Henry Martin

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Martin

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Henry

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0000-0002-0456-8539

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Publications 1 - 10 of 46

Influence of tracking duration on the privacy of individual mobility graphs
Wiedemann, Nina; Martin, Henry; Suel, Esra; et al. (2023)
Journal of Location Based Services
Location graphs, compact representations of human mobility without geocoordinates, can be used to personalise location-based services. While they are more privacy-preserving than raw tracking data, it was shown that they still hold a considerable risk for users to be re-identified solely by the graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider a scenario where the attacker wants to match the new tracking data of a user to a pool of previously recorded mobility profiles, and we analyse the dependence of the re-identification performance on the tracking duration. We find that the re-identification accuracy varies between 0.41% and 20.97% and is affected by both the pool duration and the test-user tracking duration, it is greater if both have the same duration, and it is not significantly affected by socio-demographics such as age or gender, but can to some extent be explained by different mobility and graph features. Overall, the influence of tracking duration on user privacy has clear implications for data collection and storage strategies. We advise data collectors to limit the tracking duration or to reset user IDs regularly when storing long-term tracking data.
Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
Eichenberger, Christian; Neun, Moritz; Martin, Henry; et al. (2022)
Proceedings of Machine Learning Research ~ Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 10(12) GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input.
Visual Analysis of Spatio-temporal Phenomena with 1D Projections
Franke, Max; Martin, Henry; Koch, Steffen; et al. (2021)
Computer Graphics Forum
It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users’ foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.
Analyzing performance in Orienteering from movement trajectories and contextual information
Sailer, Christian; Martin, Henry; Gaia, Luca; et al. (2019)
Adjunct Proceedings of the 15th International Conference on Location Based Services (LBS 2019
This paper presents a framework to automatically analyze the performance of elite orienteers under the consideration of the slope and of a wide-range of vegetation types. We test our approach using data of four different competitions of the European Orienteering Championships 2018. Two use cases of the framework are presented: first, the analysis of the speed and slope on a competition level and second, the analysis of the speed as a function of the vegetation type either on a competition level or on an individual athlete level. The presented framework can be used efficiently across multiple data sets and by coaches and athletes to develop new strategies for training or competitions.
Bike network planning in limited urban space
Wiedemann, Nina; Nöbel, Christian; Ballo, Lukas; et al. (2025)
Transportation Research Part B: Methodological
The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, efficient, and health-promoting transportation modes. Bike network planning is a tedious process, relying on heuristic computational methods that frequently overlook the broader implications of introducing new cycling infrastructure, in particular the necessity to repurpose car lanes. In this work, we call for optimizing the trade-off between bike and car networks, effectively pushing for Pareto optimality. This shift in perspective gives rise to a novel linear programming formulation towards optimal bike network allocation. Our experiments, conducted using both real-world and synthetic data, testify the effectiveness and superiority of this optimization approach compared to heuristic methods. In particular, the framework provides stakeholders with a range of lane reallocation scenarios, illustrating potential bike network enhancements and their implications for car infrastructure. Crucially, our approach is adaptable to various bikeability and car accessibility evaluation criteria, making our tool a highly flexible and scalable resource for urban planning. This paper presents an advanced decision-support framework that can significantly aid urban planners in making informed decisions on cycling infrastructure development.
Trackintel: An open-source Python library for human mobility analysis
Martin, Henry; Hong, Ye; Wiedemann, Nina; et al. (2023)
Computers, Environment and Urban Systems
Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-source Python library for human mobility analysis. Trackintel is built on a standard data model for human mobility used in transport planning that is compatible with different types of tracking data. We introduce the main functionalities of the library that covers the full life-cycle of human mobility analysis, including processing steps according to the conceptual data model, read and write interfaces, as well as analysis functions (e.g., data quality assessment, travel mode prediction, and location labeling). We showcase the effectiveness of the Trackintel library through a case study with four different tracking datasets. Trackintel can serve as an essential tool to standardize mobility data analysis and increase the transparency and comparability of novel research on human mobility. The library is available open-source at https://github.com/mie-lab/trackintel.
