Internship Opportunities

Urban mobility modeling and analysis

Project Context

MoST is offering an internship opportunity focused on urban mobility analysis through the application of data-driven methods and statistical modeling. The project aims to leverage traffic data, sensor observations, and network information to understand and estimate mobility patterns in urban contexts.

The internship is framed within the wider Bologna Digital Twin project, which FBK is carrying out in collaboration with the Municipality of Bologna. Specifically, the use case concerns urban mobility, aiming to evaluate both the direct impacts (on traffic, public transport usage, etc.) and indirect impacts (on emissions, socio-economic aspects, etc.) of different potential policies designed to reduce private vehicle use in Bologna.

Planned Activities

The intern will contribute to:

• Managing data collection and integration
• Develop and apply analytical methods (e.g., time series analysis, network-based estimation techniques, assignment models) to characterize traffic patterns, estimate mobility flows, and assess the impact of policy interventions

This internship provides the chance to work in a multidisciplinary team and to actively participate in applied research that combines behavioral analytics with user-centered approaches.

Key Topics

• Analysis of the impact of the ticket price increase on private vehicle usage: Has public transport usage been discouraged, in favor of higher private vehicle use? To answer this project question, the intern will use traffic sensor time series data to study whether the introduction of the new public transport pricing system has significantly affected urban mobility.
• Estimation of vehicle traffic on the Bologna road network based on observations from a limited set of streets: The goal is to use traffic sensor counts in Bologna, which are partial and limited to the sensor locations, to estimate traffic on other streets without sensors.
• Development of a data-driven traffic assignment method: The goal is to map trips from an origin-destination matrix onto streets while taking into account traffic observations on the roads. The new method will be based on modifications of existing traffic assignment tools developed within the MoST unit (FTS).

Requirements

• Academic background in Computer Science, Computer Engineering, Data Science, Engineering, Physics, Mathematics, or related technical field
• Good data analysis skills and knowledge of or interest in statistical modeling and network analysis
• Experience with Python
• Fluency in written and spoken English (minimum B2 level)
• Ability to work collaboratively in a research setting

Reference person

Arianna Burzacchi: aburzacchi@fbk.eu.

Urban environment characterization and analysis

Project Context

MoST is offering an internship opportunity focused on the characterization and analysis of urban environments through the integration of geospatial data, combining objective physical characteristics of urban infrastructure with subjective factors that influence citizens' perception and use of urban spaces.

The internship is framed within the wider Bologna Digital Twin project, which FBK is carrying out in collaboration with the Municipality of Bologna. Specifically, the use case concerns urban planning in the historical city center, aiming to evaluate and suggest improvements to the characterization of the urban structure in terms of accessibility and equity.

Planned Activities

In this context, the intern will contribute to:

• Data collection and integration: extract and process urban infrastructure and service data from OpenStreetMap and other relevant sources
• Urban network characterization and analysis: construct and analyze key indicators for modeling the urban environment
• Validation: visualizations and tests to assess the effectiveness of the proposed characterization for specific case studies (e.g., specific fragility groups)

This internship provides the chance to work in a multidisciplinary team and to actively participate in applied research that combines behavioral analytics with user-centered approaches.

Key topics

• Construction and characterization of the transport road graph for the study of proximity in historical centers: The project aims to create a road graph that not only contains physical information about the city system (e.g., number of lanes, travel speed) but is also characterized by additional KPIs that may influence perception (e.g., street lighting presence and intensity, presence of sidewalks, proximity to services). OpenStreetMap will be used as the data source to extract and process street information. Methodologically, this study will use geostatistics and network analysis methods to model proximity indicators.

Requirements

• Academic background in Computer Science, Computer Engineering, Data Science, Engineering, Physics, Mathematics, or related technical field
• Good data analysis skills and interest in geospatial tools and analysis
• Experience with Python
• Fluency in written and spoken English (minimum B2 level)
• Ability to work collaboratively in a research setting

Reference person

Arianna Burzacchi: aburzacchi@fbk.eu.