2017-2020: In 2019, autonomous shuttles will start operating in DTU Lyngby campus as part of LINC: the largest test of autonomous shuttles in Denmark. The goal of our project is to dynamically predict where and when passengers would like to use the shuttles for traveling on-campus using a variety of machine learning methods.
2017-2018: The experimental project sought to find correlations between traffic management, traffic flow and air quality by measuring the difference between the air pollution levels when cars are waiting for the traffic light to turn green compared to when they are driving through the intersection.
The project succeeded in finding as reliable correlations for NO2 and CO as can be achieved in real-life data gathering.
2016-2018: Tripod is a system that incentivizes travelers to pursue specific routes, modes of travel, departure times, ride sharing, trip making, and driving styles in order to reduce energy use. Tripod relies on an app-based travel incentive tool designed to influence users’ travel choices by offering them real-time information and rewards.
2016-2017: This project helped the Danish Road Directorate in identifying extraordinary queueing situation in the main roads of Denmark using probe-vehicle data provided by INRIX. The approach consists of anomaly detection algorithms that were optimized based on data provided by the DRD. The developed methodology is currently used in production in the DRD’s traffic tower to constantly monitor the conditions of the Danish road network and detect extraordinary queueing situations.
Prediction intervals for bus arrival times using quantile regression method and deep learning.
Multi-modal visualization for extracting mobility insights.
Anticipating mobility disruptions caused by special events.
2017: In 2017, the Danish Road Directorate (DRD) considered using a Floating Car Data product, which can observe and predict traffic all over Denmark. Our task was to estimate how reliable and effective the product was. We analyzed data from the product, as gathered from vehicle fleets over 61 days in February-May 2017 in 1250 strategically important road segments.