Review on AI-Based Coordinated Traffic Load Scheduling for Rail-Road Freight Corridors

Abstract

The rapid expansion of freight transportation demand has intensified congestion, operational inefficiencies, and environmental concerns in rail–road freight corridors. Traditional scheduling approaches are largely static and rule-based, making them inadequate for handling dynamic demand fluctuations, infrastructure constraints, and real-time disruptions. This research proposes an AI-based coordinated traffic load scheduling framework for rail–road freight corridors. The study integrates Machine Learning-based demand forecasting, multi-objective optimization techniques, and intelligent decision-support mechanisms to dynamically allocate freight loads across multimodal networks. The proposed model aims to minimize total transportation cost, transit time, and energy consumption while maximizing corridor throughput and system reliability. Real-time data inputs—including traffic conditions, wagon availability, truck fleet status, and terminal congestion—are incorporated to enhance adaptive scheduling capability. The performance of the proposed framework will be evaluated using simulation-based analysis and comparative assessment against conventional scheduling methods. Expected outcomes include improved resource utilization, reduced congestion, lower carbon emissions, and enhanced operational resilience. This research contributes to the advancement of smart and sustainable freight transportation systems by developing a data-driven, adaptive, and scalable AI-enabled scheduling solution for integrated rail–road logistics networks.