MATEC Web Conf.
Volume 296, 20192019 7th International Conference on Traffic and Logistic Engineering (ICTLE 2019)
|Number of page(s)||6|
|Published online||22 October 2019|
Factors Affecting Crash Frequencies: A Negative Binomial Regression Based Analysis of Indus Highway, Pakistan
School of Naval, Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
a Corresponding author: firstname.lastname@example.org
The increase in vehicular traffic have also increased the highway crash frequency with the passage of time. Improvements in highway safety is of vital importance as it could save vast life and monetary losses. The highway crash frequency analysis of major Pakistani highways is a subject less discovered and many important strategic and trade routes are not studied in this regard. This study is aimed to analyze the crash frequency and the prominent factors that cause these crashes on a 302 km section of Indus highway; one of the most important trade routes of the country. Eight years’ data from 2011 till 2018 was arranged into 19 variables where the crash frequency is set as dependent variable, while the eighteen prominent causation factors as independent variables. The tool used for analysis was negative binomial regression being run in the SPSS software. The results indicate that the driver’s behavior, understanding & risk recognition, negligence and law adherence have a significant effect on the crash frequency. Furthermore, highway crash frequency significantly increases with increase in highway segment lengths, number of lanes and lane widths. Similarly, the highway crash frequency significantly enhances when the light, pavement surface and climate condition gets deteriorated. The results of this study are of vital importance to government, transportation companies and general public in order to recognize the most important accident causing factors and devise the transport policies, rules and behaviors accordingly.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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