TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Prediction

Estimated time of arrival (ETA) is a critical component of intelligent transportation systems. Though many tools exist for ETA, ETA for special vehicles, such as ambulances, fire engines, etc., is still challenging due to the limited amount of traffic data for special vehicles. Existing works use one model for all types of vehicles, which can lead to low accuracy. To tackle this, as the first in the field, we propose a deep transfer learning framework TLETA for the driving time prediction. TLETA constructs cellular spatial-temporal knowledge grids for extracting driving patterns, combined with the road network structure embedding to build a deep neural network for ETA. TLETA also supports knowledge transfer between different categories of vehicles. Importantly, our transfer models only train the last layers to map the transferred knowledge, which reduces the training time significantly. The experimental studies show that our model outperforms many state-of-the-art approaches in training efficiency and prediction accuracy.