Jiangsu Railway Group Deploys AI Agent Platform for Automated Train Timetable Analysis

The train working timetable forms the core foundation and master plan for passenger transport organisation. Thorough timetable analysis and precise identification of changes brought by timetable revisions supply robust baseline data to guide timetable adjustments and settle transport revenue clearing. 

For a long time, timetable analysis has relied heavily on manual offline work, with inherent drawbacks including difficulties balancing accuracy and timeliness and heavy workload for data aggregation and verification. 

To resolve bottlenecks in conventional workflows, the operational management team of Jiangsu Railway Group has adopted an agent platform and deployed artificial intelligence as a tool to boost operational efficiency and professional capability. 

The team brings AI capacity to frontline operations and builds a full-process, integrated and intelligent timetable analysis system closely aligned with practical operational scenarios.

The project dismantles barriers between business workflows and technology to deliver AI capability directly to operational staff. Leveraging the platform’s no-code functionality, staff issue instructions in natural language to trigger intelligent agents to complete diverse analytical tasks automatically. 

Automation and intelligence for analytical workflows can be achieved without dedicated software developers. Frontline teams are freed from repetitive manual labour and can focus judgement and decision-making on higher-value work.

The system mitigates risks of losing practical operational expertise by converting hands-on experience into digital capability. The team has fully mapped and standardised timetable analysis workflows, allowing intelligent agents to execute analytical work automatically according to predefined rules. 

Mature operational procedures are packaged as standardised skill components, translating field experience into reusable digital capacity. This unifies operational standards across the whole workflow and cuts training periods for new staff, enabling a shift from experience-driven operation to algorithm-driven management.

Measurable results show the transition from labour-intensive manual work to instant query-based responses. Tasks that once demanded 48 hours of manual work can now be completed in ten minutes by AI agents, with judgement accuracy reaching 100 per cent. 

A supporting visual interactive web interface simplifies operation and lowers technical barriers for frontline users. The full chain covering data retrieval, aggregation, assessment and analysis for timetable monitoring is fully operational, forming a closed-loop digital, visual and standardised management framework.

The operational management team of Jiangsu Railway Group will continue to prioritise passenger transport services. It will refine lightweight, accessible and highly adaptable intelligent toolkits in line with the digital transformation of railway operations, strengthening refined operational management and lifting railway performance through digital and intelligent technologies.