Category: Uncategorized

Our new paper, “Quantifying the Impacts of Non-Recurrent Congestion on Workplace EV Charging Infrastructures”, is now published in Transportation Research Part D: Transport and Environment (Vol. 138, 2025). The study addresses a critical but overlooked issue: how unexpected traffic accidents and congestion ripple into EV workplace charging systems that rely on smart-charging and solar integration.

Motivation: Workplace EV charging is expected to play a major role in absorbing surplus midday solar energy. Smart-charging algorithms are typically optimised using historical travel patterns, but unexpected congestion can cause many EVs to arrive simultaneously once traffic clears. These concurrent arrivals create demand surges, raising operating costs and reducing trust in EV charging infrastructure.

Contribution: We develop a co-simulation framework that couples a scalable traffic simulator (Cell Transmission Model, CTM) with a high-fidelity EV charging simulator (ACN-Sim). The integrated pipeline:

Results & Policy Insights: Using Minneapolis as a case study, we find that non-recurrent congestion can increase Time-Of-Use (TOU) costs by up to 27%. However, access to timely accident information reduces these surprise costs by more than 50%, showing the value of integrating traffic data into charging operations.

Policy recommendations include: Installing slightly higher charging capacity (10–15% above historical demand), deploying faster Level 2 chargers for greater robustness, and co-optimising charging with building loads to reduce surprise costs. Investing in early-warning systems for traffic accidents incorporating and real-time traffic information utility coordination could also significantly reduce the risk propagation from the transpiration layer to the power system.

👉 Check out the open-source code and data on GitHub.

A new paper, titled “Spatial Nudging: Converging Persuasive Technologies, Spatial Design, and Behavioral Theories”, was presented at the 16th International Conference on Spatial Information Theory (COSIT 2024).

This paper introduces the Spatial Nudging framework—a theory-driven approach that maps out nudging strategies in the mobility domain, with a focus on cycling. The framework integrates physical and digital interventions to promote sustainable mobility, drawing from behavioral theories such as Nudge Theory and the Theory of Affordances. Additionally, we propose a graph-based workflow that uses cognitive graphs and the Fine-to-Coarse heuristic to model how cyclists perceive their environment. This method generates cognitive routes that align perceived affordances with the physical environment, closely resembling real-world cycling trajectories.

For more details, check out our paper!

Ayda Grisiute gave a talk at the 8th Cycling Research Board Meeting (CRBAM). The presentation, titled “Conceptualizing Spatial Nudging: A Theoretical Framework for Integrating Interventions to Promote Cycling“ introduced Spatial Nudging framework that delineates nudging practices in the mobility domain and offers a theoretically integrated perspective on promoting cycling through spatial interventions.

In addition, Ayda presented a poster titled “Building a Planning Tool for the E-Bike City Vision,” which showcased a web application created by two geomatics students. The tool helps urban planners reallocate road space for bike lanes using various optimization strategies.

Check out the poster here!

As the ecosystem of transport planning and evaluation metrics, tools, methods, and services grows, there is a pressing need to enhance domain interoperability and interpretability. In our new paper, titled “An ontology-based approach for harmonizing metrics in bike network evaluations“ , we construct a formal ontology, VeloNEMO, a formal ontology designed to capture the key attributes of bike network evaluation metrics and resolve terminological inconsistencies across them. We also introduce a machine-readable knowledge graph that compiles existing metrics, allowing for more efficient meta-analyses of various evaluation strategies. To further enhance transparency, we offer recommendations for making metric descriptions more comparable across different evaluation approaches.

For more details, check out our paper!

What is the potential for integrating vehicle-to-grid with car sharing in the future? As part of the V2G4Carsharing project, we simulated scenarios for 2030 and quantified the benefits in terms of monetary savings and peak shaving effect. In our case study done in collaboration with the swiss car sharing provider Mobility, we found that Mobility could offer flexibilities between 12 to 50 MW, dependent on the scenario. There is a sweet spot where both car sharing and power grid operators benefit.

Our paper titled Vehicle-to-grid for car sharing – A simulation study for 2030 was now published in Applied Energy! Check out the paper here.

Get in touch if you are interested to learn more, or checkout our project code base and the car sharing simulator.

Our paper on “Quantifying the dynamic predictability of train delay with uncertainty-aware neural networks” has been published in Transportation Research Part C!

In light of the importance of accurate delay prediction for transport services and passengers, many predictive methods have been proposed. However, they hardly account for the involved uncertainty and there is a lack of work analysing the dynamic predictability over time. We fill this gap with an uncertainty-aware neural network and a framework for describing the predictability by the prediction horizon. The results on Swiss train delay data show 1) an exponential decay of the predictability by the horizon, 2) a significant portion of (aleatoric) data uncertainty in contrast to model uncertainty, and clear advantages of the NN compared to MC models.

For more details, check out our paper! This work was done in collaboration with the Institute for Trans­port Plan­ning and Sys­tems at ETH Zurich.