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Leveraging Deep Learning and Multi-agent Reinforcement Learning for Traffic Forecasting and Intelligent Traffic Light Optimization
Antwerpen | More than two weeks ago
Harnessing the Power of Deep Learning for Next-Generation Urban Traffic Management
Traffic
state predictions can be used for proactively managing the likely traffic in
the near future to avoid congestion and unsafe situations as well as lower
emissions. For effective traffic management and governance, accurate traffic
predictions are essential as they give access to valuable information such as
the propagation of congestion. In this PhD, we will examine how these
predictions can be used to adapt timings on smart traffic lights. Next to motorized traffic, cyclists and
pedestrians will be able to move more smoothly at intelligent
intersections. Furthermore, a smoother
flow of traffic and a reduction in unnecessary stops and accelerations of cars
and trucks will have a positive impact on the environment, aiding the general
plans of sustainable urban mobility with the aim to give a higher benefit to
society. Data-driven techniques will be investigated and combined with
multi-agent reinforcement learning to learn the relation between roads helping
the traffic lights to decide action. This results in alternative management
strategies based on various scenarios such as extending the green signal phase
for specific traffic flows that are causing or may be impacted by future
traffic congestion. These approaches will be assessed/benchmarked in terms of
accuracy, reliability of the outcomes and the computational load (uncertainty
analysis/sensitivity analysis of the model scalability) and combined in a data
and resource efficient hybrid methodology for multi-modal forecasting in
large-scale road networks.
Required background: Engineering Technology, Engineering Science, Computer Science or equivalent
Type of work: 70% modeling/simulation, 20% experimental, 10% literature
Supervisor: Siegfried Mercelis
Co-supervisor: Joris Finck
Daily advisor: Ynte Vanderhoydonc, Eridona Selita
The reference code for this position is 2024-157. Mention this reference code on your application form.