Deep Encoder Cross Network for Estimated Time of Arrival

Abstract

<p>Estimated time of arrival (ETA) is essential to enable various intelligent transportation services and reduce passenger waiting time. Estimating the time of arrival of public transport in a highly dynamic and uncertain transportation system could be challenging. Many indirect factors beyond the remaining travel distance could dramatically deviate the time of arrival from the original schedule. Existing distance-based estimation methods disregarding those factors usually result in inaccurate estimations. In this paper, we propose a new deep learning model, called Deep Encoder Cross Network (DECN), to improve the ETA prediction based on multiple non-distance-based factors such as weather, road speed and congestion, and traffic composition. Unlike most regression tasks that output the target directly, we predict the ETA residual over the location-based ETA prediction. To effectively learn in the large and sparse input feature space, we use a new neural network structure consisting of three main components. First, a deep neural network is responsible for modeling explicit feature interactions. Second, an encoder network is constructed to reduce the input feature dimensionality. Third, a cross-network is introduced to learn from the implicit feature interactions. We conduct extensive experiments on a large real-world bus ETA dataset of Hong Kong, which contains about 2.95×108 rows with 27 different features on an 84-dimensional space. The results show that the deep learning approach with the DECN model can improve the ETA error by 11% on average, and 49% for late arrival. The proposed approach can be further improved and extended to estimate other traffic information by incorporating non-distance-based related information.<br></p>