| contributor author | Ghazan Khan | |
| contributor author | Xiao Qin | |
| contributor author | David A. Noyce | |
| date accessioned | 2017-05-08T21:05:05Z | |
| date available | 2017-05-08T21:05:05Z | |
| date copyright | May 2008 | |
| date issued | 2008 | |
| identifier other | %28asce%290733-947x%282008%29134%3A5%28191%29.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/38059 | |
| description abstract | Spatial statistical techniques can be an effective tool for analyzing patterns and autocorrelation in crash data, especially weather-related crashes. Since weather is a geographic phenomenon, it tends to show distinct geographic patterns affecting certain locations more than others. Accordingly, “weather-related” crashes may also display similar distinct patterns or clustering. The objective of this research was to use spatial statistical techniques to identify significant patterns of weather-related crashes. Weather-related crashes, defined as those crashes which occurred in adverse weather conditions, were analyzed using the Getis-Ord | |
| publisher | American Society of Civil Engineers | |
| title | Spatial Analysis of Weather Crash Patterns | |
| type | Journal Paper | |
| journal volume | 134 | |
| journal issue | 5 | |
| journal title | Journal of Transportation Engineering, Part A: Systems | |
| identifier doi | 10.1061/(ASCE)0733-947X(2008)134:5(191) | |
| tree | Journal of Transportation Engineering, Part A: Systems:;2008:;Volume ( 134 ):;issue: 005 | |
| contenttype | Fulltext | |
