Recurrent Autoencoder with Skip Connections and Exogenous Variables for Traffic Forecasting
Preprint
Abstract
The increasing complexity of mobility plus the growing population in cities, together with the importance of privacy when sharing data from vehicles or any device, makes traffic forecasting that uses data from infrastructure and citizens an open and challenging task. In this paper, we introduce a novel approach to deal with predictions of speed, volume, and main traffic direction, in a new aggregated way of traffic data presented as videos. The approach leverages the continuity in a sequence of frames and its dynamics, learning to predict changing areas in a low dimensional space and then, recovering static features when reconstructing the original space. Exogenous variables like weather, time and calendar are also added in the model. Furthermore, we introduce a novel sampling approach for sequences that ensures diversity when creating batches, running in parallel to the optimization process.Keywords
All Related Versions
This publication has 4 references indexed in Scilit:
- Multi-modal Sequence to Sequence Learning with Content Attention for Hotspot Traffic Speed Prediction2018
- Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask LearningIEEE Transactions on Intelligent Transportation Systems, 2014
- Practical Recommendations for Gradient-Based Training of Deep Architectures2012
- Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithmNeurocomputing, 2011