Fine-grained Reconstruction of Vehicle Trajectories Based on Electronic Registration Identification Data

Electronic registration identification technology (ERI) has developed rapidly in recent years. This technology has been widely used in large urban transportation monitoring, vehicle counting, identification, and traffic congestion detection. It has many advantages, such as long recognition distance, high recognition accuracy, more information stored, fast reading speed, etc. Currently this technology has achieved full coverage of the entire road network and vehicles in the cities where it is applied. Despite the richness of this data, there are significant limitations in terms of vehicle trajectories, especially in terms of spatial and temporal density. Compared with ERI trajectories, vehicle GPS trajectories have a higher sampling rate, but we are unable to obtain more comprehensive and complete vehicle GPS data due to the limitations of vehicle technology and security factors. In this paper, we innovatively propose a new method to reconstruct fine-grained ERI trajectories by learning from taxi GPS data. This approach can be divided into two steps. First, a novel Taxi-ERI traffic network is proposed to connect ERI data and taxi data. It’s a directed multi-graph whose nodes are consisted of all ERI acquisition points and edges are composed of clustered taxi trajectories. Then, the probability of each road is calculated by a Bayes classification based on the multi-road travel time distribution model while there are multi roads between two adjacent acquisition points, the model parameters are trained by the expectation maximization (EM) algorithm. Finally, we extensively evaluate the proposed framework on the taxi trajectory dataset and ERI data collected from Chongqing, China. The experimental results show that the method can accurately reconstruct vehicle trajectories.