Explainable Traffic Prediction in Intelligent Transportation and Vehicular Networks via Large Language Model Reasoning
Keywords:
explainable artificial intelligence, traffic prediction, large language models, intelligent transportation systems, vehicular networks, spatiotemporal forecasting, governanceAbstract
Accurate traffic prediction is a foundational capability for next-generation intelligent transportation systems and connected vehicular networks, yet the opaque nature of state-of-the-art deep learning models erodes trust among operators, regulators, and road users. While graph neural networks and spatiotemporal transformers have dramatically improved forecasting accuracy, their capacity to produce human-intelligible explanations remains limited to attention heatmaps or saliency maps that rarely translate into actionable operational narratives. This paper presents a system-level investigation into the integration of large language model reasoning as a prospective explanatory layer atop conventional traffic prediction architectures. It charts the structural trade-offs between deterministic sensor-driven forecasts and generative natural language rationales, articulating how a multi-stage pipeline can convert latent spatiotemporal representations into contextualized explanations that reference infrastructure status, recurrent congestion patterns, weather conditions, and social event calendars. The discussion extends beyond model accuracy to encompass governance challenges, data provenance auditing, robustness under adversarial sensor perturbations, fairness across heterogeneous urban districts, sustainability of large-model deployments, and policy frameworks for automated decision support in traffic management centers. Through extended architectural analysis and cross-domain comparison with explainability techniques in finance and healthcare, the paper identifies where LLM-based reasoning can add genuine decision-making value and where it introduces new failure modes, including hallucinated causal inferences and disproportionate computational cost. The analysis is structured to inform both system designers and regulatory stakeholders about the conditions under which language-driven explainability can enhance the resilience, transparency, and public accountability of intelligent vehicular infrastructures.
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