Research

Bike2Green is a EU funded project led by KTH in collaboration with FB Innovation Soceita A Responsabilita Limitata Semplificata, Italy and European Cyclists Federation ASBL, Belgium.

ENCom is a knowledge and networking platform for collaborative energy research.

The project investigates workplace choice of individuals living in Stockholm while considering their time and space constraints throughout the day.

Project aims to present novel applications for data collected from moving camera image sequences in transportation.

The purpose of this project is to understand the undergoing process behind human spatiotemporal decisions in performing daily trips by re-estimating an agent-based model (Scaper) using mobile Phone Data in Stockholm in 2019 and 2020.

A machine learning approach that investigates how neural networks' ability to learn non-linear phenomena efficiently can provide increased performance in a four-step model.

This project extends previous work on the division’s collaboratively built scheduling model, Scaper, by making the model sensitive to activity duration. This work will improve the model’s power as a decision support tool, especially around policy questions impacted by the timing of people’s activities and travel.

The aim of this research project is to support urban design using street-view image sequences collected with a moving camera to generate pedestrian count data and heat maps.