In modern Communications-Based Train Control (CBTC) systems, the precise calculation of braking curves is critical for ensuring both safety and operational efficiency. Traditional methods rely on conservative, fixed parameters—such as worst-case adhesion coefficients, maximum train mass, and static gradient profiles—which inherently limit track capacity and energy efficiency. This paper proposes a novel framework for dynamically optimizing train braking curves by integrating real-time trackside data. The system utilizes a network of sensors to provide live measurements of wheel-rail adhesion, actual train mass from onboard load cells, instantaneous weather conditions, and infrastructure status. These data streams are processed by an enhanced Vital Zone Controller equipped with a Dynamic Braking Curve Optimizer (DBCO) module. Through high-fidelity simulation of a 25-kilometer metro loop under various environmental scenarios, the performance of this dynamic system is compared against the conventional static model. The results demonstrate substantial improvements: average line speed increased by 8-12%, net energy consumption decreased by 10-15% through enhanced regenerative braking recovery, and theoretical minimum headway was reduced by 5-8%. Crucially, the system also enhanced safety resilience, proactively managing sudden adhesion drops and reducing the incidence of emergency brake applications by over 70% in transitional weather scenarios. This study validates that the fusion of real-time contextual data with CBTC logic enables a paradigm shift from static safety margins to adaptive, risk-aware train control, paving the way for higher-capacity and more energy-efficient urban rail transit.

Ziran Ge. Optimizing Train Braking Curves Using Real-Time Trackside Data in CBTC Environments. International Journal of Frontiers in Engineering Technology (2026), Vol. 8, Issue 1: 1-6. https://doi.org/10.25236/IJFET.2026.080101.

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