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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
31.10.2024
DOI
https://doi.org/10.7307/ptt.v36i5.576
ISSUE
2024 (Vol 36), Issue 5
LICENSE
Copyright (c) 2024 Mesut ULU, Yusuf Sait TÜRKAN
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Bibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future Directions

Authors:Mesut ULU, Yusuf Sait TÜRKAN

Abstract

Traffic accidents are one of the main causes of fatalities and serious injuries among both adults and children worldwide. Due to the ongoing significant socio-economic losses brought on by traffic accidents, precise estimation of the risk of accidents is crucial to reducing subsequent incidents. For this reason, a significant proportion of the studies in the literature include studies on estimating the risk, severity, frequency, location and duration of accidents. The objective of this article is to identify patterns, gaps and future research trends in traffic accident prediction studies conducted between 2003 and 2023. A bibliometric study is carried out to investigate the links and trends in traffic accident and forecasting studies, with a focus on identifying dominant narratives and networks within the academic community. In the keyword search, 1,566 articles were analysed using the Web of Science main collection and bibliometric indicators such as annual publications and citations, top 10, authors, journals, institutions, most cited articles, and a citation analysis of the articles was presented. The results obtained suggest that the discernible patterns identified in this bibliometric analysis of traffic accidents and their predictions will find a much broader application in new paradigms that are ready to catalyse transformative advances in this field, such as artificial intelligence, machine learning and Industry 4.0 applications.

Keywords:traffic accident, prediction, bibliometrics analysis, research status, trend analysis, literature review

