| Abstract |
Analysis of spatial patterns can provide an efficient answer to the problem of locating global or local patterns of the spatial distribution of traffic crashes. Approximately 21% of vehicle crashes in the United States occur due to inclement weather, costing the U.S. economy more than $217.5 billion yearly. One major road winter maintenance activity is snowplow and spreading salt on the road surface to improve the driving condition. The potential for rear-end collisions or conflicts between motorists and Snowplow Trucks (SPTs) is a major safety concern. This study extensively applies Ripley's K-function, the global Moran's I measure and the Getis–Ord Gi* function along with Kernel Density Estimation and Network-based Kernel Density Estimations with the aim of analysing snowplow-involved crash hotspots in the state of Wyoming. The positive Moran's I, the high z-scores and the small p values indicate that Snowplow truck crashes were spatially clustered. |
| Authors |
Imran Reza   , Muhammad Tahmidul Haq , Khaled Ksaibati
|
| Journal Info |
Taylor & Francis | International Journal of Crashworthiness , pages: 1 - 13
|
| Publication Date |
6/22/2024 |
| ISSN |
1358-8265 |
Type |
article |
| Open Access |
closed
|
| DOI |
https://doi.org/10.1080/13588265.2024.2366437 |
| Keywords |
Collision Analysis (Score: 0.615503) , Crash Prediction Models (Score: 0.60979) , Spatial Analysis (Score: 0.57432) , Crash Tests (Score: 0.546322) , Driver Behavior (Score: 0.527937)
|
