Invited Seminar Talk

A Study of Human-Mobility Leveraging Shared-Mobility Data

Pietro Rampazzo, Technical University of Denmark
Laboratory of Digital and Computational Demography, May 31, 2022

Abstract

There is a gap in the study of mobility. The work done so far is not taking into account the changes the shared-mobility is bringing into our society. This research project aims to leverage shared-mobility services' data (i.e, Movi and Kørmit) for a better understanding of new patterns in the human-mobility. These new services allow people to use a shared vehicle based on their needs, without the necessity to own one. Shared mobility is going towards users' needs and letting them reach their destination as close as possible. Sharing mobility is improving the data collected and at the same time reshaping the commuting patterns. Understanding travel behaviour is key to creating more resilient, sustainable urban transport networks and reducing carbon emissions.

In this research, Pietro Rampazzo started analysing data from Movi and Kørmit. The Movi data focus on Padova, while the Kørmit data on Copenhagen Metropolitan Area. Movi (ex Mobike) is a free-floating bike sharing system active in Italy and Spain. Kørmit is a scooter service active in Denmark, built on top of an existing one, which aims to solve the "last-mile'' problem for commuters. Kørmit can be used to reach the workplace from the closest train station or the other way around. The data collected by the two services is very detailed and rich. The data sets contain high-level detailed information that is related to service usage. For every trip made it is known: (1) anonymized user id and rental plan, (2) vehicle id, (3) origin (latitude, longitude), (4) destination (latitude, longitude), (5) start date and time (timestamp), (6) end date and time (timestamp), and (7) rounded meters/kilometres travelled. All the information is anonymized. Kørmit has asked all the users before signing up to the service to fill in a survey where many demographic information are collected (i.e., age, sex, civil status). In this presentation, he provided an overview of the literature and preliminary results from the two datasets. 

About

Pietro is a master student enrolled in the Human-Centered Artificial Intelligence program at Technical University of Denmark (DTU) in Copenhagen, Denmark. Since May 2019 he has been working on Shared-mobility data to study mobility by applying data science methods and machine learning techniques. He has been working on Movi (ex Mobike) data related to the city of Padova, and more recently on usage data from Kørmit, a scooter service run by the Danish State Railways (DSB) for his master thesis. Pietro holds an undergraduate degree in Computer Science from Ca' Foscari University of Venice. 

Website: peterampazzo.com
Twitter: @peterampazzo