Building Resilience in Pedestrian Safety Against Behavioral Uncertainty

Abstract

Pedestrian safety at urban intersections is influenced by complex interactions among environmental conditions, social cues, and boundedly rational behavioral responses, making intervention planning under uncertainty particularly challenging. Existing studies have largely emphasized descriptive analysis or predictive modeling, with limited attention to prescriptive decision support under data scarcity. This study proposes a Bayesian-network-based robust optimization framework for pedestrian safety planning, complemented by a large language model (LLM)-assisted parameter-elicitation process. We first construct a three-layer Bayesian network based on the stimulus–organism–response paradigm to represent the propagation of risk from environmental cues through latent psychological mechanisms to behavioral violations and accident risk. To support model initialization when site-specific behavioral data are limited, we introduce an LLM-assisted elicitation protocol that maps literature-based qualitative evidence to intervention mechanisms, effect directions, and qualitative strength classes. Numerical parameter ranges are subsequently assigned through explicit mapping rules rather than generated directly by the LLM. We then formulate a bi-objective robust optimization model that distinguishes between physical interventions, which alter root-node distributions, and cognitive interventions, which modify conditional probability tables. Using the ϵ-constraint method, the framework generates Pareto-optimal intervention portfolios and evaluates cost–risk trade-offs under behavioral uncertainty and adverse operating scenarios.