- Student: Gil Salvans Torras
- Course: i3 Project
- Summer Semester 2021
Universität Salzburg
Throughout the past years, the public transport sector has experienced significant technological improvements in several aspects. From infrastructural changes, management and planning improvements or even technological changes on the user side (e.g. Apps, monitoring…). In this sense, the mobility sector has been revolutionized and keeps evolving towards a smart mobility paradigm. Following this line, this research focuses on the implementation of Artificial Intelligence in mobility. Furthermore, it aims to build a data pipeline to develop a neural network using public transport data in Salzburg. More specifically, this research focuses on the predicting delay patterns per regional bus station within the entire city. In other words, to predict what will be the delay magnitude per station in the next hour. To do so, historic time-series data from the public transport and related relevant data sources will be retrieved, stored, processed and analysed through machine/deep learning techniques. In agreement with this, the predictive analysis will be carried out by a neural network, which will be trained and deployed accordingly. From the client side, there will be a dashboard web application running and universally accessible over the internet through any device with the predicted results. Therefore, with such a predictive tool further implemented, the users will be able to use this output to improve their mobility needs in their daily lives. From the mobility planning and management side, the SVV (Salzburger Verkehrsverbund) will also take into account these hourly-predicted results for further and future decision-making.
Visit the sample AI4Mob dashboard web application under http://ai4mob.researchstudio.at
