Computational Urban Science - Call for papers: Promoting Computational Advanced Data Fusion for Urban Computing
The following special issue in Computational Urban Science is open for submissions. The submission deadline is May 31, 2023. The manuscript can be submitted at any time before the deadline. Once it is accepted (after peer review), it will be published online immediately with open access and social media promotion.
Call for papers:
Promoting Computational Advanced Data Fusion for Urban Computing
Guest Editors:
Dr. Subasish Das, Assistant Professor, Ingram School of Engineering, Texas State University, USA, subasish@txstate.edu (this opens in a new tab)
Dr. Kakan Dey, Assistant Professor, Department of Civil and Environmental Engineering, West Virginia University, USA, kakan.dey@mail.wvu.edu (this opens in a new tab)
Aims and Scope:
In the era of big data and the Internet of Things (IoT), the massive growth of connected devices with real-time and high frequency geo-location pings has helped many private vendors and public agencies collect location tags and speed samples of such devices on the urban transportation network. These data sources are beneficial for advanced urban computing. Data fusion, as a part of data integration, is a process of integration of multiple data sources, temporally and spatially, to prepare the multi-source information into a consistent, accurate, and robust representative dataset with key insights from each data source (Zheng et al., 2014; Zheng, 2015). Data fusion techniques such as Dempster-Shafer evidential reasoning, artificial neural networks, fuzzy logic, Kalman filtering, and Bayesian inference have been applied to solve urban computing-related issues (Klein, 2004). However, there is a need for data fusion-related algorithmic advancements and solutions to measure or estimate different traffic-related performance measures such as operating speed, free-flow speed, average delay, travel time by vehicle type, and other real-time operational and safety performance measures for the advancement of urban computing.
In response to the growing demand for advanced multi-source data fusion techniques in academia and profession, this special issue of Computational Urban Science encourages the submission of original manuscripts that focus on addressing data fusion problems by designing open-source data/products, developing analytical tools, developing online and interactive data visualization tools, and so on.
Submitted manuscripts could cover but are not limited to the following themes:
Open-source data fusion algorithms that benefit urban science communities. Advanced usage of hybrid data fusion techniques using probe data, point detector data, and other multi-source data. Online visualization, analytical, and data-sharing platforms that promote advanced data fusion for both academia and professional use.Development of software packages and interactive analytical tools that advance data fusion techniques.Applied data fusion workflows and frameworks. Other research and visions related to reproducibility and replicability in computational urban science.
Please submit your article here: https://www.editorialmanager.com/cusc/ (this opens in a new tab)
When submitting your article, please select the designated Thematic Series in the "additional information Questionnaire" (the fourth step).
Articles will undergo all of the journal's standard peer review and editorial processes outlined in its submission guidelines. (this opens in a new tab)
Reference
Klein, L., 2004. Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, Second Edition. Bellingham, Washington, U.S.
Zheng, Y., Capra, L., Wolfson, O., and Yang. H., 2014. Urban Computing: Concepts, Methodologies, and Applications. ACM Transactions on Intelligent Systems and Technology, Vol. 5, No. 3, pp. 1-55.
Zheng, Y., 2015. Methodologies for Cross-Domain Data Fusion: An Overview. IEEE Transactions on Big Data. Vol. 1, No. 1, pp. 16-34.