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Urban bike flow prediction: traditional methods vs. explainable machine learning

Grant number: 22/06328-7
Support Opportunities:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): August 23, 2022
Effective date (End): December 22, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal Investigator:Fabio Kon
Grantee:Eduardo Bobrow Falbel
Supervisor: Kay Axhausen
Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Research place: Swiss Federal Institute of Technology Zurich, Switzerland  
Associated to the scholarship:21/09285-4 - Urban cycling flow prediction using machine learning, BP.IC

Abstract

The push towards bicycle use is cities is a part of public policies which aim to promote the use of non-polluting means of transport that contribute to the population's overall health by inhibiting sedentary lifestyles. Deciding where to build cycling infrastructure is crucial to the viability of such policies. In this project we will evaluate data on bicycle trips in cities with public bike-sharing systems (BSSs). We shall utilize machine learning models in order to infer flows from socioeconomic, Point of Interest (POI) and topology (among other kinds of) data. Our goal is to utilize transfer-learning techniques in order to fit these models for use in systems without already established BSSs and, subsequently, allow for better planning regarding the placement of the stations for such systems and the construction of new cycling infrastructure. (AU)

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