A Bi-Level Methodology for Identification of Types of Secondary Tasks from Observed Driving Behavior Data: Application of Ensemble Tree Machine Learning Algorithms on SHRP 2 NDS Data

According to the National Highway Traffic Safety Administration (NHTSA), more than 3,477 people (including 551 non-occupants) were killed and 391,000 were injured due to distraction-related crashes in 2015. The distracted driving epidemic has long been under research to identify its impact on driving behavior. There have been a few attempts to detect drivers’ engagement in secondary tasks from observed driving behavior. Yet, to the authors’ knowledge, there has not been any effort to identify the types of secondary tasks from driving behavior parameters. This study proposes a bi-level hierarchical classification methodology to identify the different types of secondary tasks drivers are engaged in using their driving behavior parameters. At the first level, drivers’ engagement in secondary tasks is detected, while at the second level, the distinct types of secondary tasks are identified. Comparative evaluation is performed between nine ensemble tree classification methods using five driving behavior parameters (speed, longitudinal acceleration, lateral acceleration, pedal position, and yaw rate) to identify three types of secondary tasks (cellphone calling, cellphone texting, and interaction with an adjacent passenger). The results show that the Decision Tree method detects drivers’ engagement in secondary tasks with a high accuracy of 99.3%, while the Random Forest method identifies the types of secondary tasks with an overall accuracy of 81.7%. The proposed methodology can be implemented to (1) characterize drivers’ engagement in unlawful secondary tasks (such as texting) before crashes, and (2) alert drivers to pay attention back to the main driving task when risky changes to their driving behavior take place.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ00 Section - Data and Information Systems.
• Authors:
  • Osman, Osama A
  • Hajij, Mustafa
  • Karbalaieali, Sogand
  • Ishak, Sherif
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 5p

Subject/Index Terms

• TRT Terms: Algorithms; Automatic data collection systems; Behavior; Decision trees; Distraction; Drivers; Machine learning; Methodology
• Identifier Terms: Strategic Highway Research Program 2
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01658664
• Record Type: Publication
• Report/Paper Numbers: 18-03944
• Files: TRIS, TRB, ATRI
• Created Date: Jan 31 2018 4:58PM