A similarity query system for road traffic data based on a NoSQL document store

Advancements in sensing and communication technologies are enabling intelligent transportation systems (ITS) to easily acquire large volumes of road traffic big data. Querying road traffic data is a crucial task for providing citizens with more insightful information on traffic conditions. In this paper, we have developed a similarity query system for road traffic big data, called SigTrac, that runs on top of an existing MongoDB document store. The SigTrac system represents road traffic sensor data having spatio-temporal characteristics into traffic matrices and stores them into a MongoDB NoSQL document store by exploiting map-reduce operations of MongoDB. In addition, SigTrac efficiently processes similarity queries for traffic data with singular value decomposition (SVD)-based and incremental SVD-based algorithms. Our experimental studies with real traffic data demonstrate the efficiency of SiqTrac for similarity query processing for road traffic big data.