Deep Learning and Optimization Techniques for Smart Transportation Systems

The Modern transportation systems face critical 
challenges including inefficient demand management, manual 
scheduling, poor resource utilization, and lack of real-time 
intelligence. Traditional transport management systems fail to 
adapt dynamically to changing passenger demand, leading to 
operational inefficiencies and reduced service quality. 
This paper presents YATRIK ERP, an AI-powered intelligent 
transportation management system that integrates deep learning 
models and optimization algorithms for real-time 
decision-making. The system utilizes Long Short-Term Memory 
(LSTM) networks for passenger demand prediction, Genetic 
Algorithms for autonomous scheduling, and multi-factor 
analysis for crew fatigue monitoring. 
The transportation network is modeled as a dynamic system 
where demand patterns, resource availability, and operational 
constraints are continuously analyzed. The proposed system 
optimizes scheduling by considering demand forecasts, resource 
utilization, and safety constraints. 
The architecture follows a microservices-based design, 
separating the ERP backend (Node.js) from machine learning inference (Python Flask), ensuring scalability 
maintainability. Experimental results demonstrate significant 
improvements in operational efficiency (≈25%), cost reduction 
(≈30%), and enhanced passenger satisfaction. 
The system provides a scalable, intelligent, and practical solution 
for modern smart transportation systems.