban mobility networks. Their inherent structure, optimized to represent interconnected entities, aligns perfectly with the topology of mobility networks. By seamlessly integrating graph databases with graph analytics tools and spatial libraries, we can gain a deeper understanding of traffic patterns, congestion hotspots, and the relationship between network topology and mobility behaviour. This information can be used to inform strategic decision making, optimize traffic flow, improve accessibility, and promote sustainable urban mobility. The GBMMM model of the city of Modena (Italy) is presented as a use case for the simulation of road closure scenarios and for the experimentation of new routing methodologies based on cyclist and pedestrian needs (e.g., safety)

Digital Twins for Urban Mobility

Urban Digital Twins (DTs) can help tackle the challenges of planning, monitoring, and managing modern cities. Existing mobility systems are already inadequate, yet urbanization and population growth will increase mobility demand still further. For this reason, urban mobility planning can benefit from DTs producing new knowledge executing automatically complex functions based on real-time data. The paper describes two different DTs for urban mobility and their implementation. The first one is the Traffic and Air Quality DT (TAQ) which investigates the relationship between traffic flows and air quality conditions through a chain of simulation models. The second DT is a multi-layered Graph-Based Multi-Modal Mobility (GBMMM) DT to study the interaction between different transport modes

TRAFAIR Traffic Dashboard

<p>The two videos show the TRAFAIR traffic dashboard that was a web application developed within the TRAFAIR European project and that was active between the second half of 2020 and the beginning of 2023. Now the web application is no more available.</p&gt

TRAFAIR Air Quality Dashboard

<p>Videos that show the functionalities of the TRAFAIR Air Quality Dashboard: a web application that was active betweeen 2020 and the beginning of 2023. The dashboard was realized within the scope of the TRAFAIR Eurpean Project.</p&gt