Graph-ResNets for short-term traffic forecasts in almost unknown cities
Martin, Henry; Bucher, Dominik; Hong, Ye; et al. (2020)
Proceedings of Machine Learning Research ~ Proceedings of Machine Learning Research, NeurIPS 2019 Competition and Demonstration Track, 8-14 December 2019, Vancouver, CA
The 2019 IARAI traffic4cast competition is a traffic forecasting problem based on traffic data from three cities that are encoded as images. We developed a ResNet-inspired graph convolutional neural network (GCN) approach that uses street network-based subgraphs of the image lattice graphs as a prior. We train the Graph-ResNet together with GCN and convolutional neural network (CNN) benchmark models on Moscow traffic data and use them to first predict the traffic in Moscow and then to predict the traffic in Berlin and Istanbul. The results suggest that the graph-based models have superior generalization properties than CNN-based models for this application. We argue that in contrast to purely image-based approaches, formulating the prediction problem on a graph allows the neural network to learn properties given by the underlying street network. This facilitates the transfer of a learned network to predict the traffic status at unknown locations.
Graph Convolutional Neural Networks for Human Activity Purpose Imputation
Martin, Henry; Bucher, Dominik; Suel, Esra; et al. (2018)
NIPS 2018 Spatiotemporal Workshop
Automatic location tracking of people has recently become a viable source for mobility and movement data. Such data are used in a wide range of applications, from city and transport planning to individual recommendations and schedule optimization. For many of these uses, it is of high interest to know why a person visited at a given location at a certain point in time. We use multiple personalized graphs to model human mobility behavior and to embed a large variety of spatio-temporal information and structure in the graphs' weights and connections. Taking these graphs as input for graph convolutional neural networks (GCNs) allows us to build models that can exploit the structural information inherent in human mobility. We use GPS travel survey data to build person specific mobility graphs and use GCNs to predict the purpose of a user's visit at a certain location. Our results show that GCNs are suitable to exploit the structure embedded in the mobility graphs.
Conserved quantities in human mobility
Hong, Ye; Martin, Henry; Xin, Yanan; et al. (2022)
Arbeitsberichte Verkehrs- und Raumplanung
Quantifying intra-person variability in travel choices is essential for the comprehension of activity-travel behaviour. Due to a lack of appropriate datasets and methods, there is limited understanding of how an individual’s travel pattern evolves over months and years. We use two high-resolution user-labelled datasets consisting of billions of GPS track points from ∼ 3800 individuals to analyze individuals’ activity-travel behaviour over the long term. The general movement patterns of the considered population are characterised using mobility indicators. Despite the differences in the mobility patterns, we find that individuals from both datasets maintain a conserved quantity in the number of essential travel mode and activity location combinations over time, resulting from a balance between exploring new choice combinations and exploiting existing options. A typical individual maintains ∼ 15 mode-location combinations, of which ∼ 7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well modelled using a power-law fit that slows down over time. Our findings enrich the understanding of the long-term intra-person variability in activity-travel behaviour and open new possibilities for designing mobility simulation models.
How do you go where?: improving next location prediction by learning travel mode information using transformers
Hong, Ye; Martin, Henry; Raubal, Martin (2022)
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options. Here, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes, which are behaviour dimensions often overlooked in previous work. In particular, the prediction of the next travel mode is designed as an auxiliary task to help guide the network's learning. For evaluation, we apply this approach to two large-scale and long-term GPS tracking datasets involving more than 600 individuals. Our experiments show that the proposed method significantly outperforms other state-of-the-art next location prediction methods by a large margin (8.05% and 5.60% relative increase in F1-score for the two datasets, respectively). We conduct an extensive ablation study that quantifies the influence of considering temporal features, travel mode information, and the auxiliary task on the prediction results. Moreover, we experimentally determine the performance upper bound when including the next mode prediction in our model. Finally, our analysis indicates that the performance of location prediction varies significantly with the chosen next travel mode by the individual. These results show potential for a more systematic consideration of additional dimensions of travel behaviour in human mobility prediction tasks. The source code of our model and experiments is available at https://github.com/mie-lab/location-mode-prediction.

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