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CHIARA BACHECHI

Dottorando
Dipartimento di Ingegneria "Enzo Ferrari"

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Pubblicazioni

2024 - HypeAIR: A novel framework for real-time low-cost sensor calibration for air quality monitoring in smart cities [Articolo su rivista]
Bachechi, Chiara; Rollo, Federica; Po, Laura
abstract

2023 - Anomaly Detection and Repairing for Improving Air Quality Monitoring [Articolo su rivista]
Rollo, F.; Bachechi, C.; Po, L.
abstract

Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants’ concentrations can support the identification of urban areas with poor air quality and raise citizens’ awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms’ performances.

2022 - Big Data Analytics and Visualization in Traffic Monitoring [Articolo su rivista]
Bachechi, Chiara; Po, Laura; Rollo, Federica
abstract

This paper presents a system that employs information visualization techniques to analyze urban traffic data and the impact of traffic emissions on urban air quality. Effective visualizations allow citizens and public authorities to identify trends, detect congested road sections at specific times, and perform monitoring and maintenance of traffic sensors. Since road transport is a major source of air pollution, also the impact of traffic on air quality has emerged as a new issue that traffic visualizations should address. Trafair Traffic Dashboard exploits traffic sensor data and traffic flow simulations to create an interactive layout focused on investigating the evolution of traffic in the urban area over time and space. The dashboard is the last step of a complex data framework that starts from the ingestion of traffic sensor observations, anomaly detection, traffic modeling, and also air quality impact analysis. We present the results of applying our proposed framework on two cities (Modena, in Italy, and Santiago de Compostela, in Spain) demonstrating the potential of the dashboard in identifying trends, seasonal events, abnormal behaviors, and understanding how urban vehicle fleet affects air quality. We believe that the framework provides a powerful environment that may guide the public decision-makers through effective analysis of traffic trends devoted to reducing traffic issues and mitigating the polluting effect of transportation.

2022 - Detection and Classification of Sensor Anomalies for Simulating Urban Traffic Scenarios [Articolo su rivista]
Bachechi, Chiara; Rollo, Federica; Po, Laura
abstract

2022 - Digital Twins for Urban Mobility [Relazione in Atti di Convegno]
Bachechi, Chiara
abstract

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.

2022 - GIS-Based Geospatial Data Analysis: the Security of Cycle Paths in Modena [Relazione in Atti di Convegno]
Bachechi, Chiara; Po, Laura; Degliangeli, Federico
abstract

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.

2022 - Real-Time Visual Analytics for Air Quality [Capitolo/Saggio]
Bachechi, C.; Po, L.; Desimoni, F.
abstract

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.

2022 - Road Network Graph Representation for Traffic Analysis and Routing [Relazione in Atti di Convegno]
Bachechi, C.; Po, L.
abstract

The road network is the infrastructure along which the mobility of users and goods takes place; the analysis of these networks in terms of spatial and graph theoretical approaches can provide insights to understand urban mobility, improve daily commuting, and reflect on new, more sustainable, scenarios. This paper presents an open-source framework to analyze the road network and investigate the relationship between its topology and traffic conditions. Open-source geographical data are stored in a graph database containing roads, junctions, and Points of Interest (POI), allowing importing of traffic data. The framework includes routing algorithms to obtain the optimal path based on different aspects such as distance, traffic volume, and the number of traversed junctions; furthermore, it allows simulating road closures to observe how they affect road viability. The framework was tested in the use case of the city of Modena (Italy) providing promising results.

2022 - Semi Real-time Data Cleaning of Spatially Correlated Data in Traffic Sensor Networks [Relazione in Atti di Convegno]
Rollo, Federica; Bachechi, Chiara; Po, Laura
abstract

2021 - Anomaly Detection in Multivariate Spatial Time Series: A Ready-to-Use implementation [Relazione in Atti di Convegno]
Bachechi, Chiara; Rollo, Federica; Po, Laura; Quattrini, Fabio
abstract

2020 - Real-time data cleaning in traffic sensor networks [Relazione in Atti di Convegno]
Bachechi, Chiara; Rollo, Federica; Po, Laura
abstract

2020 - Using real sensors data to calibrate a traffic model for the city of Modena [Relazione in Atti di Convegno]
Bachechi, Chiara; Rollo, Federica; Desimoni, Federico; Po, Laura
abstract

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.

2020 - Visual analytics for spatio-temporal air quality data [Relazione in Atti di Convegno]
Bachechi, Chiara; Desimoni, Federico; Po, Laura
abstract

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.

2019 - From Sensors Data to Urban Traffic Flow Analysis [Relazione in Atti di Convegno]
Po, Laura; Rollo, Federica; Bachechi, Chiara; Corni, Alberto
abstract

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.

2019 - Implementing an urban dynamic traffic model [Relazione in Atti di Convegno]
Bachechi, C.; Po, L.
abstract

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.

2019 - Traffic analysis in a smart city [Relazione in Atti di Convegno]
Bachechi, C.; Po, L.
abstract

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.