Fatality is a common disorder characterized by car crashes. One of the most significant current discussions in transportation science is find a precise method to predict car crashes in different areas such as road segments.The past decades has seen the rapid development of using statistical methods to predict accident. These rapid changes are having a serious effect on reduce the number of fatalities in car crashes. However, far too little attention has been paid to the role of spatial dependence between roads segments. This paper compares spatial autoregressive methods for taking spatial dependence into account in crash rate prediction. This paper has been divided into four parts. The first part deals with collecting accident data along the road including location of accident occurrence. The second part focused on dividing road to the same length segments. Qazvin- zanjan freeway was selected for study in this research. The third part deals with accident rates calculation in each segment. The forth part of this study deals with using spatial autoregressive methods to predict the accident rates in each segment and comparison the results with the results of conventional regression models such as linear regression model. The results of this investigation show that spatial dependence consideration lead to improve in results.
Boroujerdian, A. (2015). Using spatial autoregressive lag model to predict crash rates in roads segments(maximum likelihood estimation). Quarterly Journal of Transportation Engineering, 6(3), 383-396.
MLA
Amin Boroujerdian. "Using spatial autoregressive lag model to predict crash rates in roads segments(maximum likelihood estimation)". Quarterly Journal of Transportation Engineering, 6, 3, 2015, 383-396.
HARVARD
Boroujerdian, A. (2015). 'Using spatial autoregressive lag model to predict crash rates in roads segments(maximum likelihood estimation)', Quarterly Journal of Transportation Engineering, 6(3), pp. 383-396.
VANCOUVER
Boroujerdian, A. Using spatial autoregressive lag model to predict crash rates in roads segments(maximum likelihood estimation). Quarterly Journal of Transportation Engineering, 2015; 6(3): 383-396.
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