AI-Enhanced Rail Network Optimization: Dynamic Route Planning and Traffic Flow Management
DOI:
https://doi.org/10.53555/ephijse.v7i3.277Keywords:
AI in rail transport, train routing optimization, traffic flow management, real-time data, predictive analytics, operational efficiency, rail network automationAbstract
By means of dynamic route planning and advanced traffic flow management, artificial intelligence (AI) is transforming the optimization of goods rail networks & significantly improving efficiency & the reliability. Conventional goods train operations run against network congestion, scheduling conflicts, unanticipated delays & the poor resource allocation, which drives increased running costs. Rigid timelines & the reactive decision-making define traditional systems, which limits their responsiveness to actual time changes. By means of predictive analytics, actual time data processing & the machine learning, AI-driven solutions increase goods train efficiency, so enabling operators to foresee congestion, optimize scheduling & the dynamically reroute shipments depending on live data from sensors, GPS, weather forecasts & the past traffic patterns. This reduces downtime, minimizes unnecessary interruptions & improves the general productivity, therefore saving fuel & the running costs. By distributing goods loads & changing the station stay times, AI-driven traffic flow management systems optimize the train load distribution & the terminal operations, thereby insuring enhanced capacity use. By means of IoT & the digital twin technologies, train operators & the logistics companies may replicate many scenarios, improve decision-making & eliminate inefficiencies in freight movement. Furthermore, AI-driven automation helps to improve safety, lower human error rates & increase collaboration between logistics companies and goods train operators. Through best energy utilization & minimum carbon emissions, artificial intelligence improves sustainable goods transportation. Still, broad application calls for addressing issues including data privacy concerns, system compatibility & the significant upfront costs.
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