ArticlePloS one2026
Non-linear impact mechanisms of multi-modal urban traffic on air quality: An interpretable machine learning study for sustainable policy making.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Urban air pollution, specifically Nitrogen Dioxide (NO2), presents a multifaceted challenge that is intricately coupled with the stochastic, multi-modal, and non-linear dynamics of mega-city traffic systems. This study systematically investigates the non-linear impacts of mixed traffic flow-comprising motorcycles (MC), private cars (PC), and heavy vehicles (BT)-on local air quality at the iconic Bundaran HI intersection in Jakarta, Indonesia. Leveraging a high-resolution, year-long longitudinal dataset, we developed a robust Random Forest (RF) modeling framework integrated with Permutation Importance and Partial Dependence Analysis (PDP) to decipher the environmental footprint of urban transport under tropical conditions. Our results reveal that private car volume and the Volume-to-Capacity (V/C) ratio act as the primary catalysts for NO2 spikes, significantly outweighing the contribution of heavy vehicles in this specific urban corridor. Crucially, a distinct non-linear threshold effect was identified: NO2 concentrations undergo a regime shift, rising exponentially once the V/C ratio exceeds a critical "elbow" of 0.65. This non-linearity indicates that traditional linear mitigation strategies and average-speed-based emission models significantly underestimate pollution risks during saturated traffic states. Policy scenario simulations demonstrate that a 30% reduction in private vehicle volume yields a 5.8% reduction in mean NO2, offering nearly six times the environmental utility of heavy vehicle restrictions. Furthermore, the study explores the role of road surface materials-specifically Stone Mastic Asphalt (SMA)-and meteorological interactions in exacerbating localized pollution. This research provides a data-driven, interpretable framework for urban planners to transition from generic traffic bans toward precision-based, sustainable management strategies that align with the core principles of cleaner production, urban resilience, and UN Sustainable Development Goal 11.
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