Do you know the probability of traffic incident in the future? Let us to evaluate it.

A Novel Spatio-Temporal Generative Inference Network for Predicting the Long-Term Highway Traffic Speed

This work considers combine multi-tricks with highway network to achieve traffic flow prediction accurately.

Welcome to quote our published papers, and the codes have been uploaded.

Using to predict the highway traffic speed

Config files for my GitHub profile.

When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time

sharing traffic data for our research direction, there have traffic speed, flow, incident, etc,.

this page used to proved the project processing

An Optimized Spatiotemporal Encoder-Decoder Neural Network for Ride-Sourcing Demand Prediction

In this study, we propose an end-to-end deep learning model-RCL-Learning that integrates ResNet and ConvLSTM.

Our model utilized the muti-cities historical air pollutant and meteorological data to predict future pollutant concentration

A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction

Knowledge graph embedding

weather and pollutants