A Review of Big Data in Road Freight Transport Modeling–Gaps and Potentials

Wasim Shoman, Sonia Yeh, Francis Sprei, Jonathan Köhler, Patrick Plötz, Yancho Todorov, Seppo Rantala, Daniel Speth

    Research output: Contribution to journalReview Articlepeer-review


    Road transport accounted for 20% of global total greenhouse gas emissions in 2020, of which 30% come from road freight transport (RFT). Modeling the modern challenges in RFT requires the integration of different freight modeling improvements in, e.g., traffic, demand, and energy modeling. Recent developments in 'Big Data' (i.e., vast quantities of structured and unstructured data) can provide useful information such as individual behaviors and activities in addition to aggregated patterns using conventional datasets. This paper summarizes the state of the art in analyzing Big Data sources concerning RFT by identifying key challenges and the current knowledge gaps. Various challenges, including organizational, privacy, technical expertise, and legal challenges, hinder the access and utilization of Big Data for RFT applications. We note that the environment for sharing data is still in its infancy. Improving access and use of Big Data will require political support to ensure all involved parties that their data will be safe and contribute positively toward a common goal, such as a more sustainable economy. We identify promising areas for future opportunities and research, including data collection and preparation, data analytics and utilization, and applications to support decision-making.
    Original languageEnglish
    Number of pages16
    JournalData Science for Transportation
    Issue number2
    Publication statusPublished - 21 Feb 2023
    MoE publication typeA2 Review article in a scientific journal


    • big data
    • big data analytics
    • road freight transport
    • transport modeling


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