BikeZ - Model Suite for Mass Cycling as a Service Simulation
I am leading a project funded by Innosuisse, the Swiss Innovation Agency exploring the bicycle traffic dynamics. Bicycle data are scarce and if we want to realize bicycle-centric future cities, we need quantitative results on the mode's efficiecy and comparative results against prevalent modes like cars.
BikeZ aims to shift the bicycles from a side mode to the primary choice for short and medium distances, exploiting insights from the ETH E-Bike City project. BikeZ envisions a future where cycling infrastructure is significantly expanded. Over 18 months BikeZ will build on outputs from the ETH E-Bike City project and the ETH Mass-Cycling experimental campaign and will; a) conduct 3 additional scenarios with drone experiments to gain in-depth insights into cyclists’ behaviors and decision-making process, b) develop a comprehensive Model Suite with an API for integration in simulation software packages to enable simulation of mass bicycle traffic, c) carry out a case study in Zurich to quantify the impact of mass cycling on congestion, emissions and energy consumption under reallocated infrastructure.
AntifragiCity - Horizon Europe project
I am leading the ETH Zurich side, developing Antifragile traffic control methodoologies in urban road networks.
AntifragiCity is a groundbreaking European research initiative aiming to revolutionize how urban mobility systems respond to disruptions. Rather than merely resisting shocks, AntifragiCity envisions cities that learn, adapt, and grow stronger through challenges - embracing the concept of antifragility.
At ETH Zurich, we lead the effort to bring antifragility into urban traffic control. Traffic systems today are fragile by nature—vulnerable to demand fluctuations, technology-driven changes, and unpredictable events. Our goal is to develop learning-based traffic management algorithms that not only handle disruptions efficiently but also improve system performance over time. This includes:
Modeling diverse stress scenarios using real-world data, designing new evaluation criteria for antifragile traffic operations, developing traffic control strategies that are robust, fair, and scalable, ensuring that AI models remain accurate, unbiased, and resilient even under data uncertainty. By embedding AI, multi-agent systems, and simulation tools into real-time traffic control, our work will lay the foundations for cities that evolve stronger from every disruption.
FEDORA - Horizon Europe project
Fedora brings together 16 partners from 11 countries, including leading universities, research centres, mobility operators, technology providers, and international associations.
I am leading on the ETH Zurich side, developing dynamic pricing and incentivisation models for traffic management applications and Sim2Real techniques for model-to-application transferability
FEDORA aims to pave the way towards advanced traffic and network management through the development of a federated spaces platform offering a holistic framework of innovative solutions and services that enable precise and pro-acting sensing of supply and demand, facilitate optimal operation of transport services, and advances learning and evolution in complex environments. At the operational level, FEDORA offers a collaborative space of data that can realize advanced data alchemy processes using interconnected services and tools, a space of advanced traffic management optimisation services and a multi-modal simulation space to create and assess future mobility scenarios. The approach is validated in six thematic demonstrations in Vienna, the Basque country, Reggio Emilia, Nicosia, Budapest and Denmark, covering varying EU urban and rural contexts, infrastructure maturity levels, multimodal mobility services availability, organisation/operational structures and social conditions. Interaction with existing programmes on roadmapping and recommendations at national, EU and global level will be promoted, allowing a multiplication effect of project’s results.
SiL - Reproducibility in Research - Students-in-the-loop
I am leading a project to promote reproducible research in transportation engineering.
The vision is a transportation community where open science, reproducible research and replicable studies are the norms, advancing scientific rigour, accelerating innovation and fostering translation into practice. RERITE is a global working group of volunteers who are passionate about practicing and promoting Reproducible Research (RR) in Transportation Engineering.
The students-in-the-loop (SiL) project aims to promote reproducible research through student projects. Essentially, students choose peer-reviewed papers that are published and they do not provide a code repository for reproducibility. The students learn and publish an open-access repo so the research can be used as baseline. The paper authors disseminate their work to the public. The research community benefits from another baseline. Win-Win-Win:)
ANTIGONES - Designing anti-fragile large-scale traffic frameworks
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.
E-Bike City
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.
AVTunnels - Impact of AVs on tunnels
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.
OptFlow - Travel-time estimation with FLIR cameras sensors
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.
Related publications: An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation.
JRC AUTOTRAC 2020 - How the future road transport will look like?
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
Towards a Connected, Coordinated and Automated Road Transport (C2ART) system
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.
JRC Proof of Concept Project Ridechain
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.
JRC Container Traffic Monitoring System - ConTraffic
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.