MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting

Predicting traffic flow in large cities is beneficial for a wide range of applications, including vehicle navigation services, vehicle routing, and traffic congestion management. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn long-term dependencies. However, these neural networks only learn the temporal traffic information for each trajectory (moving object), failing to take advantage of spatial information shared by neighboring trajectories. This paper introduces MTL-LSTM (Multi-Task Learning-based LSTM) traffic flow estimator, which attempts to explore both temporal and spatial dependencies among adjacent trajectories. Specifically, we employ LSTM predictors with the MTL approach to explore traffic flow patterns across urban trajectories. To examine the proposed model, we predict traffic flow in Porto's city using a data set from buses and taxies. Our experiments show improvements of 10% to 15% over the state-of-the-art.