Investigation of factors contributing to pedestrian hit-and-run crashes in India

Abstract

Objectives

Hit-and-run crashes account for about 14% of total accidents (MoRTH 2017) in India despite it being a cognizable offense under the Indian motor vehicle act of 1988. Hit-and-run crashes also account for a significant number of pedestrian crashes. A better understanding of these crashes is required to reduce the severity of pedestrian crashes. Recent reports claim that there is an increase in the number of fatal victims concerning pedestrians in 2017 over 2016. The number of pedestrians killed in road accidents has climbed from 15,746 in 2016 to 20,457 in 2017 (29.9% increase).

Methods

Nine years of pedestrian crash data from the RADMS database (2009–2017) for the state of Tamilnadu were used in this study. This study explores several factors that increase the likelihood of pedestrian hit-and-run crashes, including temporal factors, environmental factors, crash characteristics, and road features. One hundred ten variables under 30 factors were assessed for statistically significant association with hit-and-run crashes.

Results and conclusions

The results of the present study show that the perpetrator’s tendency to leave the spot of crash increases in the dark unlighted conditions. Increased likelihood of hit-and-run crashes is found during summer, winter season and in urban areas. Among pedestrian characteristics, pedestrians who are intoxicated, pedestrian locations such as footpath, crossing the road and shoulder, pedestrian victims who are injured significantly contribute to hit-and-run crashes. Factors such as day of the week, road category, collision cause, vehicle type, weather conditions, driver alcohol status, pedestrian residence type, and traffic movement were found to be statistically not significant with a 90% confidence interval. We finally discuss a few recommendations based on the results and intervention that can reduce hit-and-run pedestrian fatalities and injuries.

Keywords:

• Hit-and-run crashes
• logistic regression
• pedestrian safety
• road traffic accidents

Acknowledgments

The authors wish to thank RADMS and TN police for providing the data. This research did not receive any specific research grants from the funding agencies in public, commercial or not-for-profit sectors.