A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data

Badu-Marfo, Godwin; Farooq, Bilal; Patterson, Zachary

doi:10.1007/s42421-019-00001-z

“…Similarly, comparisons of several big data sources on the same routes show significant differences, representing a fraction of total travel (Griffin and Jiao 2015a). Big data often results as a 'residue' of electronic activity, may have substantial privacy issues, and rarely aligns with specific urban problems without substantial processing (Batty 2016;Badu-Marfo et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

¹

,

Mulhall

²

,

Simek

³

et al. 2020

J. Big Data Anal. Transp.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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“…Similarly, comparisons of several big data sources on the same routes show significant differences, representing a fraction of total travel (Griffin & Jiao, 2015a). Big data often results as a 'residue' of electronic activity, may have substantial privacy issues, and rarely aligns to specific urban problems without substantial processing (Badu-Marfo, Farooq, & Patterson, 2019;Batty, 2016).…”

Section: Introductionmentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

¹

,

Mulhall

²

,

Simek

³

et al. 2020

Preprint

View full text Add to dashboard Cite

Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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“…For relatively large datasets (5 GB or more), it is challenging, if not impossible, to achieve real-time visual updates with conventional visual analytic platforms. Recent developments aimed at handling big transportation data leverages high-performance computing clusters in the back end for all the heavy-lifting computations including data ingestion, aggregation, integration and reduction (Badu-Marfo et al 2019;Islam and Sharma 2019). The filtered, aggregated and lightweight data are subsequently pushed to the front end for visual exploration.…”

Section: Introductionmentioning

confidence: 99%

GPU-Enabled Visual Analytics Framework for Big Transportation Datasets

Adu-Gyamfi

¹

2019

J. Big Data Anal. Transp.

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Transportation agencies rely on a variety of data sources for condition monitoring of their assets and making critical decisions such as infrastructure investments and project prioritization. Recent exponential increase in the volumes of these datasets has been causing significant information overload problems for data analysts; data curation process has increasingly become time consuming as legacy CPU-based systems are reaching their limits for processing and visualizing relevant trends in these massive datasets. There is a need for new tools that can consume these new datasets and provide analytics at rates resonant with the speed of human thought. The current paper proposes a new framework that allows for both multidimensional visualization and analytics to be carried seamlessly on large transportation datasets. The framework stores data in a massively parallel database and leverages the immense computational power available in graphical processing units (GPUs) to carry out data analytics and rendering on the fly via a Structured Query Language which interacts with the underlying GPU database. A front-end is designed for near-instant rendering of queried results on simple charts and maps to enable decision makers to drill down insights quickly. The framework is used to develop applications for analyzing big transportation datasets with over 100 million rows. Performance benchmarking experiments conducted showed that the methodology developed is able to provide real-time visual updates for big data in less than 100 ms. The performance of the developed framework was also compared with CPU-based visual analytics platforms such as Tableau and D3.

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A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data

Cited by 20 publications

References 105 publications

Mitigating Bias in Big Data for Transportation

Mitigating Bias in Big Data for Transportation

Mitigating Bias in Big Data for Transportation

GPU-Enabled Visual Analytics Framework for Big Transportation Datasets

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