Intelligent Load-Aware Seat Allocation for Railway Coaches: A Greedy Heuristic Approach with Simulation-Based Validation

27 May
Posted on Author

Authors: Ms. Shweta Arun Singh, Ms. Tejal Vinod Apraj, Dr. Jasbir Kaur, Assistant Professor Mr. Suraj Kanal, Assistant Professor Ms. Sandhya Thakkar

Abstract: Efficient seat allocation in large-scale railway reserva- tion systems remains challenging due to uneven passenger weight distribution across coaches. Existing platforms such as IRCTC employ deterministic sequential allocation strategies that prioritise berth preferences but do not explicitly optimise for real-time load balancing. This paper presents the Intelligent Load-Aware Seat Allocation (ILASA) framework, a greedy minimum-load heuristic that dynamically assigns seats by incorporating pas- senger weight, age, journey segment, and berth preference into a single multi-constraint decision process. The framework is evaluated through discrete-event simulation using synthetic passenger data calibrated against published Indian Railways demographic statistics. Across 100 independent simulation runs per experimental condition, ILASA achieves a 55.1% reduction in inter-coach load imbalance (standard deviation of coach loads) and a 22.0% improvement in average seat utilisation under peak occupancy, compared against sequential and random allocation baselines. Mean allocation latency of 12.4 ms satisfies real-time booking requirements. We explicitly acknowledge the limitations of this study: reliance on synthetic rather than operational railway data, a simplified passenger weight model that does not yet account for luggage, group travel constraints, or dynamic passenger movement, and the heuristic nature of the algorithm which has not been benchmarked against metaheuristic or mathematical programming alternatives. A web-based prototype demonstrates the feasibility of real-time visualisation. This work provides a foundation for future research incorporating real-world operational data, more sophisticated optimisation techniques, and comprehensive passenger acceptance studies.

DOI: https://doi.org/10.5281/zenodo.20406082