Developing a Big Data Science Based Model Linked to Meteorological Data for Enhanced Applicability of Transportation Analytics
Arnav Goenka
Vellore Institute of Technology, Vellore, Tamil Nadu, India
Download PDF
http://doi.org/10.37648/ijps.v17i01.020
Abstract
In the current era of big data, vast amounts are generated rapidly from diverse, rich data sources. Embedded within these big data sets is valuable information and knowledge that can be uncovered using big data science techniques. Transportation data and meteorological data are prime examples of such big data. This paper presents a big data science solution for transportation analytics incorporating meteorological data. Specifically, we analyse meteorological data to examine the impact of various weather conditions (e.g., fog, rain, snow) on the on-time performance of public transit. Evaluation using real-life data collected from the Canadian city of Winnipeg demonstrates the practicality and effectiveness of our big data science solution in analysing bus delays caused by different meteorological conditions.
Keywords:
big data; meteorological data; transportation analysis
References
- S.H. Ahmed, et al., “Guest editorial introduction to the special issue on data science for intelligent transportation systems. IEEE TITS 23(9), 2022, 16484-16491.
- D. Deng, et al., “Spatial-temporal data science of COVID-19 data,” IEEE BigDataSE 2021, 7-14.
- K.E. Dierckens, et al., “A data science and engineering solution for fast k-means clustering of big data,” IEEE TrustCom-BigDataSE-ICESS 2017, 925-932.
- C.K. Leung, et al., “Big data science on COVID-19 data,” IEEE BigDataSE 2022, 14-21.
- U. Qamar, M.S. Raza, Data Science Concepts and Techniques with Applications. Springer, 2020.
- M.T. Alam, et al., “Mining high utility subgraphs,” IEEE ICDM Workshops 2021, 566-573.
- M.E.S. Chowdhury, et al., “A new approach for mining correlated frequent subgraphs,” ACM TMIS 13(1), 2022, 9:1-9:28.
- R.R. Haque, et al., “UFreS: a new technique for discovering frequent subgraph patterns in uncertain graph databases,” IEEE ICBK 2021, 253-260.
- D. Choudhery, C.K. Leung, “Social media mining: prediction of box office revenue,” IDEAS 2017, 20-29.
- C.C.J. Hryhoruk, C.K. Leung, “Compressing and mining social network data,” IEEE/ACM ASONAM 2021, 545-552.
- C.K. Leung, S.P. Singh, “A mathematical model for friend discovery from dynamic social graphs,” IEEE/ACM ASONAM 2021, 569-576
- J. De Guia, et al., “DeepGx: deep learning using gene expression for cancer classification,” IEEE/ACM ASONAM 2019, 913-920.
- D.L.X. Fung, et al., “Self-supervised deep learning model for COVID- 19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19,” BMC J. Transl. Med. 19, 2021, 318:1-318:18.
- C.K. Leung, C. Zhao, “Big data intelligence solution for health analytics of COVID-19 data with spatial hierarchy,” IEEE DataCom 2021, 13-20.
- Q. Liu, et al., “A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT images,” IEEE Access 8, 2020, 213718-213728
- J. Zammit, et al., “Semi-supervised COVID-19 CT image segmentation using deep generative models,” BMC Bioinformatics 23 (Supplement 7), 2022, 343:1-343:15.
- C.C.J. Hryhoruk, C.K. Leung, “Interpretable mining of influential patterns from sparse web,” IEEE/WIC/ACM WI-IAT 2021, 532-537.
- C.K. Leung, et al., “A web intelligence solution to support recommendations from the web,” IEEE/WIC/ACM WI-IAT 2021 Companion, 160-167.
- C.C.J. Hryhoruk, et al., “Smart city transportation data analytics with conceptual models and knowledge graphs,” IEEE SmartWorld 2021, 455-462.
- M.D. Jackson, et al., “A Bayesian framework for supporting predictive analytics over big transportation data,” IEEE COMPSAC 2021, 332-337.
- J. Kim, et al., “A regression-based data science solution for transportation analytics,” IEEE IRI 2022, 55-60.
- M. Kolisnyk, et al., “Analysis of multi-dimensional road accident data for disaster management in smart cities,” IEEE IRI 2022, 43-48.
- C.K. Leung, et al., “Conceptual modeling and smart computing for big transportation data,” IEEE BigComp 2021, 260-267.
- R.C. Camara, et al., “Fuzzy logic-based data analytics on predicting the effect of hurricanes on the stock market,” FUZZ-IEEE 2018, 576-583.
- T.S. Cox, et al., “An accurate model for hurricane trajectory prediction,” IEEE COMPSAC 2018, vol. 2, 534-539.
- B. Nguyen, et al., “A data science solution for mining weather data and transportation data for smart cities,” IEEE COMPSAC 2022, 1672- 1677.
- C. Silva, F. Martins, “Traffic flow prediction using public transport and weather data: a medium sized city case study,” WorldCIST 2020, vol. 2, 381-390.
- J. Gilmore, et al., “Commuters using sustainable transportation in census metropolitan areas,” Statistics Canada, 2017.
- K. Savage, “Results from the 2016 census: commuting within Canada’s largest cities,” Statistics Canada, 2019.
