An analytical framework for optimizing urban rail schedules with energy recovery and sensor integration

Abstract

Urban railway systems are crucial components of sustainable public transportation but face significant operational costs due to energy consumption and maintenance needs. This study develops a novel optimization framework that integrates regenerative braking strategies and Internet of Things (IoT) adoption into train scheduling for improved energy efficiency and reliability. A real-world case study was conducted using data from Iran Urban Railway Organization. The proposed model was solved using CPLEX in GAMS software and tested under various adoption rates of IoT technologies. Results demonstrate that an optimal IoT adoption rate of 0.7 minimizes total operational cost, achieving a cost reduction from 1,687,600 (at 0 adoption) to 1,265,432 units. This rate also balances implementation cost (4,682,356 units) and leads to a 52% decrease in quality-related costs. Moreover, train schedule optimization improved timing consistency: dwell times were stabilized at 1.5–2 min, with longer stops (5 min) at major stations, and train speeds ranged between 30–43 km/h. These improvements enhance service reliability and enable significant energy recovery through regenerative braking. This research provides a robust decision-support tool for railway operators by combining IoT-based predictive maintenance and energy-aware train scheduling, offering measurable cost and performance benefits in real-world operations.