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Transport Research for Innovation and Sustainability (TRIS)
Smart card data mining to analyse mobility patterns
Thesis in external company
keywords DATA MINING, NONLINEAR ANALYSIS, COMPLEX NETWORK, INTELLIGENT TRANSPORT SYSTEMS, MODELLING AND EXPERIMENTAL TESTS, STATISTICAL DATA ANALYSIS
Reference persons CRISTINA PRONELLO
Research Groups Transport Research for Innovation and Sustainability (TRIS)
Thesis type DATA ANALYSIS AND MODELING, DATA MINING, EXPERIMENTAL AND MODELING, PUBLIC TRANSPORT
Description The thesis aims to define an algorithm capable of building the origin-destination matrix from the check-in data collected in the public transport network of Torino where thousands of people commute every day, using smart cards to validate their travel documents while boarding.
The student will have to benchmark the current models defining the origin-destination data from smart-card validations recorded in Torino and other cities transport network.
The students will have to:
- individuate the best approach allowing to understand the destination of the trips knowing only the origin;
- build the origin-destination matrixes of the users of public transport;
- calculate the load on the lines.
The students will start from a model developed in a previous work in Oise department (France) and try to improve it and increase its precision.
Required skills Preferably, the needed skills are software programming, data mining, algorithm development. A knowledge of transport systems is also useful to better exploit the potentiality of the ICT.
Deadline 05/11/2025
PROPONI LA TUA CANDIDATURA