2017
DOI: 10.1016/j.jtrangeo.2016.11.011
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Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida

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Cited by 39 publications
(22 citation statements)
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“…This suggests that older drivers are generally more cautious across all driving periods. Ulak et al [18] also found that factors related to visibility (such as lighting conditions) were negatively correlated with crash involvement for older drivers, possibly pointing to the reason why it was nonsignificant in this study. Weekend crashes were less severe than weekday crashes, as found in Uddin and Huynh [7].…”
Section: Environmental Characteristicsmentioning
confidence: 47%
See 1 more Smart Citation
“…This suggests that older drivers are generally more cautious across all driving periods. Ulak et al [18] also found that factors related to visibility (such as lighting conditions) were negatively correlated with crash involvement for older drivers, possibly pointing to the reason why it was nonsignificant in this study. Weekend crashes were less severe than weekday crashes, as found in Uddin and Huynh [7].…”
Section: Environmental Characteristicsmentioning
confidence: 47%
“…Specifically, age-related differences have been established. Ulak et al [18] analyzed crashes in northern Florida based on different age groups. The study found that older driver crashes totally differ from crashes involving other age groups, both spatially and temporally.…”
Section: Introductionmentioning
confidence: 99%
“…The outcome of KDE, based on Euclidean distance over planer space, was found to be biased for location datasets over a network space, due to the over-estimation of crash clusters [24]. To overcome this limitation, a network-based KDE was developed and adopted in road network spaces to discern the crash patterns involving different age groups and severity levels [25,26].…”
Section: Spatial Analysis Of Crash Patternsmentioning
confidence: 99%
“…The tool iterates each crash location and uses it as the center point to generate a buffer with a user-defined radius distance (e.g., 200 m), and then selects and counts the crash points that are located entirely within the buffer. The search radii attempted by the authors include the ranges from 100 m to 300 m (with a 50 m incremental interval) for urban areas and the ranges from 800 m to 1000 m (with a 100 m incremental interval) for rural areas, by following the distance thresholds recommended by Thakali et al [16] and Ulak et al [26]. The results (in K percentage values) present the counts of adjacent crashes to a specific crash location in different cluster groups.…”
Section: K-nearest Neighbors (Knn)mentioning
confidence: 99%
“…GIS spatial overlay analysis technology refers to the superposition for two or more sets of thematic features of graphics in the same region, the same scale and the same mathematical basis but different information; then according to the intersection of various elements and polygon boundary, or polygon properties to create a new feature layer with multiple attribute combinations [29] ( Fig. 2).…”
Section: Gis Spatial Overlay Analysis Technologymentioning
confidence: 99%