This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. I. Introduction A ccording to a report on road safety published by World Health Organization in 2015, 1.25 million people died or were killed by traf- fic crashes or accidents. The report indicated that if no precautions were taken, traffic accidents leading to deaths would be one of the major causes of death [1]. Statistics show that the main factor in ac- cidents is driver-related behavior or activities, such as 89.6% [2]. All these reports indicate that analyz- ing the driver or driving behavior, monitoring driver activities, and training the driver are major issues to be discussed and required for proper solutions to re- duce traffic accidents and crashes. Big data analytics (BDA) is a recent approach to provide reasonable so- lutions to analyze and provide immediate results to data volume, velocity, variety, veracity, vulnerability and value (6Vs) issues if enough data, knowledge and infrastructures are available. Analyzing DDB consists of four main compo- nents to be considered: the driver, the vehicle, the intersection, and the environment, as shown by the following: ■ Drivers can generate physical and psychological signals. While driving, many sensors might help to Ramazan Terzi and Seref Sagiroglu Computer Engineering Department, Gazi University, Ankara, Turkey. E-mail: ramazanterzi@gazi.edu.tr, ss@gazi.edu.tr Mustafa Umut Demirezen STM Defense Co. and Gazi University, Ankara, Turkey. E-mail: udemirezen@gazi.edu.tr Big Data Perspective for Driver/Driving Behavior XXXXXX 1939-1390/19©2019IEEE IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE • 2 • MONTH 2019 Abstract—There are many articles published in driving/driver behavior (DDB) but few of them have fo- cused on DDB with big data in the literature. The reasons for this might be the lack of media coverage, data, expertise or big data perspectives. This paper presents a big data perspective for investigating the DDB based on models, data features, and experiences. For this purpose, DDB studies were reviewed and grouped into six perspectives. 6V’s of big data (volume, velocity, variety, veracity, vulnerability and value) were also revised and discussed how these V’s were compatible with DDB data. Finally, the use of big data in DDB analysis was discussed and some suggestions were presented. The lack of big data perception on DDB research was also overviewed and a new perspective was presented for researchers, applicants or business managers to do more effective and suitable studies in the field of big data DDB. Digital Object Identifier 10.1109/MITS.2018.2879220 Date of publication: xxxxxx