CONGRATULATIONS
College Name | Status | Team Name | Challenge Category | Team Members | Founder Comments |
|---|---|---|---|---|---|
Vasantdada Patil Pratishthan College Of Engineering | Winner 1st Place | Magic-Coders | Multimodal Journey Planning | Rahul Jadhav; Nashrah Ansari; Siddhesh Howale | A truly standout team — their approach to multimodal mobility was thoughtful, layered, and deeply reasoned. From sustainability to user experience, every element reflected clarity, creativity, and precision — backed by a clean, usable prototype that spoke for itself. |
Vasantdada Patil Pratishthan College Of Engineering | Winner 2nd Place | CogniSynth | Headless Rake Mapping Algo | Shivam Patel; Vijay Sharma; Karthikeya Thatipamula | This team took on the toughest challenge head-on — showing grit, curiosity, and resilience. Their work with complex datasets opened up fresh perspectives and directions for us to explore, proving that bold thinking always pays off. |
Vasantdada Patil Pratishthan College Of Engineering | Selected to Final Round | Olympico | Multimodal Journey Planning | Krish Patel; Sujay Sathe | |
Vasantdada Patil Pratishthan College Of Engineering | Selected to Final Round | Nobel-Programmers | Multimodal Journey Planning | Nipun Mahajan; Bhavesh Pathak; Shantanu Bhukan | |
FR. Conceicao Rodrigues College of Engineering | Selected to Final Round | Send-Nodes | Declining Active Users Analysis | Aliasgar Lakdawala; Siddhant Jadhav; Tejas Salvi | |
Atharva College of Engineering | Selected to Final Round | The Innov8tors | Multimodal Journey Planning | Rishabh Sharma; Tarang Gaur; Ajaz Gulfrosh | |
Thakur College of Engineering and Technology | Team-Techtonic | Chat Feature | Anjali Prajapati; Arpit Rai; Yash Pandey | ||
Thakur College of Engineering and Technology | Stone-Cold-Chat-Feature | Chat Feature | Nirek Jaiswal; Siddharth Gupta; Piyush Jaiswal | ||
Thakur Shyamnarayan Engineering College | Journey-Coders | Multimodal Journey Planning | Aditya Jadhav; Nitin Gupta; Bharat Patel | ||
MCT's Rajiv Gandhi Institute of Technology | Salt-N-Pepper | Multimodal Journey Planning | Aryan Mhalsank; Tanmay Chaudhari | ||
Thadomal Shahani Engineering College | TownHall3 | Multimodal Journey Planning | Pratham Manjrekar; Parth Khandelwal; Vedang Kulkarni | ||
Thakur College of Engineering and Technology | Data-Pro | Declining Active Users Analysis | Sejal Radheshyam Yadav | ||
Narsee Monjee Institute of Management Studies | Gradient-Gurus | Declining Active Users Analysis | Maadhav Agarwal; Gargi Surse; Siddhi Oswal |
Yatri App Founder's Note..
Calling all students, engineers, designers, and problem-solvers.
This is your stage.
Whether you're passionate about:
>>Designing smarter passenger experiences, or
>>Building scalable solutions for real-world mobility puzzles,
You’ll get real-world prompts, mentorship from industry experts, and a unique chance to shine in the urban mobility ecosystem. Prizes, recognition, and real reach are part of the deal.
If you’ve ever scanned a system and thought, “I could build a better version,” then you absolutely belong here.
Cofounder @ Yatri - City Travel Guide | Official App for Mumbai Local in Collaboration with Indian Railways | Mobility
Aug 05 - Aug 22
Registrations Open
Aug 7 - Aug 25
Round 1 Idea Submission
Aug 26 - Aug 27
Shortlisted Teams Round 2
Aug 28 - Sep 19
Round 2 Build Prototype
Sep 19 - Sep 21
Working Prototype Demo
Sep 22 - Sep 23
Shortlisted Teams Finale
Sep 26
FINALE !!! @Yatri office premises
Hackathon Schedule
CHALLENGES
Problem Statement
Mumbai local train rakes, equipped with GPS devices, send real-time location pings but often operate as "headless" (unmapped to train numbers), reducing live tracking accuracy. Currently, only 70% of rakes are mapped daily. Your mission is to develop a predictive model to increase this to 95%, enhancing the Yatri App's reliability for commuters.
