VELO-RL - Improved traffic light control and optimization of bicycle traffic through reinforcement learning

When implementing traffic planning measures in urban areas, the focus is predominantly on the operational requirements of motorized private transport (MIT) and local public transport (LPT), and cycling is often given insufficient consideration. A central problem here is that cycling is not taken into account in most traffic-dependent traffic light systems (LSA) control systems, while the coordination is primarily geared towards motorized traffic. The aim of the project is therefore to develop a control system based on reinforcement learning (RL) that accelerates cycling and at the same time takes into account the needs of other modes of transport. For this purpose, a fictitious traffic network with several signalized junctions is created in the open-source simulation environment SUMO. In addition to motorized private transport, local public transport and vulnerable road users (VRU) are also modeled.

You can find more details on the project page.

The research project (FE 88.0240/2025/AE03) is funded by the Federal Highway Research Institute on behalf of the Federal Ministry of Transport as part of the 3rd funding call of the "Road Innovation Program" funding guideline with the focus "Green Light for Cycling - Acceleration of Cycling on Routes or at Intersections". Responsibility for the content lies solely with the author.