Driving analytics using smartphones: Algorithms, comparisons and challenges

Vlahogianni, Eleni I.; Barmpounakis, Emmanouil

doi:10.1016/j.trc.2017.03.014

Cited by 103 publications

(48 citation statements)

References 26 publications

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“…The potential for orientation sensors to be used in a moving platform, like planes or vehicles, has been investigated based on static simulators [17]. A limited number of studies have also reported the use of small-scale inertia measurement units in a moving platform [20,21,22]. Solutions which use this approach to detect head movement, with an accuracy of <1° per axis, are commercially available [23], but they are costly and usually do not work on a moving platform.…”

Section: Methodsmentioning

confidence: 99%

An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring

Zhao

Görne

Yuen

et al. 2017

Sensors

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Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving activities (NDAs) is crucial to decide how well the driver will be able to take over control of the vehicle. One limitation of the commonly used face-based head tracking system, using cameras, is that sufficient features of the face must be visible, which limits the detectable angle of head movement and thereby measurable NDAs, unless multiple cameras are used. This paper proposes a novel orientation sensor based head tracking system that includes twin devices, one of which measures the movement of the vehicle while the other measures the absolute movement of the head. Measurement error in the shaking and nodding axes were less than 0.4°, while error in the rolling axis was less than 2°. Comparison with a camera-based system, through in-house tests and on-road tests, showed that the main advantage of the proposed system is the ability to detect angles larger than 20° in the shaking and nodding axes. Finally, a case study demonstrated that the measurement of the shaking and nodding angles, produced from the proposed system, can effectively characterise the drivers’ behaviour while engaged in the NDAs of chatting to a passenger and playing on a smartphone.

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Section: Methodsmentioning

confidence: 99%

An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring

Zhao

Görne

Yuen

et al. 2017

Sensors

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show abstract

“…To overcome the influence of data noise, Thierry, Chaix, and Kestens () employed a kernel density‐based approach to identify activity locations. The concept of trajectory segmentation used in these approaches has also been extended to support many other studies, such as travel mode detection and driver behavior analysis (Vlahogianni & Barmpounakis, ; Zhou, Yu, & Sullivan, ). However, the performance of these approaches depends on the data quality of trajectories.…”

Section: Related Workmentioning

confidence: 99%

Identifying stops from mobile phone location data by introducing uncertain segments

Zhao

Yin²,

Shaw

et al. 2018

Transactions in GIS

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Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves.” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management.

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“…Vlahogianni and Barmpounakis [13] investigate smartphone based analytics for driving behavior assessment. As mentioned by Vlahogianni and Barmpounakis, there is little knowledge about reliability of such smartphone based sensing except for the fact that GPS (in a phone) and accelerometer continue to remain a popular choice.…”

Section: Introductionmentioning

confidence: 99%

Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

Chowdhury

Chakravarty

Ghose

et al. 2018

Journal of Advanced Transportation

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Driver identification is an emerging area of interest in vehicle telematics, automobile control, and insurance. Recent body of works indicates that it may be possible to uniquely identify a driver using multiple dedicated sensors. In this paper, we present an approach for driver identification using smartphone GPS data alone. For our experiments, we collected data from 38 drivers for two months. We quantified the driver's natural style by extracting a set of 137 statistical features from data generated for each completed trip. The analysis shows that, for the "driver identification" problem, an average accuracy of 82.3% is achieved for driver groups of 4-5 drivers. This is comparable to the state of the arts where mostly a multisensor approach has been taken. Further, it is shown that certain behavioral attributes like high driving skill impact identification accuracy. We observe that Random Forest classifier offers the best results. These results have great implications for various stakeholders since the proposed method can identify a driver based on his/her naturalistic driving style which is quantified in terms of statistical parameters extracted from only GPS data.

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Driving analytics using smartphones: Algorithms, comparisons and challenges

Cited by 103 publications

References 26 publications

An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring

An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring

Identifying stops from mobile phone location data by introducing uncertain segments

Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

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