| KEYWORD |
Data analysis of Automated Passenger Counting (APC) related to bus rides in public transport. Analysis of massive amounts of public transport passenger data
Thesis in external company
keywords DATA MINING, NONLINEAR ANALYSIS, COMPLEX NETWORK, 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
Description Counting people has become crucial for public transport operators. Thanks to the progress of the Internet of Things (IoT), the number of IoT devices has increased manifold, allowing the collection of a huge amount of data to facilitate people counting. The thesis will have to deal with data collected on buses and compare them with data measured by automatic devices installed on the buses to verify their accuracy and improve it using, for example, deep learning and any other relevant technique. Activities can refer to cloud database organization; collection of data on the vehicles; data analysis algorithms.
Required skills Preferably, knowledge of advanced statistical analysis and statistical modelling, with ability to manage large databases. Use of statistical software such as, for example, SPSS, SAS, R.
Deadline 05/11/2025
PROPONI LA TUA CANDIDATURA