+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data



A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data



Accident; Analysis and Prevention 123: 274-281



According to NHTSA, more than 3477 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, not much effort has been directed to identify the types of secondary tasks from driving behavior parameters. This study proposes a bi-level hierarchical classification methodology using machine learning 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 to identify three types of secondary tasks (hand-held cellphone calling, cellphone texting, and interaction with an adjacent passenger). The inputs to the models are five driving behavior parameters (speed, longitudinal acceleration, lateral acceleration, pedal position, and yaw rate) along with their standard deviations. The results showed that the overall secondary task detection accuracy ranged from 66% to 96%, except for the Decision Tree that was able to detect engagement in secondary tasks with a high accuracy of 99.8%. For the identification of secondary tasks types, the overall accuracy ranged from 55% to 79%, with the highest accuracy of 82.2% achieved by the Random Forest method. The findings of the paper show the proposed methodology promising to (1) characterize drivers' engagement in unlawful secondary tasks (such as texting) as a counter measure to prevent crashes, and (2) alert drivers to pay attention back to the main driving task when risky changes to their driving behavior take place.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 066010735

Download citation: RISBibTeXText

PMID: 30554059

DOI: 10.1016/j.aap.2018.12.005


Related references

Covariability in three dimensions of teenage driving risk behavior: impaired driving, risky and unsafe driving behavior, and secondary task engagement. Traffic Injury Prevention 17(5): 441-446, 2016

Geographic Field Data Collection: Using machine learning techniques to verify minimum data requirements for the classification task. Journal of Geography 105(5): 636-648, 1996

Integrating data sources to improve hydraulic head predictions: a hierarchical machine learning approach. Water Resources Research 41(3): W03020, 2005

Emotional state classification from EEG data using machine learning approach. Neurocomputing 129: 94-106, 2014

Gene selection from microarray data for cancer classification--a machine learning approach. Computational Biology and Chemistry 29(1): 37-46, 2005

A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data. Sensors 19(6):, 2019

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. Omics 17(12): 595-610, 2013

A classification-based machine learning approach for the analysis of genome-wide expression data. Genome Research 13(3): 503-512, 2003

Diagnostic classification of specific phobia subtypes using structural MRI data: a machine-learning approach. Journal of Neural Transmission 122(1): 123-134, 2015

Identifying effects of driving and secondary task demands, passenger presence, and driver characteristics on driving errors and traffic violations Using naturalistic driving data segments preceding both safety critical events and matched baselines. Transportation Research Part F: Traffic Psychology and Behaviour 51: 103-144, 2017

A machine learning approach for the identification of protein secondary structure elements from cryoEM density maps. 2012

A machine learning approach to Cepheid variable star classification using data alignment and maximum likelihood. Astronomy and Computing 2: 46-53, 2013

A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps. Biopolymers 97(9): 698-708, 2012

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease. Neuroimage. Clinical 23: 101811, 2019

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data. Bmc Genomics 17(Suppl 13): 1025, 2016