InanisV / TrafficPrediction-VGG

Traffic Flow Prediction with Convolutional Neural Networks (VGG16)

TrafficPrediction-VGG

Traffic Flow Prediction with Convolutional Neural Networks (VGG16)

Brief Intro

This implementation predicts the speed of cars in the next five minutes based on past two hours.

The test database is PeMSD7, a database collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations.

To notice that VGG only takes temporal information into consideration.

Usage Explanation

Dataset.py

dataset(data_v, time_slot, predict_slot, batch_size)
# data_v        -- the input metrix with road_num x time
# time_slot     -- the number of time slots used for prediction
# predict_slot  -- the number of time slots to predict
# batch_size    -- batch size

VGG16.py

VGG16(num_classes)
# num_classes   -- the number of roads to predict

VGG_main.py