SIH26028/Smart Automation

Dynamic ETA forecasting software for trains

A software system that uses real-time data and machine learning to dynamically forecast train arrival times for passengers and railway staff.

Ministry of Railways · Software

Teams already in: 2/500almost everyone who enters · read 1 hour ago
Buildable in 36 hours: LowOur estimate, from their wording
Hard blockers: 1Restricted data
Submissions close: 15 Sept9 days left

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Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in

Whether this suits your team

Why this fits you

This organisation declared winners on 3 problem statement(s) in SIH 2025.

What argues against it

Potential concern: needs data you may not get.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Data Integration Pipeline
Collects live train location feeds, operational parameters, and historical delay records.
02
Machine Learning Model
Calculates and continuously updates train arrival times using historical and real-time data.
03
Prediction API
Exposes endpoints for external systems to retrieve up-to-date arrival forecasts.
04
Control Room Dashboard
Displays live train tracking and delay predictions for internal staff monitoring.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The jury will check if your machine learning model actually improves over static schedules using real-time data, and whether your system provides functional APIs for dashboards and displays.

They never say

exact data schemas for train feeds
required accuracy threshold for ETA predictions
specific cloud or on-premise infrastructure requirements

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Exact data schemas for train feeds?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Required accuracy threshold for ETA predictions?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific cloud or on-premise infrastructure requirements?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

9 more— the ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

Needs data you may not get
The system requires live GPS tracking data, signal aspects, and operational data from Indian Railways which students cannot easily access.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: None attached
Does the brief need data?: Nothing in the text says it depends on a dataset we would have to find.

Also from Ministry of Railways

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26028, set by Ministry of Railways.
Its official title, word for word: "Dynamic Forecast of Expected Time of Arrival (ETA) for Coaching Trains"
Theme: Smart Automation. Category: software.

In plain terms: A software system that uses real-time data and machine learning to dynamically forecast train arrival times for passengers and railway staff.

## The organisers' full description, verbatim

Background: Accurate forecasting of the Expected Time of Arrival (ETA) for coaching trains is vital for improving passenger satisfaction and operational efficiency in Indian Railways. Currently, ETA is often estimated using static schedules, current delays and in-built recovery times, which may not reflect real-time ground realities such as speed restrictions, congestion, unscheduled stoppages or historical patterns. As a result, passengers, station staff, and downstream logistics services face uncertainty and planning difficulties. With the growing demand for real-time train information and forecast, there is a pressing need to shift towards a data-driven, dynamic ETA prediction system that continuously adapts to actual train running conditions. Detailed Description: Indian Railways operates a vast network of passenger trains across diverse geographies, weather conditions, and traffic patterns. These coaching trains often face variability in journey times due to multiple real-world factors such as signal halts, congestion on busy routes, delays in preceding trains, temporary speed restrictions, unscheduled maintenance blocks, level crossing gates and operational bottlenecks.Despite this, ETA predictions at intermediate and destination stations are still often based on the train schedule, current delays and in-built recovery times, which lack accuracy and responsiveness.This limitation affects not just passengers but also impacts station planning, crew scheduling, platform allocation, cleaning operations, and feeder transport services. For long-distance trains with multi-day journeys, even a small deviation can cascade and lead to significant uncertainty. In an era where passengers expect real-time updates through mobile apps and station displays, inaccurate or outdated ETA predictions undermine service quality and trust. The challenge is to create a system that can dynamically forecast the ETA of trains at various points in their journey using real-time data feeds. These may include GPS-based location data, signal aspects, average sectional running times, weather conditions, historical delay patterns, and congestion levels on downstream tracks. The system must also be scalable to cover thousands of trains simultaneously and adaptable to the Indian Railways diverse operational zones. It should account for temporal and spatial variability in train performance and continuously refine its predictions using machine learning or statistical models. Such a system can serve as a foundation for better passenger communication, resource planning, and delay management. Expected Solution: The expected solution is a real-time ETA prediction system for coaching trains using data-driven models.It should integrate live train location data, operational parameters, historical delay trends, and network conditions to forecast arrival times at upcoming stations.The system must dynamically update ETAs in response to real-time events and delays. Machine learning or statistical forecasting techniques should be employed to improve accuracy over time. The solution should feature APIs for integration with mobile apps, station displays, and control room dashboards, ensuring that passengers and staff receive reliable, up-to-date information to support decision-making and planning.

## What the brief never says

- exact data schemas for train feeds
- required accuracy threshold for ETA predictions
- specific cloud or on-premise infrastructure requirements
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- Known obstacles in the brief: Restricted data.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

---
Problem statement facts assembled by Wynit from Ministry of Railways's published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (450 words)

Background: Accurate forecasting of the Expected Time of Arrival (ETA) for coaching trains is vital for improving passenger satisfaction and operational efficiency in Indian Railways. Currently, ETA is often estimated using static schedules, current delays and in-built recovery times, which may not reflect real-time ground realities such as speed restrictions, congestion, unscheduled stoppages or historical patterns. As a result, passengers, station staff, and downstream logistics services face uncertainty and planning difficulties. With the growing demand for real-time train information and forecast, there is a pressing need to shift towards a data-driven, dynamic ETA prediction system that continuously adapts to actual train running conditions. Detailed Description: Indian Railways operates a vast network of passenger trains across diverse geographies, weather conditions, and traffic patterns. These coaching trains often face variability in journey times due to multiple real-world factors such as signal halts, congestion on busy routes, delays in preceding trains, temporary speed restrictions, unscheduled maintenance blocks, level crossing gates and operational bottlenecks.Despite this, ETA predictions at intermediate and destination stations are still often based on the train schedule, current delays and in-built recovery times, which lack accuracy and responsiveness.This limitation affects not just passengers but also impacts station planning, crew scheduling, platform allocation, cleaning operations, and feeder transport services. For long-distance trains with multi-day journeys, even a small deviation can cascade and lead to significant uncertainty. In an era where passengers expect real-time updates through mobile apps and station displays, inaccurate or outdated ETA predictions undermine service quality and trust. The challenge is to create a system that can dynamically forecast the ETA of trains at various points in their journey using real-time data feeds. These may include GPS-based location data, signal aspects, average sectional running times, weather conditions, historical delay patterns, and congestion levels on downstream tracks. The system must also be scalable to cover thousands of trains simultaneously and adaptable to the Indian Railways diverse operational zones. It should account for temporal and spatial variability in train performance and continuously refine its predictions using machine learning or statistical models. Such a system can serve as a foundation for better passenger communication, resource planning, and delay management. Expected Solution: The expected solution is a real-time ETA prediction system for coaching trains using data-driven models.It should integrate live train location data, operational parameters, historical delay trends, and network conditions to forecast arrival times at upcoming stations.The system must dynamically update ETAs in response to real-time events and delays. Machine learning or statistical forecasting techniques should be employed to improve accuracy over time. The solution should feature APIs for integration with mobile apps, station displays, and control room dashboards, ensuring that passengers and staff receive reliable, up-to-date information to support decision-making and planning.