Papers for

transportation network operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Context aware filtering cuts communication in vehicle safety systems

Frugal Collective Perception: Context-Aware Adaptive Reporting for Safety-Critical C-ITS

Abstract: Ensuring safety and scalability in Collective Perception Service (CPS) remains a key challenge for Cooperative Intelligent Transport Systems (C-ITS). Conventional CPS enhances perception by broadcasting Collective Perception Messages (CPMs). However, its reliance on transmitting a potentially large volume of context-irrelevant information at high frequency leads to network congestion, processing delays, and poor scalability. We propose a Context-Aware Adaptive Filter that dynamically adjusts CPM content and transmission frequency based on contextual relevance and situational criticality. By prioritizing safety-critical objects and interactions, the proposed approach prevents information overload while preserving timely updates for decision-making. An end-to-end SUMO--Artery simulation evaluates safety, decision-making efficiency, and communication cost under different computational capacity tiers and transmission rates. Results show that the proposed adaptive filtering mechanism achieves safety performance comparable to conventional CPMs transmitted at the maximum allowed frequency (10~Hz), while reducing communication volume by over 93\% and preventing queue saturation. This demonstrates that context-aware adaptivity enables CPS to remain both scalable and safety-compliant across heterogeneous computing platforms.

Tue 22 SeptNetworking and Internet Architecture
The gist
Cars that share what they see to stay safe can overwhelm communication networks with too much information. The authors propose a smart filter that sends only the most important safety details depending on the situation. This helps avoid network jams while still keeping all vehicles aware of critical dangers. Their tests show the new method can reduce data traffic by over 90% without hurting safety. This approach improves how well cooperative vehicle systems can work together on different hardware.
Open → 2609.26470v1

Graph neural networks estimate connectivity loss after road disruptions

When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions

Abstract: Rapid evaluation of many simultaneous road-link disruptions requires a practical compromise between exact spectral recomputation and local approximation. We estimate relative algebraic-connectivity loss after multi-edge deletion using graph neural networks (GNNs) that learn a bounded correction to a first-order Fiedler sensitivity. The study considers independent, spatially clustered, and edge-betweenness-targeted failures, with graph-disjoint synthetic splits and zero-shot transfer to 13 OpenStreetMap (OSM) areas in six countries. GCN, GraphSAGE, and edge-aware MPNN backbones are compared with analytical baselines. In expanded OSM tests, residual GCN improves spatial-failure MAE by 0.0391 (95% hierarchical interval 0.0151-0.0662), while residual GraphSAGE improves targeted-failure MAE by 0.0257 (0.0095-0.0446). Second-order perturbation improves first-order MAE by only 0.0028-0.0053. Correction slopes decrease under targeted transfer, indicating residual shrinkage around systematic prior error. Leave-one-country-out OSM-to-OSM transfer is mixed: residual GCN improves targeted-failure MAE by 0.0622 (0.0169-0.1153) but worsens the spatial point estimate. Sparse scaling extends to 20,000 nodes and separates one-time spectral setup from amortized screening cost. These results characterize the spectral residual as a useful but domain-sensitive inductive bias for structural connectivity screening. Code, cached networks, and reproducibility artifacts are archived at doi:10.5281/zenodo.22307723.

Thu 10 SeptMachine Learning
The gist
Estimating how road network disruptions reduce connectivity is challenging because exact calculations are slow. The authors use graph neural networks to quickly predict connectivity loss after multiple road closures by correcting simpler mathematical estimates. They test their approach on synthetic data and real road maps from various countries, finding their method improves accuracy while scaling to large networks. The results show the approach works best when disruption patterns are similar to the training data and highlight limits when transferring between different regions.
Open → 2609.11166v1