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Traffic Camera Locations vs Accident Hotspots in Victoria

Statistical Analysis Report — March 2026

Data Sources: VicRoads Open Data, Victorian Government Open Data Portal

Executive Summary

This report presents a statistical analysis comparing the locations of fixed traffic cameras across Victoria with accident hotspot locations in Melbourne. The analysis uses official Victorian Government open data including 122,345 Melbourne urban crash records (2014–2023) and 287 fixed camera locations to determine how well camera placement aligns with areas of highest crash risk.

Key Findings

  • Statistically significant alignment exists: Cameras are meaningfully concentrated in higher-crash areas. Grid cells with cameras have a median of 341 crashes vs 125 for cells without (Mann-Whitney p < 0.0001, large effect size r = 0.55).
  • But the correlation is moderate, not strong: Crash volume explains only 15.3% of camera placement variance. The Spearman rank correlation is 0.24 — a real but modest positive relationship.
  • Major coverage gaps persist: Only 28 of the top 100 crash hotspot nodes are within 1km of a fixed camera. 130 of 155 high-crash-quartile grid cells (84%) have no camera.
  • 70% of top crash roads have cameras: 35 of the 50 highest-crash roads have at least one fixed camera, but notable gaps exist on Plenty Road, West Gate Freeway, Toorak Road, and Chapel Street.

Data and Methodology

Data Sources

Dataset Source Records Period
Crash node data (with lat/lng) VicRoads Open Data 197,409 2014–2023
Accident records VicRoads Open Data 194,437 2014–2023
Accident location (road names) VicRoads Open Data 194,437 2014–2023
Fixed camera locations Victorian Govt (data.vic.gov.au) 287 January 2023
Melbourne urban subset Filtered from above 122,345 2014–2023

Methodology

  • Road-Name Matching: Road names were extracted from camera location descriptions using regex and matched against crash record road names to determine road-level coverage.
  • Spatial Grid Analysis: A 2.2km resolution grid was overlaid on Melbourne (28 x 45 = 1,260 cells). Crashes and cameras were assigned to grid cells. Camera locations were geocoded by matching intersection descriptions to crash node coordinates.
  • Statistical Tests: Point-biserial correlation, Spearman rank correlation, Mann-Whitney U test, Chi-squared test, logistic regression, and nearest-neighbour distance analysis were applied.
  • Severity Weighting: Analysis was repeated using only fatal and serious injury crashes (severity <= 2) to test whether cameras target the most dangerous locations.

Geocoding Approach

The fixed camera dataset contains suburb and intersection text descriptions but no coordinates. Cameras were geocoded by matching their intersection road names to crash node locations in the VicRoads data. Of 287 cameras, 96 were successfully geocoded (1 via exact two-road intersection match, 95 via single-road centroid match, 191 unmatched). This partial geocoding rate means the spatial analysis is conservative — the true camera coverage may be somewhat higher than reported.

Traffic Camera Network Overview

Victoria operates one of Australia's most extensive road safety camera networks. The network includes fixed speed cameras, red-light cameras, mobile speed cameras, and newer distracted driver/seatbelt detection cameras.

Camera Type Approximate Count
Fixed speed cameras ~246
Red light cameras ~175
Approved mobile camera locations ~2,262
Live traffic monitoring cameras ~60
Total enforcement locations ~2,459+

Since September 2024, new fixed road safety camera sites have been activated at Bundoora, Beaufort, Keysborough, Deer Park, Roxburgh Park, and Euroa. The Victorian Government states that camera locations are selected based on crash data and speed survey results.

Top Cameras by Infringements Issued

Location Infringements
Rosanna Rd / Darebin St, Heidelberg 23,807
Warrigal Rd / Batesford Rd, Chadstone 23,401
King St / La Trobe St, West Melbourne 18,502
Fitzroy St / Lakeside Dr, St Kilda 18,107
Flinders St / William St, Melbourne CBD $20.8M revenue (top earner)
Terminal Dr / Centre Rd, Melbourne Airport 11,593

Melbourne Accident Hotspots

Based on VicRoads crash data and the AAMI Crash Index, the following locations are Melbourne's most dangerous for road crashes.

Rank Location Key Issues
1 Plenty Rd, Bundoora Australia's #1 crash hotspot for 6+ years. Multi-lane, tram line, two universities.
2 Springvale Rd, Glen Waverley ~8 collision claims/week. Congestion, tailgating.
3 Maroondah Hwy, Ringwood High-volume arterial, complex intersections.
4 Bell St, Preston Multi-lane arterial through dense urban area.
5 Princes Hwy, Dandenong Driver fatigue, disengagement on long stretches.
6 Springvale Junction One of Australia's worst urban blackspots — 10 sets of traffic lights.
7 Cooper St, Epping Heavy congestion, poor driver awareness.
8 Collins St / Spencer St, CBD Pedestrian-vehicle conflicts in high-density area.
9 West Gate Fwy / CityLink High-speed complex merges, among Melbourne's deadliest.
10 Mountain Hwy / Wantirna Rd, Wantirna Complex turning movements, inconsistent signals.

Crashes by Local Government Area (Top 10)

LGA Total Crashes Fatal Crashes Persons Killed Persons Injured
Melbourne 8,544 44 47 3,000
Casey 8,191 84 87 3,555
Dandenong 6,674 62 63 3,117
Brimbank 5,974 85 87 2,407
Monash 5,798 68 69 2,264
Hume 5,436 50 55 2,020
Moreland 5,266 34 34 1,833
Whittlesea 4,760 43 46 1,703
Darebin 4,661 32 37 1,421
Kingston 4,635 55 56 1,875

Statistical Analysis Results

Road-Name Matching Analysis

Road names were extracted from the 287 fixed camera location descriptions and matched against the top 50 crash roads in Melbourne by total crash count.

