Abstract
Road crashes are the major cause of unnatural deaths. Resultant, safety of road users has become one of the prime objectives of highway engineers apart from reliable and economic road network. This study is motivated to identify the safety requirements of different roads within the boundaries of Haridwar and Dehradun districts of Uttarakhand state, India. For analysis, six different roads were divided into 86 homogeneous segments of variable length. Essential traffic, geometric, and pavement surface variables, were selected and evaluated using Poisson regression (PR) and negative binomial regression (NB) model. Separate accident prediction models (APM’s) were developed for total, rear-end and head-on crashes. The APMs delineated important risk factors in terms of positive and negative association. The probability of a crash was identified using risk ratios. Further, the models were compared in terms of goodness of fit. Akaike information criterion (AIC), Schwarz's Bayesian (SBC) value showed that the NB model performs better than the PR model as a former model allows additional over-dispersion parameters in the model.
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Acknowledgements
The authors might want to express gratitude toward the Ministry of Education, Government of India, for financial help during the research as well as the concerned police station of Uttarakhand, for their contribution in collecting the data and cooperation. The author also acknowledges Sanjay Kumar, Anjesh Saini, and Sudhir Kumar for help in field data collection.
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Choudhary, A., Garg, R.D., Jain, S.S. (2023). Analysis of Pavement and Geometric Factors of Selected Highways for Reduction in Road Accidents. In: Devi, L., Errampalli, M., Maji, A., Ramadurai, G. (eds) Proceedings of the Sixth International Conference of Transportation Research Group of India . CTRG 2021. Lecture Notes in Civil Engineering, vol 273. Springer, Singapore. https://doi.org/10.1007/978-981-19-4204-4_16
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DOI: https://doi.org/10.1007/978-981-19-4204-4_16
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