arXiv subjects

Jürgen Hackl

Publications and source records attributed to Jürgen Hackl.

12 recordsLinked to original sources

arXiv2026

Assessing flood-conditioned power dependency of urban rail transit and its effects on resilience under climate change

Urban rail transit (URT) faces increasing flood risk while relying on traction power systems whose failures can trigger cascading disruption. Existing studies on URT flood resilience often oversimplify power dependency by overlooking traction power redundancy and rarely consider flood-conditioned cascading impacts, providing limited insight into how flood-induced traction power failures propagate through operations and affect system resilience. This study marks the first quantitative assessment of flood-conditioned power dependency of URT and its effects on system resilience. The methodology integrates a double-layer URT network model, spatial flood exposure assessment, service disruption simulation, a flood-conditioned power dependency index, and a journey-based performance metric that captures resilience properties of robustness and redundancy. It is applied to the London rail transit network under surface water flood scenarios across current and RCP8.5 climate conditions, two traction power feeding mechanisms, and four substation flood-failure thresholds. Results show that traction power redundancy fundamentally shapes the severity of cascading impacts. Under double-end feeding, power-attributable performance loss remains minimal, whereas single-end feeding leads to substantially greater loss and is highly sensitive to substation failure thresholds. The proposed dependency index is strongly correlated with dependency-related performance loss, supporting its use as a proxy for dependency-induced additional consequences in strategic planning.

physics.soc-ph↗

arXiv2026

Do simulated agents move like real people?

Human mobility is increasingly represented using synthetic populations that offer scalable alternatives when individual-level observations are unavailable or sensitive. Yet validation typically emphasizes aggregate statistics, which can obscure whether simulated agents traverse transportation networks in ways that resemble real travelers. Here, we develop a path-centric framework that combines direct path-level comparisons with higher-order network models to compare observed and simulated mobility on a shared metropolitan road network. Observed and simulated paths share broad statistical regularities and short-range memory. Beyond these similarities, however, simulated mobility underrepresents long paths, exhibits greater redundancy among long route sequences, covers a smaller and partly different portion of the network, and is more predictable overall. These discrepancies show that agreement in aggregate mobility patterns does not imply fidelity in how travelers move through the underlying infrastructure. Higher-order path analysis therefore offers a framework for validating synthetic mobility at the spatial and sequential scales relevant to scientific inference, urban planning, and policy.

cs.CE↗

arXiv2026

EuroFlood: a Python library and queryable index for the CEMS satellite-derived flood-depth archive of Europe

Satellite-derived observations of flood water depth support flood model validation and impact assessment, yet the only open continental-scale archive of such observations, the flood-depth maps of the Copernicus Emergency Management Service, is distributed as several thousand raster files whose sole spatial metadata is a coordinate encoded in each filename. This paper presents EuroFlood, an open-source Python library and a published spatial index that make the archive queryable. The index records which events inundated each grid cell, so discovery, recurrence, and footprint queries are answered from a small fraction of the archive volume, while depth rasters are retrieved on demand. It reproduces the archived event footprints losslessly at its grid, and a completeness audit against an independent flood-impact database shows that most documented floods co-occur with archived events. Demonstrations include continental recurrence mapping, comparison of observed with modelled flood extents, and event-based exposure assessment.

cs.CE↗

arXiv2026

Do Waders, Swimmers, and Divers Exist? A GPS-Based Pilot Study of Site-Dependent Visitor Movement in Theme Parks

Operators of large visitor attractions routinely sort their guests into intuitive behavioral types, from relaxed wanderers to single-minded maximizers, and use this informal typology to guide spatial design and to set the parameters of pedestrian and agent-based simulations. Yet the typology is seldom tested against how people actually move, and it is usually assumed to transfer unchanged between sites. We examine both assumptions with individual-level movement data: volunteers carried GPS trackers through several theme parks operated by different chains and completed a short exit survey, letting us compare what guests do with what they say. Each visit is summarized by a small set of interpretable movement features, and visitors are grouped within each site using a deliberately demanding, multi-criteria validation protocol rather than a single clustering run. The picture that emerges is nuanced. Behavioral groups recur reliably but without sharp boundaries, pointing to a continuum rather than to discrete categories; what people do diverges from how they describe themselves, so self-report is a weak proxy for observed behavior; and, most consequentially, the relationships among movement features reverse from site to site, so behavioral parameters calibrated at a given location cannot be carried elsewhere. A complementary agent-based experiment locates the origin of each group's spatial signature in where visitors choose to go and in what order, rather than in how fast or how directly they walk. The work reframes a familiar industry heuristic as a geographical, site-dependent phenomenon, contributes a reproducible and critically validated pipeline for segmenting movement data, and connects empirical tracking to simulation. Its central message is that human movement behavior must be calibrated in place, not borrowed across contexts.

