Incident-aware Resilient Traffic Management for Urban Road Networks (InTURN)

• Applicant: Professor Dr.-Ing. Sven Tomforde
• Subject Area: Traffic and Transport Systems, Intelligent and Automated Traffic ; Computer Architecture, Embedded and Massively Parallel Systems
• Term: from 2019 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 420542957

Imagine that you are passing an urban traffic network as fast and reliable as possible while simultaneously avoiding all congestions and disturbances. Imagine also that you are doing this without continuously and ubiquitously sending location and context information to large data-driven enterprises evaporating all your personal privacy information. In this proposal, we will investigate how such a system can be realised as a self-adaptive and self-organising (SASO) system. Urban traffic is a challenging testbed for SASO systems: Massive traffic volumes in combination with the underlying dynamics and time-variant behaviour as well as disturbances such as incidents and negative environmental effects demand for novel integrated control and management strategies. Within the last decade, several approaches for traffic light control, progressive signal systems, and route guidance have been presented, including our own preliminary work: Organic Traffic Control (OTC). Existing systems, however, are typically limited to only reacting to observed traffic conditions and they do not consider disturbances such as incidents (e.g. car accidents, construction work, or un/loading of lorries). In this proposal, we want to overcome these limitations by means of intelligent mechanisms to increase the resilience of traffic control and management solutions. We detect abnormal traffic conditions and identify incidents using machine learning approaches. These incidents are automatically classified by means of their estimated duration, their anticipated severity, and the expected influence on other intersections and road elements. We further take advantage of the interconnected character of traffic control by developing techniques for a cooperative validation of detected incidents – which also allows for detecting disturbed sensors. The incident classification is subject to an autonomous learning mechanism that self-improves and refines the decisions at runtime. In order to finally take advantage of the determined incident information, we investigate an integrated and robust traffic management system based on OTC that 1) adapts and self-improves the traffic light signalisation strategy, 2) establishes and maintains traffic-response progressive signal systems, and 3) dynamically guides drivers through the underlying road network. The result will outperform existing solutions in terms of travel times, number of stops in front of red traffic lights, and emission reductions.

• DFG Programme: Research Grants
• Co-Investigator: Professor Dr. Bernhard Sick