Abstract
Road traffic injuries rank as the eighth most prevalent cause of mortality among individuals of various age groups while specifically representing the primary cause of death for individuals between the ages of 5 and 29. On a global scale, it is observed that pedestrians, bicyclists, and motorized 2- and 3-wheelers collectively account for 54% of the total number of fatalities in traffic incidents. Therefore, pedestrians, cyclists, and motorcyclists are the most vulnerable road users (VRU). In developing nations such as India, VRUs account for over 65% of fatalities. This high proportion can be attributed to factors such as fast urbanization, high population growth rates, and poor infrastructure, all of which have contributed to increased deaths and injuries among VRUs. The existing body of research about crashes involving VRUs in India primarily concentrates on large metropolitan cities. This study primarily examines tier-2 cities that are categorized as non-metropolitan regions. By analysing historical crash records (2014–2021) from "Visakhapatnam traffic police", India, and data from road safety audits, the current research attempts to fill the gap and identify the factors contributing significant risk to the most vulnerable road users. In this case study approach, three crash severity models were built using binary logistic regression, multinomial logistic regression, and ordered probit regression techniques to identify the significant risk factors associated with vulnerable road users. Based on the statistical analysis conducted in this study, it is evident that various factors, including segment length, driver sight clearance, land use, crash time, crash season, accused vehicle type, number of median openings, curves, and pedestrian crossings, exert a substantial influence on the safety of vulnerable road users. To decrease the probability of fatal crashes involving vulnerable road users, particularly on urban National Highways, specific planning and design features are employed and adjusted to mitigate risk, considering the unique risk factors associated with each location.
Similar content being viewed by others
References
World Health Organization (2018) Global status report on road safety.
Ministry of Road Transport & Highways (2022) Road accidents in India 2020, Transport Research Wing, New Delhi.
Al-Ghamdi AS (2002) Using logistic regression to estimate the influence of accident factors on accident severity. Accid Anal Prev 34:729–774
Tay R, Rifaat SM, Chin HC (2008) A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes. Accid Anal Prev 40:1330–1336
Sarkar S, Tay R, Hunt JD (2011) Logistic regression model of risk of fatality in vehicle-pedestrian crashes on national highways in Bangladesh. Transp Res Rec 2264:128–137
Bandyopadhyaya R, Mitra S (2013) Modelling severity level in multi-vehicle collision on indian highways. Procedia Soc Behav Sci 104:1011–1019
Ahmed LA (2017) Using logistic regression in determining the effective variables in traffic accidents. Appl Math Sci 11(42):2047–2058
Goel R, Jain P, Tiwari G (2018) Correlates of fatality risk of vulnerable road users in Delhi. Accid Anal Prev 111:86–93
Mukherjee D, Mitra S (2019) Impact of road infrastructure land use and traffic operational characteristics on pedestrian fatality risk: a case study of Kolkata, India. Transp Dev Econ 5:6
Wahab L, Jiang H (2019) A multinomial logit analysis of factors associated with severity of motorcycle crashes in Ghana. Traffic Inj Prev 20(5):521–527
Lee J, Li X, Mao S, Fu W (2021) Investigation of contributing factors to traffic crashes and violations: a random parameter multinomial logit approach, J Adv Transp, pp 1–11
Mohanty M, Panda R, Gandupalli SR, Sonowal D, Muskan M, Chakraborty R, Dangeti MR (2022) Development of crash prediction models by assessing the role of perpetrators and victims: a comparison of ANN & logistic model using historical crash data. Int J Injury Control Saf Promot, 1–17
Patil VK, Sathish SH (2022) Development of crash severity models using discrete method Indian Highways. Indian Road Congr 50(2):42–48
Maiti J, Bhattacherjee A (1999) Evaluation of risk of occupational injuries among underground coal mine workers through multinomial logit analysis. J Safety Res 30(2):93–101
Siddique MT (2018) Accident severity analysis on national highways in Bangladesh using ordered probit model. Sci Res Essays 13(14):148–157
Rankavat S, Tiwari G (2016) Pedestrians risk perception of traffic crash and built environment features – Delhi India. Saf Sci 87:1–7
Mitra S, Mukherjee D, Mitra S (2019) Safety assessment of urban un-signalized intersections using conflict analysis technique. J Eastern Asia Soc Transp Stud 13:2163–2181
Karacasu M, Ergül B, Yavuz AA (2014) Estimating the causes of traffic accidents using logistic regression and discriminant analysis. Int J Inj Contr Saf Promot 21(4):305–313
Olszewski P, Szagała P, Rabczenko D, Zielińska A (2019) Investigating safety of vulnerable road users in selected EU countries. J Saf Res 68:49–57
Verma A, Sasidharan S, Bhalla K, Allirani H (2022) Fatality risk analysis of vulnerable road users from an Indian city. Case Stud Transp Policy 10:269–277
Naqvi HM, Tiwari G (2017) Factors contributing to motorcycle fatal crashes on National Highways in India. Transp Res Procedia 25:2084–2097
Sorum NG, Pal D (2022) Effect of distracting factors on driving performance: a review. Civ Eng J 8(2):382–405
Razak SFA, Yogarayan S, Aziz AA, Abdullah MFA, Kamis NH (2022) Physiological-based driver monitoring systems: a scoping review. Civ Eng J 8(12):3952–3967
Gohari M, Norozi R, Aghdam AH (2022) Evaluation and optimization of the aerodynamic noise reduction of vehicle side view mirrors: experimental and numerical study. HighTech Innov J 3(1):73–84
Mohanty M, Panda R, Gandupalli SR, Arya RR, Lenka SK (2022) Factors propelling fatalities during road crashes: a detailed investigation and modelling of historical crash data with field studies. Heliyon 8(e11531):1–14
Mohanty M, Sarkar B, Gorzelańczyk P, Panda R, Gandupalli SR, Ankund A (2023) Developing pedestrian fatality prediction models using historical crash data: application of binary logistic regression and boosted tree mechanism. Commun Sci Lett Univ Zilina 25(2):D45-53
Pusuluri VL, Dangeti MR, Kotamrazu M (2023) Road crash zone identification and remedial measures using GIS. Innov Infrastruct Solut 8(146):1–20
Medapati N, Dangeti MR, Patnaikuni CK (2022) A study on pedestrian safety, vehicular fuel consumption, and emissions using GIS and PTV VISSIM software. Innov Infrastruct Solut 7(322):1–17
Acknowledgements
The authors wish to acknowledge the support of the Traffic Police Authorities, Visakhapatnam City and Transport Department, State of Andhra Pradesh, for providing assistance and support in the data component for this project.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
The authors confirm their contribution to the paper as follows: SRG was involved in research idea and conceptual design. Data collection was done by SRG and MRD. SRG helped in analysis and interpretation of results. SRG, KP, and MRD contributed to draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Conflicts of interest
The corresponding author declares no conflict of interest on behalf of all authors. The authors have no competing interests to claim that are relevant to the content of this article.
Ethical approval
All procedures performed in this study involving with all the authors were in accordance with the ethical standards of the institutions.
Informed consent
Informed consent was obtained from all individual participants included in this study.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gandupalli, S.R., Kokkeragadda, P. & Dangeti, M.R. Analysis and modelling of crash severity of vulnerable road users through discrete methods: a case study approach. Innov. Infrastruct. Solut. 8, 298 (2023). https://doi.org/10.1007/s41062-023-01274-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41062-023-01274-8