Travelers-Tracing and Mobility Profiling Using Machine Learning in Railway Systems
Open accessAbstract
With the advent of Coronavirus Disease 2019 (COVID-19) throughout the world, safe transportation becomes critical while maintaining reasonable social distancing that requires a strategy in the mobility of daily travelers. Crowded train carriages, stations, and platforms are highly susceptible to spreading the disease, especially when infected travelers intermix with healthy travelers. Travelers-profiling is one of the essential interventions that railway network professionals rely on managing the disease outbreak while providing safe commute to staff and the public. In this plethora, a Machine Learning (ML) driven intelligent approach is proposed to manage daily train travelers that are in the age-group 16-59 years and over 60 years (vulnerable age-group) with the recommendations of certain times and routes of traveling, designated train carriages, stations, platforms, and special services using the London Underground and Overground (LUO) Network. LUO dataset has been compared with various ML algorithms to classify different agegroup travelers where Support Vector Machine (SVM) mobility prediction classification achieves up to 86.43% and 81.96% in age-group 16-59 years and over 60 years.
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Asad et al. proposed a machine-learning approach to trace daily train travelers in age cohorts of 16-59 and over 60 in order to recommend safe travel times and routes.
“Asad et al. [55] proposed to use a machinelearning-(ML)-based approach to trace daily train travelers in different age cohorts of 16-59 years (i.e., the less vulnerable age group) and over 60 (i.e., the more vulnerable age group) in order to recommend certain times and routes for safe traveling.”
From Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review (2023) · cites this paper as [55]
Using London underground and overground data, the support vector machine approach performed better than other machine-learning algorithms for classifying age group travelers and achieved accuracy above 80%.
“Using a dataset of the London underground and overground network, different ML algorithms are compared in [55] to properly classify different age group travelers, showing that the support vector machine (SVM) approach performs better to predict the mobility of travelers and achieves high accuracy (more than 80%).”
From Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review (2023) · cites this paper as [55]
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Darsena, D., Gelli, G., Iudice, I., & Verde, F. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3223297 · Cited by 71 · see citing works
- Authors
- Asad, S. M., Dashtipour, K., Hussain, S., Abbasi, Q. H., & Imran, M. A.
- Affiliations
- Transport for London (GB)
- University of Glasgow (GB)
- Venue
- 2020 International Conference on UK-China Emerging Technologies (UCET)
- Publisher
- IEEE
- Published
- 2020-08
- Pages
- 1-4
- OpenAlex
- W3089616442
- Cited by
- 21
- References
- 11
- Citations by year
- 2023: 4 · 2022: 6 · 2021: 10 · 2020: 1
- FWCI
- 11.39
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- International Conference on UK-China Emerging Technologies (UCET) (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Keywords
- COVID-19 pandemic, age group classification, route recommendation, social distancing, support vector machine
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