physics.soc-ph↗

arXiv2026

Subsystem Structure as an Inferential Resource for Coupled Engineered Systems

Engineered infrastructure systems pose inverse problems in which hidden states, unknown parameters, and subsystem couplings must be inferred from sparse and noisy measurements. These problems are difficult because physical subsystems are heterogeneous, sensing is partial, uncertainty is distributed across subsystem interfaces, and computational cost grows rapidly with system size. We address this challenge with probabilistic compositional inference, a graph-based architecture that represents a coupled system as interacting subsystems, each retaining its own local model, estimator, and uncertainty representation, while coupling is handled through physically meaningful stochastic messages exchanged across subsystem interfaces. This formulation allows mechanistic, learned, and deterministic components to coexist within a single inference framework and propagates calibrated uncertainty without assembling a global augmented state or covariance. We validate the framework in three increasingly demanding settings: a sparse-sensing canonical inverse problem, where interface couplings can also be learned from data; infrastructure-scale power networks, where the method matches centralized joint state-and-parameter inference while reducing computational scaling from approximately cubic to approximately linear; and a multi-physics turbine embedded in a power-grid network, where heterogeneous subsystems compose hierarchically without degrading local inference or collapsing local posteriors into a global estimate. Together, these results show that subsystem structure can be exploited as the organizing principle for uncertainty-aware inverse inference in coupled engineered systems.

eess.SY↗

arXiv2025

Digital Twins for Intelligent Intersections: A Literature Review

Intelligent intersections play a pivotal role in urban mobility, demanding innovative solutions such as digital twins to enhance safety and efficiency. This literature review investigates the integration and application of digital twins for intelligent intersections, a critical area within smart urban traffic systems. The review systematically categorizes existing research into five key thematic areas: (i) Digital Twin Architectures and Frameworks; (ii) Data Processing and Simulation Techniques; (iii) Artificial Intelligence and Machine Learning for Adaptive Traffic Control; (iv) Safety and Protection of Vulnerable Road Users; and (v) Scaling from Localized Intersections to Citywide Traffic Networks. Each theme is explored comprehensively, highlighting significant advancements, current challenges, and critical insights. The findings reveal that multi-layered digital twin architectures incorporating real-time data fusion and AI-driven decision-making enhances traffic efficiency and safety. Advanced simulation techniques combined with sophisticated AI/ML algorithms demonstrate notable improvements in real-time responsiveness and predictive accuracy for traffic management. Additionally, the integration of digital twins has shown substantial promise in safeguarding vulnerable road users through proactive and adaptive safety strategies. Despite these advancements, key challenges persist, including interoperability of diverse data sources, scalability of digital twins for extensive traffic networks, and managing uncertainty within dynamic urban environments. Addressing these challenges will be essential for the future development and deployment of intelligent, adaptive, and sustainable intersection management systems.

eess.SY↗

arXiv2025

Rethinking the Sioux Falls Network: Insights from Path-Driven Higher-Order Network Analysis

Benchmark scenarios are widely used in transportation research to evaluate routing algorithms, simulate infrastructure interventions, and test new technologies under controlled conditions. However, the structural and behavioral fidelity of these benchmarks remains largely unquantified, raising concerns about the external validity of simulation results. In this study, we introduce a mathematical framework based on higher-order network models to evaluate the representativeness of benchmark networks, focusing on the widely used Sioux Falls scenario. Higher-order network models encode empirical and simulated trajectory data into memory-aware network representations, which we use to quantify sequential dependencies in mobility behavior and assess how well benchmark networks capture real-world structural and functional patterns. Applying this framework to the Sioux Falls network, as well as real-world trajectory data, we quantify structural complexity, optimal memory length, link prediction accuracy, and centrality alignment. Our results show and statistically quantify that the classical Sioux Falls network exhibits limited path diversity, rapid structural fragmentation at higher orders, and weak alignment with empirical routing behavior. These results illustrate the potential of higher-order network models to bridge the gap between simulation-based and real-world mobility analysis, providing a robust foundation for more accurate and generalizable insights in transportation research.

cs.CE↗

arXiv2025

Beyond Connectivity: Higher-Order Network Framework for Capturing Memory-Driven Mobility Dynamics

