Spatial Association Between Near-Misses and Accident Blackspots in Sydney, Australia: A Getis-Ord $G_i^*$ Analysis

TL;DR

This study introduces a spatial-statistical framework using Getis-Ord and Moran's I statistics to analyze the relationship between near-misses and crash blackspots in Sydney, enabling proactive safety management based on high-resolution near-miss data.

Contribution

The paper develops a novel spatial analysis methodology combining Getis-Ord and Bivariate Moran's I to identify high-risk areas using near-miss data, advancing proactive road safety strategies.

Findings

Significant clusters of high near-miss, low crash areas identified.

Spatial relationships reveal areas with high near-misses but low crash records.

Infrastructure factors differ between high-risk and low-risk clusters.

Abstract

Conventional road safety management is inherently reactive, relying on analysis of sparse and lagged historical crash data to identify hazardous locations, or crash blackspots. The proliferation of vehicle telematics presents an opportunity for a paradigm shift towards proactive safety, using high-frequency, high-resolution near-miss data as a leading indicator of crash risk. This paper presents a spatial-statistical framework to systematically analyze the concordance and discordance between official crash records and near-miss events within urban environment. A Getis-Ord statistic is first applied to both reported crashes and near-miss events to identify statistically significant local clusters of each type. Subsequently, Bivariate Local Moran's I assesses spatial relationships between crash counts and High-G event counts, classifying grid cells into distinct profiles: High-High (coincident risk), High-Low and Low-High. Our analysis reveals significant amount of Low-Crash, High-Near-Miss clusters representing high-risk areas that remain unobservable when relying solely on historical crash data. Feature importance analysis is performed using contextual Point of Interest data to identify the different infrastructure factors that characterize difference between spatial clusters. The results provide a data-driven methodology for transport authorities to transition from a reactive to a proactive safety management strategy, allowing targeted interventions before severe crashes occur.