750 Innovative methodology for evaluating road safety on national highways in India: a case study of Haryana

<h3>Background</h3> The severity of the Road Traffic Injuries (RTIs) is highly influenced due to the transfer of the kinetic energy. This study is an endeavour to determine the relationship of crashes for a particular transport mode which is heavy transport vehicles (HTVs). We estimated the truck volume using a novel methodology and developed its relationship with various other factors. <h3>Objective</h3> To comprehensively assess the impact of heavy transport vehicles on road safety along the selected buffer zones at national highways and investigate its correlation with population density and estimated truck volume by developing a robust statistical model. <h3>Methods</h3> This study used geocoded data on fatal crashes in Haryana from 2017 to 2019. Subsequently, the crashes involving heavy vehicles that occurred on national highways were filtered. The present research adopts a new strategy for collecting volume data on heavy vehicles by utilising Google satellite imagery. Data from toll plazas located on national roads is also used to validate this methodology through modelling. Following this, the population data is extracted from the high-resolution density map retrieved from Meta. The road segment is divided into 500 and 1000-meter sections, and the Zero Inflated Negative Binomial (ZINB) and Hurdel models are used to estimate the relationship between fatal road crashes involving heavy vehicles, truck volume, population along varying buffer sections on national highways, and other variables. <h3>Results</h3> The findings indicate that the ZINB model outperforms the hurdle model in predicting crash frequency accurately. Fatal collisions tend to rise with increased heavy vehicle volume, population density along national highways, and access density. Conversely, fatal collisions decrease with more lanes and the presence of medians. Specifically, a one-unit increase in heavy vehicle volume corresponds to a logarithmic increase of 0.437 in crash frequency, holding all other factors constant. <h3>Conclusions</h3> This study provides significant findings on the intersection of heavy vehicle volume, roadside population, and crash occurrences on national highways. It emphasizes the importance of features such as medians and multiple lanes, for more accurate projections of crash frequency and developing safer road scenarios along the national highways.

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Publication Details

Published
2024-08-30
DOI
https://doi.org/10.1136/injuryprev-2024-safety.374
Primary Topic
Traffic and Road Safety
Type
article
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article

750 Innovative methodology for evaluating road safety on national highways in India: a case study of Haryana

Rahul Goel, Abhishek Bansal, Parth Parikh
Traffic and Road Safety
article

750 Innovative methodology for evaluating road safety on national highways in India: a case study of Haryana

Rahul Goel, Abhishek Bansal, Parth Parikh
article en

Abstract

<h3>Background</h3> The severity of the Road Traffic Injuries (RTIs) is highly influenced due to the transfer of the kinetic energy. This study is an endeavour to determine the relationship of crashes for a particular transport mode which is heavy transport vehicles (HTVs). We estimated the truck volume using a novel methodology and developed its relationship with various other factors. <h3>Objective</h3> To comprehensively assess the impact of heavy transport vehicles on road safety along the selected buffer zones at national highways and investigate its correlation with population density and estimated truck volume by developing a robust statistical model. <h3>Methods</h3> This study used geocoded data on fatal crashes in Haryana from 2017 to 2019. Subsequently, the crashes involving heavy vehicles that occurred on national highways were filtered. The present research adopts a new strategy for collecting volume data on heavy vehicles by utilising Google satellite imagery. Data from toll plazas located on national roads is also used to validate this methodology through modelling. Following this, the population data is extracted from the high-resolution density map retrieved from Meta. The road segment is divided into 500 and 1000-meter sections, and the Zero Inflated Negative Binomial (ZINB) and Hurdel models are used to estimate the relationship between fatal road crashes involving heavy vehicles, truck volume, population along varying buffer sections on national highways, and other variables. <h3>Results</h3> The findings indicate that the ZINB model outperforms the hurdle model in predicting crash frequency accurately. Fatal collisions tend to rise with increased heavy vehicle volume, population density along national highways, and access density. Conversely, fatal collisions decrease with more lanes and the presence of medians. Specifically, a one-unit increase in heavy vehicle volume corresponds to a logarithmic increase of 0.437 in crash frequency, holding all other factors constant. <h3>Conclusions</h3> This study provides significant findings on the intersection of heavy vehicle volume, roadside population, and crash occurrences on national highways. It emphasizes the importance of features such as medians and multiple lanes, for more accurate projections of crash frequency and developing safer road scenarios along the national highways.

Indian Institute of Technology Delhi (IN)
Sustainable cities and communities
Openalex Percentile: Top 84%
Traffic and Road Safety
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