Driving analytics using smartphones: Algorithms, comparisons and challenges. (June 2017)

• Record Type: Journal Article
• Authors: Vlahogianni, Eleni I.; Barmpounakis, Emmanouil N.
• Is Part Of: Transportation research. Volume 79(2017)
• Page Start: 196
• Page End: 206
• Publication Date: 2017-06
• Subjects: Driver's behavior -- Critical driving patterns -- Smartphones -- Rough set theory -- Machine learning; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.03.014
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: The use of smartphones as an alternative for driving behavior analysis is examined. The accuracy of smartphones is compared to fixed position devices. Rough set theory is used to identify thresholds and detect harsh events. Free and Controlled Driving experiments are conducted for evaluation. Smartphones can accurately detect harsh longitudinal and lateral driving patterns. Abstract: The present work investigates the use of smartphones as an alternative to gather data for driving behavior analysis. The proposed approach incorporates i. a device reorientation algorithm, which leverages gyroscope, accelerometer and GPS information, to correct the raw accelerometer data, and ii. a machine-learning framework based on rough set theory to identify rules and detect critical patterns solely based on the corrected accelerometer data. To evaluate the proposed framework, a series of driving experiments are conducted in both controlled and "free-driving" conditions. In all experiments, the smartphone can be freely positioned inside the subject vehicle. Findings indicate that the smartphone-based algorithms may accurately detect four distinct patterns (braking, acceleration, left cornering and right cornering) with an average accuracy comparable to other popular detection approaches based on data collected using a fixed position device.