This collaborative project with Huawei MRC research group in TUM Munich is about designing and developing a framework to fuse physics knowledge in the design of the large-​scale system optimization by means of machine learning, control theory, and simulation in order to achieve antifragile behavior at scale. 

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The design idea is to reallocate 50% of the existing urban road space to e-bikes and to assess what the change could achieve in terms of accessibility, generalized costs of travel, changes in daily life and reductions in CO2 emissions.

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Rising traffic numbers and increasingly scarce traffic areas mean that the traffic of the future must become safer and more efficient. What do automated vehicles mean for special infrastructures such as road tunnels in the Swiss national road network? Will this have an impact on safety risks? Will there be new opportunities, and to what extent will technical and operational operations have to adapt to the new situation? Pilot/test routes are needed to enable automated driving. An important prerequisite for initial test applications under real traffic conditions is the identification of ideal routes.

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Novel sensor technology represents a significant potential for traffic management in cities. Thermal cameras in particular have recently gained considerable importance and are now also to be used in the Traffic Management Department (DAV) of the City of Zurich. In this context, the determination of accurate travel times plays a central role, which is to be carried out much more precisely by means of thermal cameras than with the previously used sensor technology. Nevertheless, an efficient application will require a scientific investigation of the measurement accuracy, the required sensor density in the study area as well as the sensor positioning. In order to answer these open questions, this research project focuses on the determination of travel times.

The raw data provided by the sensors is examined, travel times derived and compared with the evaluation software provided for the cameras and a comparative data set (GoogleTraffic, TomTom). In this way, it is investigated for the DAV how accurately travel times can be measured with the current sensor density and positioning. In addition, the potential for improved traffic management through thermal imaging cameras is shown.

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Related publications: An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation.

JRC AUTOTRAC 2020 is the first autonomous vehicle traffic competition. Differently from other competitions involving small-scale automated vehicles (AVs), AUTOTRAC is about vehicles’ cooperation behavior rather than their individual capacity to complete a certain path.

The final event of the competition was held in the form of an online event on 17 June 2021 as part of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems.

Related publications: Robotic Competitions to Design Future Transport Systems: The Case of JRC AUTOTRAC 2020

JRC organizes experimental campaigns for the assessment of advanced vehicle technologies, currently available in commercial vehicles and considered a proxy for the first Automated Vehicles in the near future. 

The Joint Research Centre (JRC) is investigating the impacts of CAV technologies through a combined approach based on traffic modelling and simulation, desk research and stakeholders’ consultation.

The project investigated the applicability and market potential of blockchain technology for asset sharing in the road transport sector. The project comprised two principal activities. The first activity was market research and analysis to support the development of a new service concept and business model for blockchain-powered shared mobility. The second activity was technology prototyping to demonstrate the technical feasibility of the novel service concept using state of the art blockchain and IoT frameworks.

Application of Information-based risk analysis using container movement data to control containerized cargo, target high-risk consignments and proceed with costly physical checks, only where needed.