Dataset Information

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior.

ABSTRACT: The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.

SUBMITTER: Stavrakaki AM

PROVIDER: S-EPMC7248787 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior.

Stavrakaki Anna-Maria AM Tselentis Dimitrios I DI Barmpounakis Emmanouil E Vlahogianni Eleni I EI Yannis George G

Sensors (Basel, Switzerland) 20200502 9

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to ...[more]

PMID: 32370264

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Project description:ObjectiveThere is a substantial body of evidence that the recidivism of impaired-driving offenders is reduced while an ignition interlock device (IID) is on their vehicles. This study examines changes in driving behaviors and drinking behaviors used by DWI offenders to manage driving with the IID.MethodsA total of 166 IID participants who completed two surveys covering the period from arrest to IID installation (T1) and during IID use (T2) were examined. Four domains were covered: demographics, driving environments and transportation needs, reported driving activity, and reported drinking activities. Participants were on average 38 years old, 43% were female, 35% completed college, 34% had an income of more than $50,000, and 83% were employed. For those who provided it, the mean blood alcohol content (BAC) at arrest was .184 g/dL, with only 8 (5%) individuals below .08 g/dL, and 93 (56%) at over .18 g/dL. About 45% were repeat DWI offenders.ResultsBetween T1 and T2 there was a slight increase in acknowledging public transportation was available (p=.001), an increase in other individuals driving the interlock-equipped vehicle (p=.002), an increase in the number of vehicles in the household not registered to the DWI offender (p< .001), and an increase in the number of participants who reported that driving was important to their lifestyle (p=.008). Initial (T1) expectations about whether the interlock would be a problem were significantly different from actual experiences reported in T2 (p<.001). With respect to alcohol consumption, 14% reported abstinence at T2 compared to 2% at T1 (p=.001) and the number of drinks per drinking occasion decreased from a mean of 4.90 at T1 to 3.14 at T2 (p=.001), but the number of drinking occasions increased by a third (p=.003). The number of drinking locations (p=.001), the frequency of stopping after work for a drink (p=.001), and drinking at a bar all decreased (p<.001).ConclusionsInterlock users make some adjustments in how they drink, the amount they drink, and where they drink. This finding suggests that there may be methods that can be used to extend the benefits of the IID beyond the sanction period.

Project description:The recently developed magnetic resonance imaging (MRI) proton density fat fraction (PDFF) allows measurement of the fat in all segments of hepatic tissue. However, it is time consuming and inconvenient to measure each segment repeatedly. Moreover, volume of each segment also should be adjusted with arithmetic mean of the selected segments when total amount of liver fat is estimated. Therefore, we try to develop a clinically-relevant and applicable method of estimating hepatic fat in PDFF image.A total of 164 adults were enrolled. We addressed the measurement frequency and segment selection to determine the optimal method of measuring intrahepatic fat. Total hepatic fat was estimated by the weighted mean of each segment reflecting their respective segmental volumes. We designed 2 models. In Model 1, we determined the segment order by which the mean was closest to the whole weighted mean. In Model 2, we determined the segment order by which the arithmetic mean of the selected segments was closest to the whole weighted mean.Fat fraction (FF) was most important risk factor of hepatic heterogeneity in multivariable analysis (β = 0.534, P < .001). In severe fatty liver (FF > 22.1%), intrahepatic fat variability was 2.47% (1.16-6.26%). The arithmetic mean total intrahepatic FF was 12.66%. But the weighted mean that applied to each segmental volume was 12.90%. In Model 1, arithmetic mean of segments 4 and 5 was closest to the total estimated hepatic fat amount. However, when we added segment 8, the mean of segments 4, 5, and 8 was significantly different from the estimated total hepatic fat amount (P = .0021). In Model 2, arithmetic mean of segments 4 and 5 was closest to the total estimated hepatic fat amount. There was a significant reduction in variability between segment 4 and segments 4 and 5 (P < .0001).Averaging the mean hepatic FF of segments 4 and 5 was the most reasonable method for estimating total intrahepatic fat in practice.

Dataset Information

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior.

Publications

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior.

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