Automation of the Validation, Anonymization, and Augmentation of Big Data from a Multi-year Driving Study

Wallace, Bruce; Goubran, Rafik; Knoefel, Frank; Porter, Michelle M.; Harlow, Madelaine; Puli, Akshay

doi:10.1109/bigdatacongress.2015.93

Cited by 15 publications

(14 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CanDrive) that have published driving data from several years of data collection 20 . Some studies have used radio frequency identification tags to identify the driver and anonymize home locations of their participants 21 . Driver identification was limited to participant self-report.…”

Section: Discussionmentioning

confidence: 99%

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Babulal¹,

Traub

Webb

et al. 2016

F1000Res

View full text Add to dashboard Cite

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking Device TM was installed in each participant’s vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven. Setting: The Knight Alzheimer’s Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance. Participants were aged 65+ years and had normal cognition. Measurements: Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations. Temporal components included number of trips taken during different times of the day. Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods: Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS. Results: Primary locations were confirmed to be 100% accurate when compared to known addresses. Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns. Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night. Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Babulal¹,

Traub

Webb

et al. 2016

F1000Res

View full text Add to dashboard Cite

show abstract

“…In addition the OBDII connection provides power to the sensor system. The processing to ready the data for analysis [24] included techniques required to validate, anonymize and augment the data with additional information such as posted speed limit from geographic information systems.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…This includes the tendency a driver has to make trips at similar times of day, and measures of the percentage of a trip that occurs in each of the 24 hours of the day and also for each of the days of the week [18]. Drivers may also adjust their driving patterns based on other influences, such as avoiding high traffic that occurs at rush hour [22] or avoiding driving when it is dark [24]. The northern latitude location of Ottawa results in a large variation of the solar day over the year.…”

Section: Trip Features Measuredmentioning

confidence: 99%

See 1 more Smart Citation

Features that Distinguish Drivers: Big Data Analytics of Naturalistic Driving Data

2017

STBD

View full text Add to dashboard Cite

The unique behaviours of drivers have many emerging applications. These include the personalization of automated/selfdriving vehicles so the owners are more comfortable with them, and the identification of changing driving behaviours that may be associated with aging or disease. This paper explores measures of driving behaviours that might allow for the differentiation of drivers based on their individual driving characteristics. An emerging challenge within longitudinal studies of drivers is to distinguish between different drivers of a shared vehicle. It also has application in the insurance industry where insurance risk and associated owner premium depends on the diversity or lack thereof of drivers for a vehicle such as a vehicle driven/never driven by secondary drivers that have higher risk driving behaviours. In this paper, a big data set of driving data for 14 older drivers is analyzed -a single year of data includes over 250,000 km and almost 5000 hours of driving for the 14 drivers. A set of 162 trip level calculated features are analyzed to determine their ability to be used to distinguish between two drivers of a vehicle. The results show that features based on road choice and driver chosen velocity provide the best performance individually and in feature pairs with 2 features providing error rates less than 5% for some driver pairs. The set of features that provided the best performance differed for each driver pair and was found to include features from measures of a driver's road choice, velocity and velocity ratio in addition to the features measuring trip similarity to two phase acceleration and deceleration relationships for the driver. The best error rate obtained was 1.5% for a driver pair. On the other hand, the results suggest that a number of features and feature groups do not allow for older driver differentiation. For instance, overnight driving and high rates of acceleration are not sufficiently exhibited by these drivers to be useful.

show abstract

“…The study of the ongoing performance of IADLs through sensors leads to the capture of longitudinal data for the patient and these datasets can become quite large as they capture the evolution of the patient's ability over months and even years. The work presents contributions that address the needs for the preparation of the large datasets for analysis including quality assurance to ensure data integrity, anonymization techniques to ensure privacy and augmentation of longitudinal sensor data post capture from reference data sources such as digital map data-bases (refereed conferences [11,20] and journals [10]).…”

Section: Big Data Analyticsmentioning

confidence: 99%

An Integrated Approach for Episodic Cognition Assessment

Wallace¹

View full text Add to dashboard Cite

Dementia and Mild Cognitive Impairment (MCI) are significant health issues that are a rising cost to society. They are monitored with cognitive tests during clinical appointments that are limited by the healthcare system capacity and patient's ability/willingness to attend. Current cognitive tests use behavioural measures and not direct measures of underlying cellular change. This causes delay in This work could never have been possible without the inspiration, support and acceptance of the long hours of work from my family including

show abstract

Automation of the Validation, Anonymization, and Augmentation of Big Data from a Multi-year Driving Study

Cited by 15 publications

References 24 publications

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Features that Distinguish Drivers: Big Data Analytics of Naturalistic Driving Data

An Integrated Approach for Episodic Cognition Assessment

Contact Info

Product

Resources

About