Research Projects

Autonomous Shuttles On-Campus

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.
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2017-2018: PRISM is about designing, implementing and testing methodologies to better predict transport demand in a city. While plenty solutions exist today for this objective, there is general consensus that, under stress scenarios (e.g. large social events, inclement weather, demonstrations, special days), those approaches are insufficient. For example, a one-way car sharing (e.g. DriveNow) or an autonomous mobility on demand service, are highly sensitive to rebalancing operations (moving vehicles to where demand is expected).
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Traffic-flow & Air Quality Experiment

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.
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Tripod (@MIT)

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.
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Extraordinary Queuing detection in Denmark

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.
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Projects under the TINV3 – Transportens Innovationsnetværk

2014-2017: The TINV3 – Transportens Innovationsnetværk (Transport Innovation Network) helps to strengthen the Danish transport and logistics industry’s competitiveness and technological leadership. This research initiative financed a series of research projects covering topics such as:

  • 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.

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Evaluation of Floating Car Data for the Danish Road Directorate

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.
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