Citation

Naqvi HM, Tiwari G. Transp. Res. Proc. 2020; 48: 1185-1199.

Copyright

(Copyright © 2020, Elsevier Publications)

DOI

10.1016/j.trpro.2020.08.142

PMID

unavailable

Abstract

This paper investigates factors contributing to fatal crashes on three National Highways (NHs) sections in India by developing safety performance functions. The generalized linear model technique is used for analysing linear and non-linear effect of continuous and categorical predictor variables on discreet dependent variable (fatal crashes) separately for each NH segments. In India, NHs are not usually access controlled, and heterogeneous vehicles travel on highways. The probable explanatory variables are short-listed after thorough literature review, and availability of data. These variables comprise of vehicular traffic (ADT), highway elements, and roadside land use. The fatal crash data for the past five years (2009-2013), traffic and highway inventory data have been collected for three NHs having varying lane configuration: two-lane NH-8, four-lane NH-24 and six-lane NH-1. The study results revealed negative binomial regression model fit the data statistically, and also identified number of statistically significant variables ('segment length', 'roadside land use', 'presence of service road (SR)' and 'terrain type') to estimate fatal crashes at NHs segments. The results of the safety performance functions (SPFs) showed that out of seven explanatory variables examined for each NH (segments), the significant explanatory variable is found to be 'segment length' in km for all three models of NHs (segments). Other significant variable is 'land use' along NHs for both two-lane NH-8 and four-lane NH-24. Similarly, the explanatory variables 'presence of SR' and 'terrain type' are found significant for four-lane NH-24 and two-lane NH-8 respectively. ADT (in vehicles) being an important variable in fatal crash modeling, is kept as an offset variable in each model.

Language: en

Keywords

access roads; Fatal crashes; land use; National Highways; negative binomial regression