Author:
Shoman Wasim,Yeh Sonia,Sprei Frances,Köhler Jonathan,Plötz Patrick,Todorov Yancho,Rantala Seppo,Speth Daniel
Abstract
AbstractRoad 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.
Funder
European Union's Horizon 2020 research and innovation program
Chalmers University of Technology
Publisher
Springer Science and Business Media LLC
Reference105 articles.
1. ACEA and T&E, 2021. Zero-emission trucks: Industry and environmentalists call for binding targets for infrastructure.
2. AEOLIX, 2022. AEOLIX [WWW Document]. URL https://aeolix.eu/ Accessed 4 Nov 2022).
3. Alho AR, You L, Lu F, Cheah L, Zhao F, Ben-Akiva M (2018) Next-generation freight vehicle surveys: Supplementing truck GPS tracking with a driver activity survey. IEEE Conf Intell Transp Syst. 22:2974–2979. https://doi.org/10.1109/ITSC.2018.8569747
4. Arias MB, Bae S (2016) Electric vehicle charging demand forecasting model based on big data technologies. Appl Energy 183:327–339. https://doi.org/10.1016/j.apenergy.2016.08.080
5. Asatiani A, Malo P, Nagbøl PR, Penttinen E, Rinta-Kahila T, Salovaara A (2020) Challenges of explaining the behavior of black-box AI systems. MIS Q Exec 19:259–278
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