1/4
Headless Rake Mapping Algo
Objective
Create a solution to accurately map headless rakes to their respective train numbers using GPS data, historical patterns, and other inputs, improving real-time tracking for millions of users.
Input Data
-
Real-time GPS pings (latitude, longitude, timestamp)
-
Historical rake-to-train number mappings
-
Train schedules, routes, and station stop data with dwell times
Expected Outcome
A predicted train number for each headless rake, validated in real-time, to boost daily mapping accuracy.
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Problem Statement
Objective
The Yatri App is facing a drop in Daily Active Users (DAU), potentially due to UI/UX issues, slow loading times, large app size, or inaccurate live tracking from unmapped trains. Users are uninstalling or reducing engagement, and your task is to identify the root causes and propose solutions to boost retention.
2/4
Declining
Active Users
Analysis
Analyze data to pinpoint the key factors driving DAU decline and develop actionable strategies to enhance user satisfaction and retention.
Input Data
-
User session data (frequency, duration, feature usage)
-
App performance metrics (loading time, crash rate, app size)
-
User feedback and reviews from app stores
-
Uninstall survey responses
Expected Outcome
A detailed report highlighting the primary causes (e.g., slow loading, unmapped trains) and prioritized recommendations to reverse the decline.
3/4
Multimodal
Journey
Planning
Problem Statement
Objective
Yatri’s current journey planner, built on GTFS and OpenTripPlanner (OTP), prioritizes the fastest routes for Mumbai’s multimodal network (Western/Central Railways, Harbour/Trans-Harbour Lines, BEST, VVMT, MBMT, Metro, Monorail), often leading to 5-6 impractical transfers. Your challenge is to optimize routes, integrate last-mile connectivity.
Enhance the OTP algorithm to prioritize routes with 1-2 transfers (or max switch overs chosen by user), keeping travel times within 10-20% of the fastest option, and integrate last-mile options (e.g., auto-rickshaws, Ola/Uber, bike-sharing). Routes can be demonstrated on UI/UX designed to display the plan.
-
GTFS data and OTP algorithm outputs
-
Multimodal network schedules (rail, bus, metro, monorail)
-
Last-mile service data (e.g., auto-rickshaw zones, ride-hailing APIs, bike-sharing locations)
-
User feedback on current UI/UX issues
Input Data
Expected Outcome
A modified OTP algorithm balancing travel time and switches, including last-mile options with real-time fares and booking. A revamped web app UI/UX with a clean interface highlighting optimized routes and preferences (e.g., fewer switches, cost, accessibility), plus a prototype with mockups or a working frontend.
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4/4
Chat Feature
Problem Statement
Objective
The Yatri App aims to introduce a chat feature, to let users share real-time commute updates on train delays, crowding, and more. Your challenge is to design and prototype this feature to boost engagement and set Yatri apart from competitors.
Develop a functional chat feature prototype with a scalable backend and intuitive UI to enhance user interaction and potentially increase DAU.
Input Data
-
User demographics and commute patterns (for tailored chat rooms)
-
Competitor analysis (e.g. chat features from competitor apps)
-
Technical constraints (e.g., app size, server capacity)
Expected Outcome
A prototype integrating a real-time chat system into the Yatri App, with route-specific rooms and moderation tools, optimized for performance.
REWARDS & BENEFITS
Prize
First Prize - INR 15000 & Runner up - INR 10000
Scholarships
100% Scholarship from DataAstraa worth Rs.50K for courses
Free Access
Python Basic Courses worth Rs.20K
Recruiter Attention
Winners showcased on DataAstraa portal
Freelance
Opportunity to Freelance for DataAstraa
**Scholarships will have strict acceptance criteria, which may be over and above the hackathon results
PhD Students / Research Scholars
Graduate / Postgraduate Students
Undergraduate Students
WHO CAN PARTICIPATE
NOTE: Working professionals may not register for the Hackathon
Gain Mentorship from Industry Experts
Networking with like-minded Enthusiasts
Internship Opportunities**
WHY PARTICIPATE
**Internship opportunities are solely at discretion of the hiring companies based on performance and demand for the skills
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