Md. Moshiur Rahman
ABSTRACT
Accurate traffic flow prediction is a keystone for building intelligent traffic management systems which have gained attention from researchers because of the availability of the massive volume of traffic data and advances in deep learning technologies. However, there are many cities in the world, that suffer from terrible traffic congestion but there are no infrastructure facilities to collect traffic data. To address this problem we develop a tool that collects traffic data from Google Maps without using its paid API. After that, we proposed an Attention-based Deep Hybrid network (ADHN) for traffic flow prediction using Google map data. The proposed ADHN combines two Convolutional Long Short-Term Memory (ConvLSTM) to capture dynamic spatial temporal dependencies of the traffic flow and applies attention mechanism on traffic features. The experiment result shows that our proposed ADHN can provide higher prediction accuracy compared with the other state-of-the-art approaches. Our code and data are available at https://github.com/Moshiurcse13/trafficDataCollectionTool.
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Index Terms
- Attention based Deep Hybrid Networks for Traffic Flow Prediction using Google Maps Data
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