Open AccessAccident Analysis and Prediction of Model on National Highways
Author(s) -
R. K. Singh,
Sanjeev Kumar Suman
Publication year - 2012
Publication title -
international journal of advanced technology in civil engineering
Language(s) - English
Resource type - Journals
ISSN - 2231-5721
DOI - 10.47893/ijatce.2012.1015
Subject(s) - truck , transport engineering , traffic volume , road accident , environmental science , population , geography , forensic engineering , statistics , engineering , mathematics , environmental health , medicine , automotive engineering
Rapid growth of population coupled with increased economic activities has favored in tremendous growth of motor vehicles. This is one of the primary factors responsible for road accidents. It is observed that few works have been carried out on statistical analysis of accidents particularly on two-lane National Highways. For this paper stretch of NH-77 has been selected from Hajipur to Muzaffarpur. The accidental data was collected for last eleven years, 2000-2010 from the Police Stations where FIR was lodged. The collected data were analyzed to evaluate the effect of influencing parameters on accident rate. Heavy vehicles like truck are involved in maximum number of accidents on the selected stretch. It is estimated that a heavy vehicles is involved in almost 48% accidents followed by two-wheelers 16%, car 12% and bus 10%. There is no definite trend for monthly variation in accident on a study section but the accidents in month of July and January are generally higher. Accident rate in terms of number of accidents per km-year increases with traffic volume. But the accidents rate in terms of number of accident per million-vehicle kilometer-year (MVKY) decreases with increase in traffic volume. Accident rate per MVKY increases during the study year, whereas both injury and fatality rate per MVKY show a declining trend over the study period. The developed model for accident prediction represents that the number of accidents per-km-year increases with AADT and decreases with improvement in road condition.