| KEYWORD |
Transport Research for Innovation and Sustainability (TRIS)
People counter in bus stops, stations (metro, train, airport)
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 Understand the mobility patterns of the users is needed to improve the transport network of the cities as well as the use of the different public spaces (road and squares). To implement an Intelligent Transport System, new technologies are applied for collecting and processing relevant data. In terms of mobility patterns, in this thesis we would like to answer the following questions: How many people is using this bus/metro/train stop or airport? Is this stop/station used mainly for departure, arriving or transit? What is the average waiting time of passengers in these stops? How public spaces as roads and squares are used? The student will develop a device that will count the number of passengers waiting for a bus/metro/train/plane in a specific bus/station stop or airport or in open spaces as roads and squares. This device will collect data and, to improve accuracy, the data will be processed using machine learning or deep learning algorithms.
An IoT development kit that will collect information about devices in its vicinity will be provided to process it to find out metrics related to waiting passengers in bus stops. The result can be shown on a very simple dashboard.
Required skills Preferably, Python, some knowledge of REST and MQTT could be beneficial. Data analysis and Visualisation.
Deadline 05/11/2024
PROPONI LA TUA CANDIDATURA