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---
library_name: keras
tags:
- transportation
- eta-prediction
- time-series
- regression
- hong-kong
- tabular
framework: tensorflow
license: mit
widget:
- text: "View Demo in Space"
output:
url: "https://huggingface.co/spaces/WheelsTransit/HK-TransitFlow-Demo"
---
# HK-TransitFlow-Net
A Deep Neural Network for predicting bus travel times in Hong Kong.
**Accuracy:** ~64 seconds Mean Absolute Error.
**Coverage:** Trained on KMB and CTB routes.
The published version is mapped to used with [hk-bus-crawling](https://github.com/hkbus/hk-bus-crawling)
This is an open weight model, the source is not availible.
This is not Wheels Atlas, nor it is trained using the same way Atlas is.
## Inputs
The model accepts 5 inputs. All numerical inputs should be shaped `(N, 1)`.
1. **`distance`** (Float): Physical distance of the segment/trip in meters.
2. **`num_stops`** (Float): Number of stops in the trip.
3. **`hour`** (Int): Hour of the day (0-23).
4. **`day_of_week`** (Int): 0=Sunday, 1=Monday, ..., 6=Saturday.
5. **`route_id`** (String): The specific Route ID. If unknown, use `"UNKNOWN"`.
## Usage (Python)
```python
import tensorflow as tf
import numpy as np
from huggingface_hub import from_pretrained_keras
# 1. Download and Load
model = from_pretrained_keras("WheelsTransit/HK-TransitFlow-Net")
# 2. Prepare Data (Example: 5km trip, 8 stops, Mon 9AM)
# Note: Strings must be passed as tf.constant with dtype=tf.string
sample = {
'distance': np.array([[5000.0]], dtype='float32'),
'num_stops': np.array([[8]], dtype='float32'),
'hour': np.array([[9]], dtype='int32'),
'day_of_week': np.array([[1]], dtype='int32'),
'route_id': tf.constant([["UNKNOWN"]], dtype=tf.string)
}
# 3. Predict
prediction = model.predict(sample)
print(f"Predicted Duration: {prediction[0][0]:.2f} seconds") |