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Ministry of Railways

Browse and strategize across all 3 official problem statements submitted by Ministry of Railways for Smart India Hackathon 2026.

3
Total PS
2
Software
1
Hardware

Problem Statements (3)

Match My Team
PS 26026HARDWARE
Heavy R&D
4.3/5.0Feas 3.9

Development of Mobile (Quadruped)/Handheld Device/System for Real-Time Detection of Narcotics and Explosives across Indian Railways.

Ministry of Railways
Robotics and Drones

Drug trafficking and explosive threats across Indian Railways platforms, train coaches, and ballast tracks require rapid non-intrusive screening in GPS-denied, crowded environments. Build an AI-enabled autonomous quadruped robot and handheld inspection system equipped with thermal/optical cameras, LiDAR SLAM, and chemical spectrometry/sensor payloads to detect narcotics (heroin, cocaine, meth) and high explosives (RDX, TNT, IEDs).

+5 more
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PS 26027SOFTWARE
36h Feasible
3.9/5.0Feas 4.5

Al-Powered Automatic Block Planning to Maximize Asset Availability for Train Operations on Indian Railways

Ministry of Railways
Transportation & Logistics

Indian Railways Engineering, Traction (OHE), and Signalling departments plan track maintenance blocks in decentralized silos (TMS, SMMS, TDMS), causing severe train delays and under-utilized track downtime. Build an AI-powered automated block planning and multi-department scheduling optimizer that synchronizes joint maintenance windows, models train traffic congestion, and maximizes track availability.

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PS 26028SOFTWARE
Moderate Scope
4.2/5.0Feas 4.3

Dynamic Forecast of Expected Time of Arrival (ETA) for Coaching Trains

Ministry of Railways
Disaster Management

Indian Railways National Train Enquiry System (NTES) uses static historical section running times to forecast train Arrival Times (ETA), leading to inaccurate passenger updates during unexpected yard congestion and weather delays. Build a real-time dynamic ETA forecasting engine combining GPS locomotive telemetry, section signaling occupancy, line capacity bottleneck models, and weather feeds to predict station arrival times with minute-level precision.

+4 more
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