ELSSA: Explainable large language model-based decision support for public transit incident management

Abstract

Rapid incident response in public transit requires supervisors to make defensible decisions that align with both formal standard operating procedures (SOPs) and practical agency experience recorded in past reports. However, conventional retrieval-augmented generation (RAG) often fragments handbook context and underutilizes precedent knowledge embedded in unstructured control narratives. We present ELSSA, an Experience- and Logic-integrated System for Supervisor Assistance, which unifies rule-based and experience-based evidence for explainable incident-management support. ELSSA couples (i) a hierarchy-aware RAG pipeline over a structured ontology of the Toronto Transit Commission (TTC) incident handbook, and (ii) a knowledge-graph–based RAG module that converts TTC service occurrence reports into a regularized transit-incident knowledge graph via lightweight frequency-based auto-regularization; hybrid semantic and graph retrieval then surfaces relevant clauses and structurally similar precedents. Experiments using TTC supervisor training materials and Route 29 service occurrence reports show that ELSSA improves rule-based SOP question answering over strong RAG baselines and produces more robust experience-based control suggestions across original, radio-style, and paraphrased incident descriptions. A temporally separated 2023 held-out database further provides an initial test of generalization to newly received reports. A compliance analysis shows that service recovery and core documentation elements are highly visible in historical records which reflacts the reliability of the suggestions from ELSSA. Overall, ELSSA demonstrates how hierarchy-aware SOP retrieval and regularized incident knowledge graphs can support evidence-linked, auditable, and human-supervised decision assistance for public transit operations.