Facilitating sustainable development of sharing micro-mobility and transit multimodal transport systems (eFAST)

Transformation towards transport-effective society requires new transport mode integration, urban planning at the system level and travel behavior changes.

This project leverages big data analysis, machine learning, and massive usage data from shared micro-mobility for quantitative results on sustainability impacts.

The project will reveal relationships between shared micro-mobility services and transit networks (as well as other transport modes) under different contexts, and the determining factors on sustainability with data-driven methods.

The results, in combination with user behavior modeling, will be used for developing joint planning and optimization methods to integrate micro-mobility and electric transit networks to maximize sustainable impacts.

The project targets system impacts that go beyond existing qualitative and coarse studies and focuses on generating high resolution quantified scientific evidence, as well as planning and optimization tools to support decision-making.