We develop intelligent tools and platforms that help cities, researchers, and transportation agencies understand, predict, and optimize urban mobility — from signal control to travel behavior.
From data collection to decision-making — our tools cover the full spectrum of modern transportation challenges.
Smartphone applications that passively collect travel diaries, trip chains, and mode choices using GPS and motion sensors — giving researchers rich, longitudinal mobility datasets without participant burden.
Design, deploy, and manage multi-language travel behavior surveys with built-in stated preference experiments, participant incentives, and real-time response monitoring — all integrated with mobility data streams.
Reinforcement learning and deep learning models that optimize traffic signal timing in real time, reducing congestion, emissions, and delay across urban corridors — adaptive to demand fluctuations and incidents.
Integrating GPS traces, loop detectors, floating car data, transit feeds, and survey responses into unified analytics pipelines — enabling comprehensive urban traffic modeling and evidence-based policy decisions.
Active research projects pushing the boundaries of mobility intelligence.
Measuring the Value of Travel Time in the era of automated and emerging mobility. VOTAVI combines stated preference surveys with passive smartphone-based mobility data collection to advance behavioral modeling and inform transport policy.
A collaboration between TUM Chair of Transportation Systems Engineering and LISER.
View VOTAVI Project →