Malte Rothhämel
Malte Rothhämel graduated from Technical University in Dresden, Germany, as Diplom-Ingenieur. He undertook an industrial PhD in steering feel and active steering in heavy trucks at Scania and Royal Institute of Technology (KTH), Sweden. After being sent as a guest researcher to Volkswagen Global Research in Wolfsburg, Germany, working on fail-operation chassis concepts for automated heavy vehicles, Malte spent 3.5 years in the development of active steering and back-up steering systems for heavy trucks as well as braking and steering system integration in automated heavy trucks at Scania.
In 2020 he joined KTH in Stockholm as Assistant Professor in Vehicle System Technology, widening the focus including also human powered vehicles. In February 2026 he became Associate Professor.
Sesión
The study proposes a novel approach based on Mixed-Integer Programming (MIP) to
derive a representative driving cycle for bicycles using statistical data. The model
provides a flexible tool for generating driving cycles based on relatively simple data-sets.
It can be used both as a way of setting performance standards for bicycles and related
infrastructure, and as a validation benchmark in the development of other methods.
The approach leverages the strength of Mixed Integer Programming to generate driving
cycles which adhere closely to the original dataset on key features which can be selected
by the user.
To introduce the model and the MIP framework, we provide a case study based on data
from the Mon Reso Vélo mobile application from the city of Montréal. A driving cycle
is optimized to fit the data set, based on available features such as velocity distribution,
stops in-between, and acceleration profiles, as well as the requirement of the driving
cycle being continuous.
The method can easily be applied to other datasets containing similar meta-data, as
well as be extended in order to generate a highly specific driving cycle tailored to cities
with unique infrastructure or elevation profiles. Overall, this study makes a significant
contribution towards quantifying and optimizing bicycle energy efficiency, advancing
sustainable urban mobility.