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README.md

@@ -1,3 +1,52 @@
----
-license: mit
----
+---
+library_name: keras
+tags:
+- transportation
+- eta-prediction
+- time-series
+- regression
+- hong-kong
+- tabular
+framework: tensorflow
+license: mit
+---
+
+# 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.
+
+## 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 GTFS Route ID (e.g., `968+1+...`). 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")