Estimation of Travel Time Based on Ensemble Method with Multi-Modality Perspective Urban Big Data

Abstract

With the development of urban science, researches on mining of urban big data have attracted more and more attention. One typical microcosm of urban big data is taxi trajectory data. Predicting the travel time between the two specified points accurately is great significance for applications, such as travel plan. However, the current approach just uses limited modality data or single model without considering their one-sidedness. This paper puts forward to one optimized method to estimate travel time, which is based on ensemble method with multi-modality urban big data, namely Travel Time Estimation-Ensemble (TTE-Ensemble). First, we extract the feature sub-vectors from the multi-modality data as the model input. Then we use the gradient boosting decision tree (GBDT) model to process the low dimensional simple features and adopt the deep neural network (DNN) model to handle high dimensional underlying features. Finally, the ensemble method was introduced to integrate the two model of GBDT and the DNN. Extensive experiments were conducted based on real datasets of origin-destination points in Chengdu and Shanghai, China. These experiments demonstrate the superiority of the TTE-Ensemble model.