Investigating snowplow-related injury severity along mountainous roadway in Wyoming

Author:

Haq Muhammad Tahmidul,Reza Imran,Ksaibati Khaled

Abstract

Snow removal and deicing using snowplow trucks assist transportation agencies to enhance roadway safety and mobility. However, due to slower travel speeds during these operations, motorists often end up in crashes for poor visibility and disturbance of the snow. Despite the risk associated with snowplows, no previous study was found that exclusively investigate the factors associated with injury severity in snowplow-involved crashes. Therefore, this paper presents an extensive exploratory analysis and fills this knowledge gap by identifying the significant contributing factors affecting the occupant injury severity from the aspects of crashes with snowplow involvement. The study utilized eleven years (2010-2020) of historical snowplow-related crash data from Wyoming. Both the binary logit model and mixed binary logit model were developed to investigate the impacts of the various occupant, vehicle, crash, roadway, and environmental characteristics on the corresponding occupant injury severity. As one of the important findings from this research concludes that other vehicle drivers are more responsible than snowplow drivers contributing to more severe injuries in crashes involving snowplows. Recommendations suggested based on the modeling results are expected to help transportation agencies and policymakers take necessary actions in reducing snowplow-involved crashes by targeting appropriate strategies and proper resource allocation.

Publisher

Centre of Sociological Research, NGO

Subject

Rehabilitation,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Real Time Thermal Image Based Machine Learning Approach for Early Collision Avoidance System of Snowplows;Journal of Intelligent Learning Systems and Applications;2024

2. Investigating Mobility and Safety Impacts of Winter Maintenance Operations Using Connected Vehicle Data;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24

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