ITSC 2025 Paper Abstract

Paper FR-LA-T40.3

Grigorev, Artur (University of technology Sydney), Lillo-Trynes, David (Compass IoT), Mihaita, Adriana-Simona (University of Technology in Sydney)

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

Scheduled for presentation during the Regular Session "S40c-Cooperative and Connected Autonomous Systems" (FR-LA-T40), Friday, November 21, 2025, 16:40−17:00, Cooleangata 4

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Cooperative Vehicle-to-Vehicle Data Sharing for Safe and Efficient Traffic Flow, Data-Driven ITS for Dynamic Road Pricing and Smart Tolling Solutions, Real-time Incident Detection and Emergency Management Systems in ITS

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.