Incident-Causal Travel-Time Forecasting for Risk-Bounded Autonomous Routing
Keywords:
Autonomous Vehicles, Causal Inference, Travel-Time Forecasting, Risk-Bounded RoutingAbstract
The advent of autonomous vehicles necessitates advanced routing protocols capable of handling dynamic and unpredictable traffic conditions. Traditional travel-time forecasting models predominantly rely on historical correlations and often fail to provide accurate predictions during anomalous events, such as traffic incidents, weather disruptions, or infrastructural failures. This paper introduces a comprehensive framework for incident-causal travel-time forecasting integrated with risk-bounded autonomous routing. By shifting from correlation-based machine learning to causal inference paradigms, the proposed methodology explicitly models the structural dependencies between traffic incidents, environmental variables, and localized congestion propagation. The forecasting engine generates probabilistic travel-time distributions rather than deterministic point estimates, enabling a robust quantification of uncertainty. Subsequently, a risk-bounded routing strategy is deployed to optimize navigation paths while strictly constraining the probability of delayed arrivals or exposure to highly volatile traffic zones. Extensive simulation-based evaluations demonstrate that the causal forecasting approach significantly reduces prediction errors under non-recurrent congestion scenarios compared to traditional deep learning baselines. Furthermore, the risk-bounded routing algorithm successfully maintains travel-time reliability, ensuring that autonomous fleets operate within strictly defined safety and efficiency margins. The findings underscore the critical importance of embedding causal reasoning into intelligent transportation systems for resilient autonomous navigation.
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Copyright (c) 2026 Fernanda Ramírez, Clara Núñez Sánchez
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