Table 1.
Summary of literature on analysing factors influencing speed or speed variability on urban roads.
Fig 1.
Geographical representation of the study region in Sydney, Australia.
The figure presents a schematic of the road network, and highlighting the Central Business District (CBD) in the study region.
Table 2.
Descriptive statistics of CS data corresponding to the functional road hierarchy.
Fig 2.
Distribution of CS data by functional road hierarchy and zones in the study region.
The bar chart illustrates the distribution of data availability across different road categories and zones.
Table 3.
Descriptive statistics of CS data and independent variables.
Fig 3.
Zonal speed variations across the day and night (a) mean speeds; (b) CoV of speed.
This figure compares average traffic speeds and their variability (CoV) across zones throughout 24-hour, illustrating how both central tendency and dispersion differ between day and night.
Fig 4.
Zonal CoV of speed (a) Morning peak hour; (b) Evening peak hour.
Table 4.
Rotated factor loadings of independent variables.
Table 5.
Summary of GWR model for morning peak hour.
Table 6.
Summary of GWR model for evening peak hour.
Fig 5.
Spatial distributions of local coefficient estimates and corresponding t-statistics for GWR modeling during the morning peak hour. Panels illustrate: (a, b) Density factor; (c, d) Private vehicle dependency; (e, f) Commercial activity; (g, h) Land-use diversity and income; (i, j) Network connectivity; (k, l) School zone variables.
This composite figure visualizes both magnitude (coefficient) and statistical significance (t-statistic) of each explanatory variable across spatial zones, highlighting zones where relationships are strong or weak. It underscores the spatial heterogeneity in how these factors influence speed variability.
Fig 6.
Spatial distributions of local coefficient estimates and corresponding t-statistics for GWR modeling during the evening peak hour. Panels illustrate: (a, b) Density factor; (c, d) Private vehicle dependency; (e, f) Commercial activity; (g, h) Land-use diversity and income; (i, j) Network connectivity; (k, l) School zone variables.
This composite figure visualizes both magnitude (coefficient) and statistical significance (t-statistic) of each explanatory variable across spatial zones, highlighting zones where relationships are strong or weak. It underscores the spatial heterogeneity in how these factors influence speed variability.
Fig 7.
Spatial distribution of local R2 values and residuals in Geographically Weighted Regression during peak traffic periods: (a, b) morning peak hour; (c, d) evening peak hour.