Abstract:
Road accidents are spatially constrained events that require analytical approaches
that account for the underlying transport network. Traditional area-based methods
often underestimate accidents along roads, as they ignore road geometry. Modeling
crashes as points on a road network improves intensity estimation. Although
conventional spatial point pattern techniques are widely applied in accident
analysis, approaches that incorporate the linear structure of roadways are more
suitable, as accidents are constrained to occur along these networks. This study
aims to identify road accident hotspots using a Linear Network Point Processes
(LNPP) approach, which is more suitable than traditional area-based methods for
linear networks. By analyzing accident data from the Kandy Police Division in Sri
Lanka, it seeks to enable precise hotspot detection and support targeted strategies
for improving road safety. Accidents occur along roads; therefore, LNPP on a
metric graph G = (V, E) was used, where V represents intersections and E
represents road segments. The linear network kernel density estimation was used
at a location x on a road segment to estimate event intensity per unit length along
the network, based on the shortest path distances between observed points.
Analyses used Gaussian and Epanechnikov kernels. The optimal bandwidth was
selected using the Likelihood Cross-Validation (LCV) method and Scott's Rule.
Diggle’s edge correction method was applied to reduce boundary bias. Hence, four
models were fitted. A total of 2,043 crash events were represented as a point pattern
on a road network comprising 108,584 line segments and 110,361 vertices. Both
Gaussian and Epanechnikov kernels produced similar density estimates on the road
network for a given bandwidth. However, the LCV method, which estimates kernel
density on linear networks, provided a more accurate bandwidth than Scott’s rule.
The smoothing bandwidths were 1.98 km and 5.07 km for the Scott and LCV
methods, respectively. Residuals range from 0–45 for Scott’s rule and only 0–13
with LCV. Several high-density road accident segments were found, highlighting
spatial heterogeneity in accident risk along the network. LCV outperformed Scott’s
rule in bandwidth selection, reducing residuals and enhancing hotspot detection,
demonstrating the effectiveness of LNPP for network-constrained traffic safety
analysis.
