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A New Tool for Transforming Urban Transportation Systems Without Building New Roads

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!

Design steps applied to a single block.

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New Paper on Understanding Complexity of Urban Traffic Prediction

A new paper titled “Enhancing Deep Learning-Based City-Wide Traffic Prediction Pipelines Through Complexity Analysis” was published in the journal Data Science for Transportation.

Our research introduces a novel metric that allows for the pre-modelling complexity evaluation of traffic prediction tasks. This metric, designed for computational efficiency and to be architecture-agnostic, assists in choosing the most suitable model for specific traffic scenarios. It advocates a shift from the traditional “bigger-is-better” approach in deep learning-based traffic prediction to a “just-enough” complexity model, optimizing both performance and resource utilization.

The findings enable a refined approach to modelling traffic prediction tasks, such as deciding between multiple chained predictions and a single-shot prediction. Furthermore, it allows for the segmentation of urban areas into regions of varying complexity, paving the way for deploying ensembles of models that better address diverse urban dynamics.

For more details on this transformative approach and its potential impacts on urban traffic management, check out our paper!

The same model, when trained on two datasets with differing intrinsic traffic complexities, achieves varying levels of effective model complexity during training.

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Spatial Nudging framework presented at COSIT 2024 conference

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!

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Ayda Grisiute presented at CRBAM 2024

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!

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New paper in Computers, Environment and Urban Systems: introducing VeloNEMO ontology to harmonize bike network evaluations

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!

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New paper in Sustainable Cities and Society: socioeconomic explanations of global urban resilience

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!

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New paper in Applied Energy: V2G4Carsharing – A simulation study for 2030

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.

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New Paper in Transportation Research Part C: Quantifying the Dynamic Predictability of Train Delays

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.

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New paper in the Journal of Big Data: Where you go is who you are

Location data is extremely sensitive, revealing where we spend how much time, what we like to do in our free time, and how we spend our money. Our new study titled “Where you go is who you are – A study on machine learning based semantic privacy attacks” sets out to answer a critical question: Just how much “semantic information” – that is, detailed and potentially sensitive insights into personal behavior – can be extracted from someone’s location data using machine learning techniques, especially when this data might be inaccurate or incomplete?

To address this question, we conducted a study using a dataset of location visits from a social network, Foursquare, where the category of the location (e.g., “dining” / “sports” / “nightlife”) is known. We simulate the following scenario: An attacker only knows the geographic coordinates and time of a location visit, and trains a machine learning model to infer the place category. Crucially, the geographic coordiates can be imprecise due to GPS inaccuracies or protection measures. This is simulated by obfuscating the true geographic coordinates with varying radius.

Our experiments reveal a significant privacy risk even with imprecise location data. Although the accuracy of the attacker decreases exponentially with the obfuscation radius, there remains a high privacy loss even if the coordinates are perturbed by up to 200m. A certain risk remains from the temporal information alone. These findings highlight the privacy risks associated with the growing databases of tracking and spatial context data, urging policy-makers towards stricter regulations.

Check out our paper and source code!