The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 367–371, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-367-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 367–371, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-367-2017

  27 Sep 2017

27 Sep 2017

THE CRASH INTENSITY EVALUATION USING GENERAL CENTRALITY CRITERIONS AND A GEOGRAPHICALLY WEIGHTED REGRESSION

M. Ghadiriyan Arani1, P. Pahlavani2, M. Effati3, and F. Noori Alamooti4 M. Ghadiriyan Arani et al.
  • 1GIS Division, School of Surveying and Geospatial Eng, College of Eng, University of Tehran, Iran
  • 2School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Iran
  • 3Department of Civil Eng, University of Guilan, Iran
  • 4Department of Civil Eng, Shahid Rajaee Teacher Training University, Iran

Keywords: Crash intensity, General centrality criterions, Geographically weighted regression, Atlanta highway

Abstract. Today, one of the social problems influencing on the lives of many people is the road traffic crashes especially the highway ones. In this regard, this paper focuses on highway of capital and the most populous city in the U.S. state of Georgia and the ninth largest metropolitan area in the United States namely Atlanta. Geographically weighted regression and general centrality criteria are the aspects of traffic used for this article. In the first step, in order to estimate of crash intensity, it is needed to extract the dual graph from the status of streets and highways to use general centrality criteria. With the help of the graph produced, the criteria are: Degree, Pageranks, Random walk, Eccentricity, Closeness, Betweenness, Clustering coefficient, Eigenvector, and Straightness. The intensity of crash point is counted for every highway by dividing the number of crashes in that highway to the total number of crashes. Intensity of crash point is calculated for each highway. Then, criteria and crash point were normalized and the correlation between them was calculated to determine the criteria that are not dependent on each other. The proposed hybrid approach is a good way to regression issues because these effective measures result to a more desirable output. R2 values for geographically weighted regression using the Gaussian kernel was 0.539 and also 0.684 was obtained using a triple-core cube. The results showed that the triple-core cube kernel is better for modeling the crash intensity.