A traffic monitoring stream-based real-time vehicular offence detection approach

Traffic offences are becoming increasingly serious as traffic volume increases rapidly in large cities, causing serious property damage and threatening public safety. Existing traffic monitoring systems lack the capability of detecting various types of offences in real-time. This paper proposes a novel monitoring stream-based vehicular offence detection algorithm, which discovers various types of offences from high-throughput traffic monitoring stream in real-time. An offence detecting and monitoring system is also designed and implemented. In order to achieve real-time detection, parallel computing techniques are utilized. An optimized data structure, a one producer-multiple consumer model and a re-hash strategy are proposed to reduce the synchronization cost incurred by multiple threads in the parallel implementation. Both real-world data and synthetic data are applied in the experiments. Experimental results demonstrate that the proposed algorithm is able to discover three types of offences from high-throughput traffic monitoring stream in real-time. Scalability is also observed. The experimental results indicate that the proposed system is sufficiently efficient to provide real-time offence detection for major metropolises.