Abstract
The hit-and-run (H-N-R) crashes are increasing in Vadodara. The purpose of this research is to investigate the pattern of H-N-R crashes and to determine the likelihood of a crash being a H-N-R crash. For the study, data was collected from the police department in the form of an FIR (first investigation report) and then extracted into a spreadsheet. To get insights about the data, exploratory analysis was performed, followed by χ2-test to find out the correlation between independent variables. Once the valid variables were identified, a binomial logistic regression model was developed to predict the probability of a crash being a H-N-R crash. From the model developed the influencing factors, confusion matrix, and ROC curve were obtained. It was found that the victim vehicle types, like four-wheelers, two-wheelers, and auto rickshaws, are at higher risk in cases of H-N-R crashes. The maneuvers of an impacting vehicle, like turning or reversing, were found to reduce the probability of a H-N-R crash with respect to going straight. It was also found that the victim’s gender and the number of victims also affected the H-N-R crash.
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References
El-Sadig M, Nelson Norman J, Lloyd OL, Romilly P, Bener A (2002) Road traffic accidents in the United Arab Emirates: trends of morbidity and mortality during 1977–1998. Accid Anal Prev 34(4):465–476. https://doi.org/10.1016/S0001-4575(01)00044-6
Transport Research Wing, Ministry of Road Transport and Highways, and Government of India, “Road Accidents in India 2019,” 2020. https://morth.nic.in/sites/default/files/RA_Uploading.pdf
Valli PP (2005) Road Accident Models for Large Metropolitan Cities of India. IATSS Res. 29(1):57–65. https://doi.org/10.1016/s0386-1112(14)60119-9
Anukesh C, Kumar M (2009) Road Crash Prediction Modeling for Cyberabad
Lee J, Abdel-Aty M, Jiang X (2015) Multivariate crash modeling for motor vehicle and non-motorized modesat the macroscopic level. Accid Anal Prev 78:146–154. https://doi.org/10.1016/j.aap.2015.03.003
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(4):1330–1336. https://doi.org/10.1016/j.aap.2008.02.003
Aidoo EN, Amoh-Gyimah R, Ackaah W (2013) The effect of road and environmental characteristics on pedestrian H-N-R crashes in Ghana. Accid Anal Prev 53:23–27. https://doi.org/10.1016/j.aap.2012.12.021
Zhang G, Li G, Cai T, Bishai DM, Wu C, Chan Z (2014) Factors contributing to hit-and-run crashes in China. Transp. Res. Part F Traffic Psychol Behav 23:113–124. https://doi.org/10.1016/j.trf.2013.12.009
Jiang X et al (2021) Examining the underlying exposures of hit-and-run and non-hit-and-run crashes. J Transp Heal 20:100995. https://doi.org/10.1016/j.jth.2020.100995
Jha AN, Chatterjee N, Tiwari G (2021) A performance analysis of prediction techniques for impacting vehicles in hit-and-run road accidents. Accid Anal Prev 157:106164. https://doi.org/10.1016/j.aap.2021.106164
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Solanki, D., Prajapati, P. (2023). Analysis and Prediction of Hit-and-Run Road Accidents. In: Rastogi, R., Bharath, G., Singh, D. (eds) Recent Trends in Transportation Infrastructure, Volume 1. TIPCE 2022. Lecture Notes in Civil Engineering, vol 354. Springer, Singapore. https://doi.org/10.1007/978-981-99-3142-2_32
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DOI: https://doi.org/10.1007/978-981-99-3142-2_32
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