Category: news

Our paper “GeOT: A spatially explicit framework for evaluating spatio-temporal predictions”, is now published in the International Journal of Geographical Information Science (IJGIS)!

Motivation: While the GIS mantra “spatial is special” is widely acknowledged, it has not made its way into how we evaluate spatio-temporal predictions. Standard error metrics like MSE or MAE are typically averaged over locations, ignore the spatial structure of the data.

Contribution: To close this gap, we propose to leverage Optimal Transport (OT), a theoretical framework to compare two distributions. With OT, we can measure the difference between the true and predicted spatial distribution by computing the minimal transportation costs to align them.

Results: We show that our GeOT framework 1) effectively measures the spatial quality of predictions, 2) relates to spatial autocorrelation, and 3) provides interpretable results. On top of that, this approach is not just a new spatial evaluation metric – it can also be integrated as a loss function in machine learning tasks.

Check out our paper and code base with tutorials.

Our new paper, titled “Context-Aware Knowledge Graph Framework for Traffic Speed Forecasting Using Graph Neural Networks,” has been accepted for publication in IEEE Transactions on Intelligent Transportation Systems.

This paper introduces a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Using a relation-dependent integration strategy, the framework generates context-aware representations that capture the intricate spatio-temporal dependencies of urban environments.

Building on this foundation, a CKG-GNN model is developed, integrating the CKG, dual-view multi-head self-attention mechanisms, and graph neural networks (GNNs). This integration not only significantly improves predictive accuracy but also underscores the importance of contextual information in forecasting traffic dynamics.

By bridging domain knowledge with graph neural architectures, the proposed approach demonstrates its potential for advancing intelligent transportation systems.

Please check our paper and codes.

We’re exploring how people perceive cycling routes and what shapes their cycling experience.

What will you do?
• Watch short cycling videos.
• Answer brief questionnaires about your experience.

Who can join?
18 years or older.
Moderate proficiency in English.
Normal eyesight (contact lenses allowed).

Why participate?
• Contribute to research that improves cycling experiences.
• Your responses will remain completely anonymous.

Ready to roll?
Then scan the QR code or click on the link to the survey: https://lnkd.in/eK_q62uF

Our paper Bike Network Planning in Limited Urban Space was accepted for publication in Transportation Research Part B: Methodological!

This paper was part of the e-bike city project, a multidisciplinary initiative at the department exploring the effects of a radically changed urban road space with priority on cycling. Planning a suitable bike network is a core challenge in this project. In our paper, we introduce a novel optimization method for placing new bike lanes with minimal impact on other travel modes.

Our approach leverages the concept of Pareto-optimality: When car lanes are repurposed as bike lanes, car travel times inevitably increase. The key question is: By how much? Pareto-optimal solutions are the street networks that present the best trade-off between car accessibility and bikeability. To quantify bikeability, we introduce the concept of “perceived bike travel time”, based on research showing that cyclists perceive dedicated bike lanes as faster. Our experiments show that the optimization approach outperforms other methods by proposing networks that have both lower car travel times and lower perceived bike travel times.

Check out our paper or get in touch if you are interested to know more!

Further resources:

• Open-source code
• Web app for bike network planning (beta)

A new paper titled “Designing an E-Bike City: An automated process for network-wide multimodal road space reallocation” was published in the Journal of Cycling and Micromobility Research.

How can we add transport capacity for rapidly growing urban populations? How can urban transportation systems be transformed for lower emissions within the short time available? In dense cities, adding transport infrastructure is expensive, takes many years of construction, and faces restrictions by historical buildings. Our research proposes a faster and cheaper approach: We have developed an automated tool that redesigns urban transportation systems only by reorganizing lanes on existing roads.

The SNMan – Street Network Manipulator is a new open-source software that allows researchers and planners to generate alternative transportation networks within existing road space. It can handle any scale, from individual blocks to entire cities. Depending on the goals set, it can add bike lanes, bus lanes, or green spaces, while reorganizing the rest so that buildings remain accessible by car and public transit remains functional. For example, Zurich’s roads could allocate 4x more space for cycling infrastructure while still satisfying these conditions.

Discover more in the recently published open-access paper!

Urban resilience refers to the ability of urban systems to adapt and recover from external shocks, such as the recent global pandemic. Our new paper, titled “Leveraging Context-Adjusted Nighttime Light Data for Socioeconomic Explanations of Global Urban Resilience,” provides a comprehensive analysis of urban responses to COVID-19.

Using context-adjusted nighttime light data, our study models urban resilience during the pandemic, identifying five diverse patterns characterized by phases of downturn, minimum impact, and recovery. We also uncover significant correlations between socioeconomic factors and urban resilience, revealing that stringent measures may reduce resilience, while proactive health and containment strategies can enhance it. Furthermore, we delve into strategic socioeconomic interventions to enhance urban resilience using counterfactual explanations.

For more details, check out our paper!