Learned Taxi Fare for real-life trip trajectories via Temporal ResNet Exploration

Abstract

Accurate taxi fare forecasting in complex and crowded scenarios is an important building block to enabling intelligent transportation systems in a smart city. Given the observation, increasing popularity of taxi services such as Uber and Didi Chuxing in China, unable to collect large-scale taxi fare data continuously. Traditional taxi fare prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. To address those issues, we propose a Deep Multi-View Network called Temporal ResNet (TRES-Net) framework. Specifically, our proposed model consists of three views: (i) temporal view: modeling correlations between future taxi fare values with near time points, (ii) spatial view: to model deep spatial correlations, we further introduce a spatial similarity matrix that can learn from spatially similar taxi trips and capture the multi-modality of the motion patterns, and (iii) semantic view: to extract more taxi fare patterns, we integrate more factors such as trip distance, travel time, passenger count, tolls amount, tip amount, etc.. Extensive experiments on more than 700 millions NYC trips over several fare prediction benchmarks demonstrate that our method is able to predict taxi fare in complex scenarios and achieves state-of-the-art performance. Our large scale evaluation demonstrates that our system is (a) accurate—with the mean fare error under 1 US dollar and (b) capable of real-time performance.