Hybrid Regression–Simulation Model for Evaluating Emission Policies in Oversaturated Urban Corridors: A Case Study of Jakarta - Fara Triadi; Arsan Kumala Jaya; Fajerin Biabdillah; Abdul Hanif (Journal of Embedded Systems, Security and Intelligent Systems, 2025)
Urban traffic emissions continue to escalate in Southeast Asian megacities, particularly along oversaturated central business district corridors where chronic congestion amplifies pollutant accumulation. Previous research often separates statistical emission modelling from microscopic simulation, limiting the ability to evaluate policy impacts under real-world saturation conditions. This study aims to assess whether lane-level transport interventions specifically bus-only lanes and motorcycle restrictions can reduce emissions in a hyper-congested Jakarta corridor through an integrated analytical approach. A hybrid regression–microsimulation framework was developed by combining multiple linear regression with SUMO-based traffic simulation. An hourly dataset of traffic flow and CO emissions (n = 8,760) from the Thamrin–Bundaran HI corridor was used to construct a regression model enriched with temporal and lagged predictors. The resulting emission profiles were embedded into SUMO to simulate baseline, bus-lane, and motorcycle-restriction scenarios. The regression model achieved strong predictive performance (R² = 0.692, RMSE = 0.252), with CO_lag1 confirmed as the dominant predictor. Simulation results showed fully overlapping CO₂ emission trajectories across all scenarios, indicating that lane-based interventions do not alter traffic states or emissions under oversaturated conditions. Structural congestion constrains the effectiveness of lane-level policies. Meaningful emission reductions require systemic strategies such as demand management, modal shift, or network redesign. The proposed hybrid framework provides a replicable tool for evaluating transport policies in dense urban corridors