Understanding and predicting mobility dynamics in transportation networks is critical for infrastructure planning, resilience analysis, and traffic management. Traditional graph-based models typically assume memoryless movement, limiting their ability to capture sequential dependencies inherent in real-world mobility patterns. In this study, we introduce a novel higher-order network framework for modeling memory-dependent dynamics in transportation systems. By extending classical graph representations through higher-order Markov chains and de Bruijn graph structures, our framework encodes the spatial and temporal ordering of traversed paths, enabling the analysis of structurally and functionally critical components with improved fidelity. We generalize key network analytics, including betweenness centrality, PageRank, and next-step prediction, to this higher-order setting and validate our approach on the Sioux Falls transportation network using agent-based trajectory data generated with MATSim. Experimental results demonstrate that higher-order models outperform first-order baselines across multiple tasks, with the third-order model achieving an optimal balance between predictive accuracy and model complexity. These findings highlight the importance of incorporating memory effects into network-based transportation analysis and offer a scalable, data-driven methodology for capturing complex mobility behaviors in infrastructure systems.

cs.SI↗

arXiv2025

Improving Pedestrian Safety at Intersections Using Probabilistic Models and Monte Carlo Simulations

National Highway Traffic Safety Administration reported 7,345 pedestrian fatalities in the United States in 2022, making pedestrian safety a pressing issue in urban mobility. This study presents a novel probabilistic simulation framework integrating dynamic pedestrian crossing models and Monte Carlo simulations to evaluate safety under varying traffic conditions. The framework captures key influences on pedestrian decisions, such as traffic light states, vehicle proximity, and waiting times, while employing the Intelligent Driver Model (IDM) to simulate realistic vehicle dynamics. Results from 500 trials show that pedestrians avoid crossing during green lights, reducing collision risks, while shorter waiting times during red lights encourage safer crossings. The risk is heightened during yellow lights, especially with nearby vehicles. This research emphasizes the importance of adaptive traffic control measures, such as pedestrian-triggered signals and enhanced traffic light timing, to mitigate risks and prioritize pedestrian safety. By modeling realistic interactions between pedestrians and vehicles, the study offers insights for designing safer and more sustainable urban intersections.

stat.AP↗

arXiv2025

Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning

Spatially embedded networks (SENs) represent a special type of complex graph, whose topologies are constrained by the networks' embedded spatial environments. The graph representation of such networks is thereby influenced by the embedded spatial features of both nodes and edges. Accurate network representation of the graph structure and graph features is a fundamental task for various graph-related tasks. In this study, a Generic Multimodal Spatially Graph Convolutional Network (GMu-SGCN) is developed for efficient representation of spatially embedded networks. The developed GMu-SGCN model has the ability to learn the node connection pattern via multimodal node and edge features. In order to evaluate the developed model, a river network dataset and a power network dataset have been used as test beds. The river network represents the naturally developed SENs, whereas the power network represents a man-made network. Both types of networks are heavily constrained by the spatial environments and uncertainties from nature. Comprehensive evaluation analysis shows the developed GMu-SGCN can improve accuracy of the edge existence prediction task by 37.1\% compared to a GraphSAGE model which only considers the node's position feature in a power network test bed. Our model demonstrates the importance of considering the multidimensional spatial feature for spatially embedded network representation.

cs.LG↗

arXiv2018

TikZ-network manual

TikZ-network is an open source software project for visualizing graphs and networks in LaTeX. It aims to provide a simple and easy tool to create, visualize and modify complex networks. The packaged is based on the PGF/TikZ languages for producing vector graphics from a geometric/algebraic description. Particular focus is made on the software usability and interoperability with other tools. Simple networks can be directly created within LaTeX, while more complex networks can be imported from external sources (e.g. igraph, networkx, QGIS, ...). Additionally, tikz-network supports visualization of multilayer networks in two and three dimensions. The software is available at: https://github.com/hackl/tikz-network.

cs.OH↗

arXiv2017

Generation of Spatially Embedded Random Networks to Model Complex Transportation Networks

Random networks are increasingly used to analyse complex transportation networks, such as airline routes, roads and rail networks. So far, this research has been focused on describing the properties of the networks with the help of random networks, often without considering their spatial properties. In this article, a methodology is proposed to create random networks conserving their spatial properties. The produced random networks are not intended to be an accurate model of the real-world network being investigated, but are to be used to gain insight into the functioning of the network taking into consideration its spatial properties, which has potential to be useful in many types of analysis, e.g. estimating the network related risk. The proposed methodology combines a spatial non-homogeneous point process for vertex creation, which accounts for the spatial distribution of vertices, considering clustering effects of the network and a hybrid connection model for the edge creation. To illustrate the ability of the proposed methodology to be used to gain insight into a real world network, it is used to estimate standard structural statistics for part of the Swiss road network, and these are then compared with the known values.

physics.soc-ph↗