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CHIARA BACHECHI

Dottorando
Dipartimento di Ingegneria "Enzo Ferrari"


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Pubblicazioni

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 - 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 - 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


2021 - Detection and Classification of Sensor Anomalies for Simulating Urban Traffic Scenarios [Articolo su rivista]
Bachechi, Chiara; Rollo, Federica; Po, Laura
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.