Smarter Urban Dynamics - Denoising Infrastrcucture Data for Intelligent Transport

The analysis of public transportation data is receiving an increasing amount of attention from the research community in the past few years. This interest is fueled by the widespread installation and open access to a variety of sensor technologies for collecting data on the state of the transport system in many cities around the world.

Different cities provide different data sources and in many cases the only common dataset is represented by GPS data of the vehicle fleet. Very often, the data contain erroneous or missing information that should be corrected before proceeding with their analysis.

In this project, we develop a methodology to de-noise scheduled bus stops and detect time schedule information using GPS AVL data. The methodology performs different sequential steps: i) cleaning process and detection of trips; ii) bus stop extraction; ii) bus stop clustering; iv) feature extraction; v) classification model construction and application.

Discrimination between bus stop and traffic light stop due to feature of the set (shape)