Abstract
Automatic number plate recognition (ANPR) systems, which have been widely equipped in many cities, produce numerous travel data for intelligent and sustainable transportation. ANPR data operate at an individual level and carry the unique identities of vehicles, which can support personalized traffic planning. However, these systems also suffer from the common problem of missing data. Different from the traditional missing cases, we focus on the problem of the loss of vehicle identities in ANPR records due to recognition failure or other environmental factors. To address the issue, we propose a heterogeneous graph embedding framework that constructs a travel heterogeneous information network (THIN) and learns the embeddings of the entities to find the best matched vehicles for the unknown records. As a result, the recovery of vehicle identities is cast as an entity alignment task on a THIN. The proposed method integrates the vehicle group entities and context relations into the THIN for capturing the spatiotemporal relationships in vehicle travel and adopts a holographic embeddings model for better fitting the network structure. Empirically, we test it with a real ANPR dataset collected from Xuancheng, China, which has a densely-distributed camera network. The experiments demonstrate the effectiveness of the proposed graph structure under different missing rates. Further, we compare it with other embedding methods and the results support the superiority of holographic embeddings.
Highlights
Nowadays, transportation systems have entered an era of data-driven intelligence [1,2] in order to alleviate the conventional but intractable problem of traffic congestion and further improve the efficiency
We propose an embedding-based framework with heterogeneous information networks (HINs) as the input for missing vehicle identity recovery
We organize these records as a travel heterogeneous information network according to the heterogeneous interactions which exist among the entities involved in vehicles’ travel
Summary
Transportation systems have entered an era of data-driven intelligence [1,2] in order to alleviate the conventional but intractable problem of traffic congestion and further improve the efficiency. Multisource traffic data should be fed into the intelligent transportation systems (ITS). High-quality traffic data can support a diversity of transportation applications like dynamic traffic forecasting [7], route planning [8] and accident warning [9]. Among those data sources, ANPR systems have attracted the most attention lately. The equipment is always installed at different directions of the intersections for violation monitoring and security surveillance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
