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All issues Volume 69 (2024) ITM Web Conf., 69 (2024) 03003 References
Open Access
Issue
ITM Web Conf.
Volume 69, 2024
International Conference on Mobility, Artificial Intelligence and Health (MAIH2024)
Article Number 03003
Number of page(s) 6
Section Mobility
DOI https://doi.org/10.1051/itmconf/20246903003
Published online 13 December 2024
  1. C. Staton, J. Vissoci, E. Gong, N. Toomey, R. Wafula, J. Abdelgadir, Y. Zhou, C. Liu, F. Pei, B. Zick et al., Road traffic injury prevention initiatives: a systematic review and metasummary of effectiveness in low and middle income countries, National Library of Medicine 11 (2016). [Google Scholar]
  2. J. Habyarimana, W. Jack, Heckle and chide: Results of a randomized road safety intervention in Kenya, Journal of Public Economics 95, 1438 (2011). [CrossRef] [Google Scholar]
  3. Statistics South Africa, Tech. rep., Statistics South Africa (2020) [Google Scholar]
  4. M. Sinclair, E. Imanirans, Aggressive driving behaviour: The case of minibus taxi drivers in Cape Town, South Africa, in Proc. of the 34th Southern African Transport Conference (2015) [Google Scholar]
  5. A. Sukhaia, P. A. Jones, Understanding geographical variations in road traffic fatalities in South Africa, South African Geographical Journal 95, 187 (2013). [CrossRef] [Google Scholar]
  6. United Nations Organization, Tech. rep., United Nations (2015) [Google Scholar]
  7. R. Naumann, L. Sandt, W. Kumfer, S. LaJeunesse, S. L. K. Heiny, Thinking in the context of road safety: Can systems tools help us realize a true “safe systems” approach?, Curr Epidemiol Rep 7, 343 (2020). [CrossRef] [Google Scholar]
  8. A. Sohail, M. Cheema, M. Ali, A. Toosi, H. Rakha, Data-driven approaches for road safety: A comprehensive systematic literature review, Safety Science, 158, 105949 (2023). [CrossRef] [Google Scholar]
  9. H. Safarpour, D. Khorasani-Zavareh, R. Mohammadi, The common road safety approaches: A scoping review and thematic analysis, Chinese journal of traumatology 23, 113 (2020). [CrossRef] [Google Scholar]
  10. H. Gajjar, S. Sanyal, M. Shah, A comprehensive study on lane detecting autonomous car using computer vision, Expert Systems With Applications 233, 120929 (2023). [CrossRef] [Google Scholar]
  11. C. Bullard, S. Jones, K. Adanu, E. J. Liu, Crash severity analysis of single-vehicle rollover crashes in Namibia: A mixed logit approach, IATSS Research 47, 318 (2023). [CrossRef] [Google Scholar]
  12. I. Konkor, M. Kansanga, Y. Sano, K. Atuoye, I. Luginaah, Risk-taking behaviours and timing to first motorbike collision in the upper west region of Ghana, Journal of Transport and Health 12, 105 (2019). [CrossRef] [Google Scholar]
  13. C. Zhang, Y. Ma, J. Khattak, A. S. Chen, G. Xing, J. Zhang, Driving style identification and its association with risky driving behaviors among truck drivers based on GPS, load condition, and in-vehicle monitoring data, Journal of Transportation Safety and Security pp. 1–35 (2023). [Google Scholar]
  14. L. Eboli, G. Guido, G. Mazzulla, G. Pungillo, R. Pungillo, Investigating car users’ driving behaviour through speed analysis, PROMET - Traffic and Transportation 29, 193 (2017). [CrossRef] [Google Scholar]
  15. H. Hooft van Huysduynen, J. Terken, J. Martens, J. Eggen, Measuring driving styles: A validation of the multidimensional driving style inventory, in Automotive UI ‘15 Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (2015), pp. 257–264 [Google Scholar]
  16. A. Useche, S. B. Cendales, F. Alonso, C. Pastor, J. L. Montoro, Validation of the multidimensional driving style inventory (MDSI) in professional drivers: How does it work in transportation workers?, Transportation Research Part F: Traffic Psychology and Behaviour 67, 155 (2019). [CrossRef] [Google Scholar]
  17. C. Wu, C. Sun, D. Chu, Z. Huang, J. Ma, H. Li, Clustering of several typical behavioral characteristics of commercial vehicle drivers based on GPS data mining: Case study of highways in China, Transportation Research Record: Journal of the Transportation Research Board 2581, 154 (2016). [CrossRef] [Google Scholar]
  18. Y. Feng, S. Pickering, E. Chappell, P. Iravani, C. Brace, Driving style analysis by classifying real- world data with support vector clustering, in Proc. of the 3rd IEEE Int. Conf. on Intelligent Transportation Engineering (ICITE) (Singapore, 2018), pp. 264–268 [Google Scholar]
  19. B. Higgs, M. Abbas, A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles, in 16th Int. IEEE Conference on Intelligent Transportation Systems (ITSC) (The Hague, Netherlands, 2013), pp. 857–862 [Google Scholar]
  20. Y. Ma, K. Tang, S. Chen, J. Khattak, A. Y. Pan, On-line aggressive driving identification based on in-vehicle kinematic parameters under naturalistic driving conditions, Transportation Research Part C: Emerging Technologies 114, 554 (2020). [CrossRef] [Google Scholar]
  21. W. Wang, J. Xi, A. Chong, L. Lin, Driving style classification using a semisupervised support vector machine, IEEE Transactions on Human-Machine Systems 47, 650 (2017). [CrossRef] [MathSciNet] [Google Scholar]
  22. K. Choudhary, A. K. Ingole, Smartphone based approach to monitor driving behavior and sharing of statistic, in Fourth International Conference on Communication Systems and Network Technologies (Bhopal, India, 2014), pp. 279–282 [Google Scholar]
  23. A. Mohammadnazar, R. Arvin, A. Khattak, Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning, Transportation Research Part C: Emerging Technologies 122, 102917 (2021). [CrossRef] [Google Scholar]
  24. eThekwiniMunicipality, Road safety plan, eThek- wini Municipality pp. 1–16 (2017). [Google Scholar]
  25. Y. Li, L. Zhao, L. Rilett, Driving performances assessment based on speed variation using dedicated route truck GPS data, IEEE Access 7, 51002 (2019). [CrossRef] [Google Scholar]
  26. R. Fu, T. Liu, Y. Guo, S. Zhang, W. Cheng, A case study in China to determine whether GPS data and derivative indicator can be used to identify risky drivers, Journal of Advanced Transportation 16, 9072531 (2019). [Google Scholar]

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