References

  1. [1] WHO. Global Status Report on Road Safety 2018: Summary.
  2. [2] Ramirez AF, Valencia C. Spatiotemporal correlation study of traffic accidents with fatalities and injuries in Bogota (Colombia). Accident Analysis & Prevention, 2021;149(105848). DOI: 10.1016/j.aap.2020.105848.
  3. [3] International Transport Forum. Road Safety Data Annual Report 2022. International Transport Forum. https://www.itf-oecd.org/sites/default/files/docs/irtad-road-safety-annual-report-2022.pdf [Accessed 20th Jan. 2023].
  4. [4] Pérez-Acebo H, et al. Evaluation of the radar speed cameras and panels indicating the vehicles’ speed as traffic calming measures (TCM) in short length urban areas located along rural roads. Energies. 2021;14(23):8146. DOI: 10.3390/en14238146.
  5. [5] Gicquel L, et al. Description of various factors contributing to traffic accidents in youth and measures proposed to alleviate recurrence. Frontiers in psychiatry. 2017;8(94). DOI: 10.3389/fpsyt.2017.00094.
  6. [6] Rolison JJ, et al. What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accident Analysis & Prevention. 2018;115:11–24. DOI: 10.1016/j.aap.2018.02.025.
  7. [7] Tang J, et al. Crash injury severity analysis using a two-layer stacking framework. Accident Analysis & Prevention. 2019;122:226-238. DOI: 10.1016/j.aap.2018.10.016.
  8. [8] Erdogan S. Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. Journal of Safety Research. 2009;40(5):341-351. DOI: 10.1016/j.jsr.2009.07.006.
  9. [9] Ferreira-Vanegas C, et al. Analytical methods and determinants of frequency and severity of road accidents: A 20-year systematic literature review. Journal of advanced transportation. 2022;( 7239464). DOI: 10.1155/2022/7239464.
  10. [10] Sharma N, et al. A bibliometric analysis of the published road traffic injuries research in India, post-1990. Health research policy and systems. 2018;16(1):1–11. DOI: 10.1007/978-3-319-10377-8_13.
  11. [11] Jing L, et al. A bibliometric analysis of road traffic injury research themes, 1928–2018. International Journal of Injury Control and Safety Promotion. 2021;28(2):266-275. DOI: 10.1080/17457300.2021.1881558.
  12. [12] Raza SA, et al. A bibliometric analysis of revenue management in airline industry. Journal of Revenue and Pricing Management. 2020;19:436-465. DOI: 10.1057/s41272-020-00247-1.
  13. [13] Cobo MJ, et al. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics. 2011;5(1):146-166. DOI: 10.1016/j.joi.2010.10.002.
  14. [14] Van Raan AF. For your citations only? Hot topics in bibliometric analysis. Measurement: Interdisciplinary Research and Perspectives. 2005;3(1):50-62. DOI: 10.1207/s15366359mea0301_7.
  15. [15] Van Eck NJ, Waltman L. Visualizing bibliometric networks. In: Measuring scholarly impact: Methods and practice. Cham: Springer International Publishing; 2014.
  16. [16] Ren H, et al. A deep learning approach to the citywide traffic accident risk prediction. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC) IEEE. 2018 (pp. 3346-3351). DOI: 10.1109/ITSC.2018.8569437.
  17. [17] Lin Y, Li R. Real-time traffic accidents post-impact prediction: Based on crowdsourcing data. Accident Analysis & Prevention. 2020;145(105696). DOI: 10.1016/j.aap.2020.105696.
  18. [18] Santos D, et al. Machine learning approaches to traffic accident analysis and hotspot prediction. Computers. 2021; 10(12):157. DOI: 10.3390/computers10120157.
  19. [19] Bai M, et al. PrePCT: Traffic congestion prediction in smart cities with relative position congestion tensor. Neurocomputing. 2021;444:147–157. DOI: 10.1016/j.neucom.2020.08.075.
  20. [20] Zhang C, et al. A road traffic accidents prediction model for traffic service robot. Library Hi Tech. 2022;40(4):1031–1048. DOI: 10.1108/LHT-05-2020-0115.
  21. [21] Chuanxia S, et al. Machine learning and IoTs for forecasting prediction of smart road traffic flow. Soft Computing. 2023;27(1):323-335. DOI: 10.1007/s00500-022-07618-3.
  22. [22] An J, et al. A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information. Ieee Access. 2019;7:20708-20722. DOI: 10.1109/ACCESS.2019.2896913.
  23. [23] Gan J, et al. An alternative method for traffic accident severity prediction: Using deep forests algorithm. Journal of advanced transportation, 2020;1–13. DOI: 10.1155/2020/1257627.
  24. [24] Lin DJ, et al. Intelligent traffic accident prediction model for internet of vehicles with deep learning approach. IEEE Transactions on Intelligent Transportation Systems. 2021;23(3):2340-2349. DOI: 10.1109/TITS.2021.3074987.
  25. [25] Park RC, Hong EJ. Urban traffic accident risk prediction for knowledge-based mobile multimedia service. Personal and Ubiquitous Computing. 2022;1–11. DOI: 10.1007/s00779-020-01442-y.
  26. [26] Azhar A, et al. Detection and prediction of traffic accidents using deep learning techniques. Cluster Computing. 2022;26:1–17. DOI: 10.1007/s10586-021-03502-1.
  27. [27] Yang Z, et al. Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework. Safety science. 2022;146(105522). DOI: 10.1016/j.ssci.2021.105522.
  28. [28] Macedo MR, et al. Traffic accident prediction model for rural highways in Pernambuco. Case studies on transport policy. 2022;10(1):278–286. DOI: 10.1016/j.cstp.2021.12.009.
  29. [29] Gutierrez-Osorio C, et al. Deep learning ensemble model for the prediction of traffic accidents using social media data. Computers. 2022;11(9):126. DOI: 10.3390/computers11090126.
  30. [30] Liu Y, et al. A grey convolutional neural network model for traffic flow prediction under traffic accidents. Neurocomputing. 2022;500:761-775. DOI: 10.1016/j.neucom.2022.05.072.
