# Gridlock Hackathon 2.0

**Platform:** Flipkart  
**Status:** LIVE  
**Duration:** May 26, 2026, 10:00 PM IST (Asia/Kolkata) – Jun 07, 2026, 11:59 PM IST (Asia/Kolkata)

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## Problem: Traffic Demand Prediction

**Max score:** 100

### Background

Cities worldwide are increasingly turning to AI-powered solutions to tackle traffic congestion. This problem disrupts the smooth flow of transportation and poses a significant barrier to economic growth. To address this challenge effectively, the first step is to understand travel demand and patterns within urban areas comprehensively. By harnessing the power of AI, cities and regions aim to gather critical insights into transportation dynamics. This will enable them to implement data-driven strategies and solutions to alleviate traffic congestion and promote more efficient mobility. Ultimately, this endeavor will foster economic development and prosperity.

### Task

Design a system that helps us provide valuable insights into passenger travel patterns, booking behavior, and trip cancellations, which can be used for various analyses and predict demand in the travel industry.

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## Dataset

### Files

| File | Shape |
|------|-------|
| `train.csv` | 77299 × 11 |
| `test.csv` | 41778 × 10 |
| `sample_submission.csv` | 5 × 2 |

### Variable Descriptions

| Column | Description |
|--------|-------------|
| `Index` | Unique identification of datapoint |
| `geohash` | Geographic information regarding a place |
| `day` | Day when the information is recorded |
| `timestamp` | Timestamp of the record inserted in the system |
| `RoadType` | Type of road in the nearby location |
| `NumberofLanes` | Number of roads/lanes present in the location |
| `LargeVehicles` | Whether large vehicles are permitted on the specific roads/lanes |
| `Landmarks` | Whether there are any landmarks near the location |
| `Temperature` | Temperature of the place |
| `Weather` | Weather of the place |
| `demand` | **TARGET** — Demand of traffic at the timestamp (train only) |

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## Evaluation Metric

```python
score = max(0, 100 * metrics.r2_score(actual, predicted))
```

- Higher R² → higher score
- Score clamped to minimum 0 (negative R² counts as 0)
- Perfect predictions → score of 100

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## Submission Requirements

- **Prediction file format:** CSV only
- **Submission size:** 41778 rows × 2 columns
- **Required columns:**
  - `Index` — correct index values as per the test file
  - `demand` — predicted demand values
- Column names must match `sample_submission.csv` exactly

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## How to Submit

1. Download dataset from the problem page
2. Solve the problem in your local environment
3. Save predictions in a `.csv` file
4. Upload prediction file via **Upload Prediction File → Choose File → Submit & Evaluate**
5. Upload source code via **Upload Source Files** (zip or tar archive containing):
   - Text file explaining your approach
   - Details about feature engineering
   - Tools used
   - Relevant source files (`.ipynb` + any presentation file)
6. Add instructions/comments in the **Your Answer** section
7. Click **Submit**

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## Key Notes

- **Dataset origin:** Based on the Grab AI for SEA – Traffic Management challenge (public Kaggle dataset). Original Grab data used `geohash6` + `day` + `timestamp` + `demand`.
- **Scoring:** R² is computed between actual and predicted `demand` on the test set server-side.
- **Leaderboard:** Live scoring visible on the Leaderboard tab.
- **Source upload:** Required alongside prediction CSV — judges review approach + feature engineering.