Publication
Risk‑Optimized Mobility through Graph‑Based Prediction
Sabine Janzen; Kanav Avasthi; Behkam Fallah; Hannah Stein; Wolfgang Maaß
In: Proceedings of International Conference on Conceptual Modeling. International Conference on Conceptual Modeling (ER-2025), 10/2025.
Abstract
Reliable routing in multimodal transport networks requires more than minimizing travel time: it demands
accounting for the risk of delays and disruptions. This paper presents ROMY (Risk-Optimized Mobility), a
decision support system that integrates heterogeneous data sources, advanced feature engineering, and a graph
neural network (GNN)–based predictive core to deliver personalized, risk-aware route recommendations. ROMY
models the transport network as a directed, attributed graph, combining spatial, temporal, modal, and semantic
attributes to capture complex dependencies between network segments. The predictive core employs edge-conditioned message passing to incorporate contextual information such as travel mode, time-of-day, and navigation instructions into segment-level risk estimation. A pilot implementation demonstrates the system’s
ability to identify high-risk segments, outperform baseline models, and offer alternative routes that reduce
risk with minimal impact on travel time. The results highlight ROMY’s potential to enhance the reliability of
multimodal mobility services and support data-driven transport planning.