Big data platform of traffic violation detection system: identifying the risky behaviors of vehicle drivers

Abstract

Since the traffic data has a high volume, high diversity and high speed of production, the traditional systems cannot process them accurately. In this paper, a big data based system was designed and implemented for identifying the offenders’ behaviors. The proposed Traffic Violation Detection System (TVD system) included four main phases which were described using the MAPE methodology. In the monitor phase, unstructured data, such as videos captured by traffic control cameras as well as the images and the descriptions provided by traffic officers, were collected. In the analysis phase, the knowledge base of unsafe driving behaviors was created and classified. In this phase, a standard Work Breakdown Structure for unsafe behaviors was created by the experts in traffic control. In the Plan phase, in order to detect unsafe driving behaviors related to police descriptions for collected images, and to detect unsafe behaviors from video cameras, Behavior-Based Safety process with Map/ Reduce technique and Vector Space Model (VSM) were employed. In the last phase, all types of data, including the structured data and the multimedia/unstructured data, along with the types and details of violations, were stored on the Hadoop Distributed File System. The prototype of the proposed TVD system was successfully implemented for a few common violations. The results showed that by applying big data technologies, the driving violations could be detected more accurately using the combination of the structured and unstructured data. The results indicate that compared to the sequential program, Hadoop only with a single slave-node decreases the processing time of big data by more than 70%. Also, by increasing the number of slave nodes from 1 to 7 in the police descriptions and images of surveillance cameras, the processing time reduces by 60.87% and 70%, respectively. Thus, the TVD performance increases by more than 75% as the number of data nodes boosts. Based on the results, it can be concluded that by identifying unsafe driving behaviors, it is possible to diminish traffic accidents and the damage caused by them at a satisfactory level. Also, the authors decided to compare these studies in terms of qualitative criteria, such as fieldwork and behavioral identification, and quantitative criteria.