  31. [31] Vaiyapuri T, Gupta M. Traffic accident severity prediction and cognitive analysis using deep learning. Soft Computing. 2021;1–13. DOI: 10.1007/s00500-021-06515-5.
  32. [32] Yan M, Shen Y. Traffic accident severity prediction based on random forest. Sustainability. 2022;14(3):1729. DOI: 10.3390/su14031729.
  33. [33] Li L, et al. A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction. Engineering Applications of Artificial Intelligence. 2020;93(103686). DOI: 10.1016/j.engappai.2020.103686.
  34. [34] Grigorev A, et al. Incident duration prediction using a bi-level machine learning framework with outlier removal and intra–extra joint optimisation. Transportation Research Part C: Emerging Technologies. 2022;141(103721). DOI: 10.1016/j.trc.2022.103721.
  35. [35] Zhao Y, Deng W. Prediction in traffic accident duration based on heterogeneous ensemble learning. Applied Artificial Intelligence. 2022;36(1):2018643. DOI: 10.1080/08839514.2021.2018643.
  36. [36] Zhang Z, et al. Traffic accident prediction based on LSTM-GBRT model. Journal of Control Science and Engineering. 2020;1–10. DOI: 10.1155/2020/4206919.
  37. [37] Panda C, et al. Predicting and explaining severity of road accident using artificial intelligence techniques, SHAP and feature analysis. International Journal of Crashworthiness. 2023;28(2):186-201. DOI: 10.1080/13588265.2022.2074643.
  38. [38] Marcillo P, et al. A systematic literature review of learning-based traffic accident prediction models based on heterogeneous sources. Applied Sciences.2022;12(9):4529. DOI: 10.3390/app12094529.
  39. [39] Ma Y, et al. Review of research on road traffic operation risk prevention and control. International Journal of Environmental Research and Public Health. 2022;19(19):12115. DOI: 10.3390/ijerph191912115.
  40. [40] Khan S, et al. Anomaly detection in traffic surveillance videos using deep learning. Sensors. 2022;22(17):6563. DOI: 10.3390/s22176563.
  41. [41] Cheng G, et al. Research on highway roadside safety. Journal of Advanced Transportation. 2021;1–19. DOI: 10.1155/2021/6622360.
  42. [42] Rajabli N, et al. Software verification and validation of safe autonomous cars: A systematic literature review. IEEE Access, 2014;9:4797-4819. DOI: 10.1109/ACCESS.2020.3048047.
  43. [43] Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact?. Scientometrics. 2015;105:1809–1831. DOI: 10.1007/s11192-015-1645-z.
  44. [44] Ji W, et al. Knowledge mapping with CiteSpace, VOSviewer, and SciMAT on intelligent connected vehicles: road safety issue. Sustainability. 2023;15(15):12003. DOI: 10.3390/su151512003.
  45. [45] Li C, et al. A bibliometric analysis and review on reinforcement learning for transportation applications. Transportmetrica B: Transport Dynamics. 2023;11(1):2179461. DOI: 10.1080/21680566.2023.2179461.
  46. [46] Amlan HA, et al. Discovering the global landscape of vulnerability assessment method of transportation network studies: A bibliometric review. Physics and Chemistry of the Earth, Parts A/B/C. 2022;103336. DOI: 10.1016/j.pce.2022.103336.
  47. [47] Moreno FC, et al. Relationship between human factors and a safe performance of vessel traffic service operators: A systematic qualitative-based review in maritime safety. Safety science. 2022;155:105892. DOI: 10.1016/j.ssci.2022.105892.
  48. [48] Hassan SA, et al. Vulnerability of road transportation networks under natural hazards: A bibliometric analysis and review. International Journal of Disaster Risk Reduction. 2022;103393. DOI: 10.1016/j.ijdrr.2022.103393.
  49. [49] Gil M, et al. A bibliometric analysis and systematic review of shipboard Decision Support Systems for accident prevention. Safety science. 2020;128:104717. DOI: 10.1016/j.ssci.2020.104717.
  50. [50] Lozano Dominguez JM, Mateo Sanguino TJ. Review on v2x, i2x, and p2x communications and their applications: a comprehensive analysis over time. Sensors. 2019;19(12):2756. DOI: 10.3390/s19122756.
  51. [51] Godin B. On the origins of bibliometrics. Scientometrics. 2006;68(1):109–133. DOI: 10.1007/s11192-006-0086-0.
  52. [52] Zou X, et al. Fifty years of accident analysis & prevention: A bibliometric and scientometric overview. Accident Analysis & Prevention. 2020;144:105568. DOI: 10.1016/j.aap.2020.105568.
  53. [53] Yilmaz G. Bibliometrics Analysis of Published Papers on Tipping in Restaurants. Seyahat ve Otel İşletmeciliği Dergisi. 2017;14(2):65–7929. DOI: 10.24010/soid.335082.
  54. [54] Gutiérrez-Salcedo M, et al. Some bibliometric procedures for analyzing and evaluating research fields. Applied intelligence. 2018;48:1275–1287. DOI: 10.24010/soid.335082.
  55. [55] Aria M, Cuccurullo C. Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics. 2017;11(4):959–975. DOI: 10.1016/j.joi.2017.08.007.
  56. [56] Lozano R, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the global burden of disease study 2010. The lancet. 2012;380(9859):2095-2128. DOI: 10.1016/S0140-6736(12)61728-0.
  57. [57] Zou Y, Zhang Y, Cheng K. Exploring the impact of climate and extreme weather on fatal traffic accidents. Sustainability. 2021;13(1):390. DOI: 10.3390/su13010390.
  58. [58] Butt FM, et al. Bibliometric analysis of road traffic injuries research in the Gulf Cooperation Council region. F1000Research, 2020;9. DOI: 10.12688/f1000research.25903.2.
  59. [59] Ospina-Mateus H, et al. Bibliometric analysis in motorcycle accident research: A global overview. Scientometrics, 2019;121:793-815. DOI: 10.1007/s11192-019-03234-5.
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