README.md

metadata

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

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)

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")