As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country.

The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide owners with dynamic and accurate travel time prediction services to assist the owners to formulate more reasonable travel plans. However, there are still some problems in the current travel time prediction research (e.g., different types of vehicles are not processed separately, the proximity of the road network is not considered, and the capture of important information in the spatial-temporal perspective is not considered in depth). In this paper, we propose a Multi-View Travel Time Prediction (MVPPT) model. First, the travel times of different types of vehicles of each section in the expressway are analyzed, and the main differences in the travel times of different types of vehicles are obtained. Second, multiple travel time features are constructed, which include a novel spatial proximity feature. On this basis, we use CNN to capture the spatial correlation and the spatial attention mechanism to capture key information, the BiLSTM to capture the time correlation of time series, and the time attention mechanism capture key time information. Experiments on large-scale real traffic data demonstrate the effectiveness of our proposal over state-of-the-art methods.