This study synthesises crash data analyses conducted across seven Victorian LGAs — Kingston, Melton, Cardinia, Moonee Valley, Greater Bendigo, Manningham, and Ballarat — covering approximately 17,941 police-reported crash records spanning 2016–2024. A consistent hierarchy of risk factors emerges: speed variability drives crash frequency; high-speed road environments (≥80 km/h) drive crash severity; and pedestrian and motorcyclist involvement universally escalates fatal injury probability.
Seven LGAs spanning metropolitan, suburban, peri-urban and regional Victorian road environments — enabling cross-context comparison of crash mechanisms that cannot be distinguished in single-jurisdiction studies.
● Metropolitan/Suburban ● Regional cities
| LGA | Setting | Crashes | Fatal % | Serious % |
|---|
Temporal patterns are highly consistent across all seven LGAs. A primary crash peak occurs in October–November (spring, +8–10% above annual mean), with a winter trough in June–July (−13–16%). The afternoon commute window 15:00–18:00 is the single most consistent high-risk period across all road environments.
Normalised to annual mean = 100. Spring (Oct–Nov) and late summer (Feb–Mar) peaks consistent across all LGAs. Winter trough Jun–Jul.
15:00–18:00 afternoon window is the dominant risk period across all 7 LGAs and all road environments.
Friday peak reflects commuter volume and end-of-week discretionary travel. Sunday shows lowest crash index.
The 15:00–18:00 afternoon window is the single highest-risk enforcement period across all seven LGAs — consistent regardless of geography, road type, or local demographics.
Friday's elevated frequency index (107 vs mean 100) reflects commuter volume plus end-of-week discretionary travel. Kingston Ordered Logit: weekend β̂ = +0.22; Manningham Cox PH: weekend HR = 1.31 (p < 0.005).
Ordinary Poisson models were unsuitable in all seven studies — Pearson χ²/df overdispersion ratios of 7 to 54. The Negative Binomial NB2 model was universally preferred, with ΔAIC improvements of 300 to 2,958 over Poisson. Speed variability — specifically high maximum speeds paired with lower mean speeds — is the universal predictor of elevated crash frequency.
| LGA | AIC | Dominant Predictors | α̂ (Dispersion) | ΔAIC vs Poisson |
|---|
Discrete choice models — Ordered Logit preferred in 4 of 7 LGAs; MNL in 2; mixed in 1 — address the question that ultimately matters most: given that a crash occurs, what determines whether someone is killed or seriously injured? Four factors emerge with cross-LGA consistency.
Fatal and serious injury proportion increases sharply with posted speed limit. High-speed zones (≥80 km/h) are the dominant severity environment.
Pedestrians and motorcyclists experience disproportionately high fatal/serious injury rates. Biomechanical exposure, not behaviour.
Seven independently conducted studies, each using the same analytical pipeline, enable direct cross-context replication. A finding confirmed across all 7 LGAs is not an artefact of local conditions — it is a structural feature of Victorian road crash risk.
COVID-19 produced a 20–45% crash reduction in 5 of 7 LGAs in 2020 via reduced vehicle kilometres travelled. Full recovery to pre-pandemic levels by 2021–2022. The underlying risk mechanisms — confirmed by count and severity models — were unchanged.
The discrete choice severity models provide a defensible quantitative basis for prioritising the intervention portfolio. DCM coefficient magnitudes determine expected impact. These interventions are not interchangeable.
The visualisation below draws from the full Victorian Open Data crash database (~194,000 records statewide). These are the crash hotspots that form the empirical foundation from which the model findings above were derived. Marker size and colour indicate relative crash concentration.