Connect Express
A platform that helps travelers find the best train routes by analyzing schedules and predicting delays ensuring smooth journeys during busy times
Created on 9th November 2024
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Connect Express
A platform that helps travelers find the best train routes by analyzing schedules and predicting delays ensuring smooth journeys during busy times
The problem Connect Express solves
Problem Statement
Consider a middle-class man trying to book a train ticket according to his convenience and budget.
Ideal Situation:
Ideally, he can find a direct train fitting his schedule, budget, and convenience.
Reality:
Currently, the passenger cannot book a direct train because:
- Seats are filled.
- Train timings don't match his schedule.
- Ticket price is too high for his budget.
- He lacks time to search for alternatives.
This is problematic because time spent searching could be used more productively. We have observed this issue affecting many.
According to Indian Railways, many people can't book train tickets due to timing and availability issues, exacerbated by the Advance Reservation Period (ARP): - High cancellation rate: About 21% of reservations are canceled.
- No-shows: 4–5% of passengers don't show up.
- Ticket blocking: Longer reservation periods increase ticket blocking.
Consequences:
Consequently, passengers spend much time finding alternative routes, requiring knowledge of train timings. If users can easily find and book alternatives, IRCTC can reduce last-minute cancellations by filling otherwise empty seats.
Proposal:
Create a web-based platform with an ML model. Users select origin, destination, time, and budget. The ML model predicts delays and provides effective train paths, including connecting trains and those stopping at the destination.
Key Features:
- Real-Time Data Integration: Use real-time Indian Railways data on seat availability, delays, cancellations, and platform changes.
- Advanced ML Capabilities: Predict delays and cancellations using historical and real-time data.
This solution saves time and effort for passengers and can reduce IRCTC's cancellation rates by filling seats that might otherwise remain empty.
Note:
We are currently seeking an IRCTC API for real-time train data integration,Meanwhile, we are using random data
Challenges we ran into
Data Accessibility:
- Lack of Official APIs: One of the primary hurdles was the unavailability of official APIs from IRCTC for accessing real-time train data such as delays, timings, seat availability, and platform information. This made it difficult to integrate accurate and up-to-date information into our platform.
- Workarounds with Data: In the absence of real-time data, we had to rely on randomly generated data for development and testing, which limited the accuracy of our predictive models.
Predictive Model Accuracy:
- Data Quality: The accuracy of our predictions heavily depends on data quality. Incomplete or inaccurate data can lead to unreliable recommendations.
- Dynamic Variables: Factors like unexpected train maintenance, sudden schedule changes, and external events introduced complexity in making accurate predictions.
Algorithm Development:
- We found it really difficult to come up with an algorithm that could effectively predict train delays and suggest optimal train paths based on user preferences. After extensive research and numerous iterations, we finally developed a suitable algorithm that meets our requirements.
Tracks Applied (1)
Best Use of MongoDB Atlas
Major League Hacking