Clustering and pedestrian crashes prediction modelling: Amman case

被引:2
作者
Shbeeb, Lina [1 ]
机构
[1] Hussein Tech Univ, Amman, Jordan
关键词
Pedestrian crashes; spatial analysis; Moran's I; K-mean; spatial regression; general linear model; SPATIAL AUTOCORRELATION; TRAFFIC ACCIDENTS; SEVERITY; IDENTIFY; FREQUENCY; ZONES;
D O I
10.1080/17457300.2023.2214900
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Pedestrian casualties are a severe domestic as well as international problem. This study analyses the spatial distribution of pedestrian casualties to define contributory factors and delineate the means for their prediction. Three years of crash data were collected along with other factors and analysed using kernel density estimation (KDE), spatial autocorrelation (Moran's I), cluster K-Means, spatial regression, and general linear regressions (GLM). Kernel density estimate defines a cluster of pedestrian deaths within 1250 meters. According to Moran's I, 17/22 attributes about casualties, road networks, demographics, and land use have positive values, indicating similar importance clustering. The spatial pattern of pedestrian casualties is random and insignificant and does not change with time. Casualties are negatively related to the surrounding attributes, indicating a tendency towards dispersion. A K-Means analysis of multiple variables revealed that when variables included in the clustering were higher, the variance explanation percentage was lower. In the multi-variable GLM assuming Poisson distribution, the road network length alone or with the house permits combined were the best predictors of casualties. Classic regressions were not significantly enhanced by spatial dimension, and none of the autoregressive coefficients were significant. The predictions from the Poisson-based GLM model are similar to the classic regressions.
引用
收藏
页码:501 / 529
页数:29
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