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The rapid expansion of expressway open-toll plazas, where high-speed electronic toll collection lanes operate alongside slower manual and single-lane toll lanes, has introduced complex crash risks that are difficult to capture using traditional crash-frequency models alone. Sparse crash records, heterogeneous operating conditions, and evolving tolling configurations call for data-driven approaches that integrate simulation, surrogate safety metrics, and computational learning. This study aims to perform a comprehensive risk assessment of open-toll plaza configurations by combining surrogate safety measure (SSM) estimations with computational learning (CL)-based accident prediction models. First, a broad set of geometric and operational scenarios is generated by systematically varying lane layout, toll-lane composition, merging length, speed limit, traffic volume, and heavy-vehicle share. For each scenario, microscopic traffic simulations are conducted, and SSMs—including time to collision (TTC), post-encroachment time (PET), and conflict counts—are extracted to quantify instantaneous interaction risk at the vehicle level. These SSMs serve both as comparative safety indicators and as target outputs for subsequent CL models. Next, the CL model is trained to predict conflict frequencies from design and traffic features, while a complementary parametric count model is estimated for benchmarking. To enhance interpretability, eXplainable AI (XAI) attribution techniques are used to decompose the CL predictions into feature-level contributions, revealing nonlinear and interaction effects associated with high-risk operating regimes. The results highlight the dominant influence of traffic volume and toll lane ratio on conflict occurrence. By integrating SSM-based simulation outputs with CL and XAI, the proposed approach provides quantitative and interpretable evidence that supports safer design and operation of expressway open-toll plazas within next-generation data-driven transportation systems.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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