Real-time analysis of road safety data for rCITI (UNSW)
Institution: rCITI (Research Centre for Integrated Transport Innovation), UNSW Sydney
Research Focus: Traffic safety analysis, crash pattern identification, and evidence-based road safety interventions
This interactive analytics platform provides comprehensive analysis of Victorian road crash data spanning 2012–2025, encompassing 194,437 crash records. The platform visualizes temporal patterns (day-of-week, hourly, and seasonal distributions), geographic hotspots (by LGA and coordinates), and injury severity profiles (by speed zone and road user type). These findings support evidence-based policy development and targeted road safety interventions across Victoria.
Data Processing: Aggregation and frequency analysis using Python (pandas, numpy). Visualization: Interactive Charts.js library for responsive, multi-axis rendering. API Architecture: RESTful PHP endpoint (api.php) serving pre-computed JSON metrics for real-time dashboard updates. Statistical Methods: Descriptive statistics (counts, proportions, temporal aggregations) and geographic concentration analysis (frequency-based hotspot identification).
Primary Source: Victorian Road Crash Data, sourced from Transport Accident Commission (TAC) public datasets and Victoria Police crash records. Coverage: 194,437 crash records; temporal range: 2012–2025; geographic scope: all Victorian local government areas (LGAs). Attribution: Data aggregated and analyzed under rCITI research protocols. For citation and further methodological details, see the accompanying technical documentation and README.md in the deployment repository.