Browsing by Author "Daniel, Ben"

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    Systematic review of the literature on big data in the transportation domain: concepts and applications
    (2019) Neilson, Alex; Indratmo, Indratmo; Daniel, Ben; Tjandra, Stevanus
    Research in Big Data and analytics offers tremendous opportunities to utilize evidence in making decisions in many application domains. To what extent can the paradigms of Big Data and analytics be used in the domain of transport? This article reports on an outcome of a systematic review of published articles in the last five years that discuss Big Data concepts and applications in the transportation domain. The goal is to explore and understand the current research, opportunities, and challenges relating to the utilization of Big Data and analytics in transportation. The review shows the potential of Big Data and analytics to garner insights and improve transportation systems through the analysis of various forms of data obtained from traffic monitoring systems, connected vehicles, crowdsourcing, and social media. We discuss some platforms and software architecture for the transport domain, along with a wide array of storage, processing, and analytical techniques, and describe challenges associated with the implementation of Big Data and analytics. This review contributes broadly to the various ways in which cities can utilize Big Data in transportation to guide the creation of sustainable and safer traffic systems. Since research in Big Data and transportation is, by and large, at infancy, this article does not prescribe recommendations to the various challenges identified, which also constitutes the limitation of the article.
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    The efficacy of stacked bar charts in supporting single-attribute and overall-attribute comparisons
    (2018) Indratmo, Indratmo; Howorko, Lee; Boedianto, Joyce Maria; Daniel, Ben
    Stacked bar charts are a visualization method for presenting multiple attributes of data, and many visualization tools support these charts. To assess the efficacy of stacked bar charts in supporting attribute comparison tasks, we conducted a user study to compare three types of stacked bar charts: classical, inverting, and diverging. Each chart type was used to visualize six attributes of data where half of the attributes have the characteristics of ‘lower better’ whereas the other half ‘higher better.’ Thirty participants were asked to perform two types of comparison tasks: single-attribute and overall-attribute comparisons. We measured the completion time, error rate, and perceived difficulty of the comparison tasks. The results of the study suggest that, for overall-attribute comparisons, the inverting stacked bar chart was the most effective with regards to the completion time. The results also show that performing overall-attribute comparisons using the classical and diverging stacked bar charts required more time than performing single-attribute comparisons using these charts. Participants perceived the inverting and diverging stacked bar charts as easier-to-use than the classical stacked bar chart for overall-attribute comparisons. However, for single-attribute comparisons, all chart types delivered similar performance. We discuss how these findings can inform the better design of interactive stacked bar charts and visualization tools.