Result: 35 of the top 50 crash roads (70.0%) have at least one fixed camera.

Notable roads in the top 50 for crashes that DO NOT have a fixed camera include: Plenty Road, Main Road, North Road, West Gate Freeway, Toorak Road, Chapel Street, Frankston-Dandenong Road, Thompsons Road, Beach Road, Ferntree Gully Road, Boundary Road, Brunswick Road, Eastern Freeway, and CityLink.

Top 20 Crash Roads — Camera Coverage

Road Crashes Has Camera
Princes Hwy 2,763 Yes
High St 2,409 Yes
Nepean Hwy 2,093 Yes
Springvale Rd 1,895 Yes
Monash Fwy 1,892 Yes
Sydney Rd 1,795 Yes
Dandenong Rd 1,767 Yes
Victoria St 1,667 Yes
Station St 1,478 Yes
Burwood Hwy 1,460 Yes
Canterbury Rd 1,409 Yes
South Gippsland Hwy 1,380 Yes
Plenty Rd 1,342 No
Wellington Rd 1,332 Yes
Warrigal Rd 1,298 Yes
Western Ring Rd 1,247 Yes
Stud Rd 1,107 Yes
Pascoe Vale Rd 1,088 Yes
Ballarat Rd 1,042 Yes
Main Rd 1,016 No

Spatial Grid Analysis

A 2.2km resolution grid (28 x 45 = 1,260 cells) was overlaid on the Melbourne metropolitan area.

Metric Value
Melbourne crashes in bounding box 114,809
Active grid cells (with crashes) 619
Grid cells with cameras 42 (6.8%)
Cameras successfully geocoded 96 of 287 (33.4%)
Grid resolution ~2.2km per cell

Correlation Tests

Point-Biserial Correlation — Tests whether crash count (continuous) correlates with camera presence (binary).

Statistic Value Interpretation
Correlation coefficient (r) +0.3560 Moderate positive correlation
p-value < 0.0001 Highly significant

Cameras tend to be placed in grid cells with higher crash counts. The correlation is positive and statistically significant, but moderate in magnitude.

Spearman Rank Correlation — Tests the rank-order relationship between crash count and camera count per grid cell.

Statistic Value Interpretation
Spearman rho +0.2398 Weak-to-moderate positive correlation
p-value < 0.0001 Highly significant

Mann-Whitney U Test

Compares the distribution of crash counts between grid cells that have cameras and those that do not.

Group Median Crashes Mean Crashes
Cells with cameras 341 466.0
Cells without cameras 125 164.8
Statistic Value
U-statistic 18,778
p-value < 0.0001
Effect size (rank-biserial r) -0.5497 (large)
Conclusion Cameras ARE placed in significantly higher-crash areas

Chi-Squared Test

Tests whether there is a significant association between crash density quartile and camera presence.

Crash Quartile No Camera Has Camera Camera Rate
Q1 (Low) 153 2 1.3%
Q2 151 4 2.6%
Q3 143 11 7.1%
Q4 (High) 130 25 16.1%
Statistic Value
Chi-squared 33.1593
p-value < 0.0001
Degrees of freedom 3
Cramer's V (effect size) 0.2315 (medium)
Conclusion Significant association between crash quartile and camera presence

Coverage and Gap Analysis

Crash Density Tier Cells with Cameras Coverage Rate
Top 10% crash cells 15 / 61 24.6%
Top 20% crash cells 22 / 123 17.9%
Top 25% crash cells 25 / 154 16.2%
Bottom 50% crash cells 6 / 309 1.9%

Camera density ratio (top 10% vs bottom 50%): 12.66x

Cameras are 12.7 times more likely to be present in the top 10% of crash zones compared to the bottom 50%. This confirms non-random, crash-informed placement. However, 75% of the highest-crash cells still lack a camera.

Severity-Weighted Analysis

Repeats the correlation analysis using only fatal and serious injury crashes (severity <= 2).

Statistic Value
Point-biserial r (severe crashes vs camera presence) +0.3546
p-value < 0.0001
Mean severe crashes in camera cells 157.3
Mean severe crashes in non-camera cells 57.2

The severity-weighted correlation (+0.355) is nearly identical to the overall correlation (+0.356), indicating that cameras correlate equally with total crash volume and severe crash locations. There is no evidence that cameras specifically prioritise severity over volume.

Nearest-Camera Distance Analysis

For each of the top 100 crash hotspot nodes in Melbourne, the Haversine distance to the nearest geocoded camera was calculated.

Metric Value
Mean distance to nearest camera 3.14 km
Median distance to nearest camera 1.74 km
Minimum distance 0.10 km
Maximum distance 16.60 km
Hotspots within 1km of a camera 28 / 100
Hotspots within 2km of a camera 55 / 100
Hotspots within 5km of a camera 82 / 100

The Spearman correlation between crash count and distance to nearest camera was -0.071 (p = 0.485), meaning there is no significant relationship between how many crashes a location has and how close the nearest camera is.

Top 20 Hotspot Audit

Rank Crashes Fatal Nearest Camera (km) Within 1km
1 74 0 1.14 No
2 47 0 1.99 No
3 47 0 0.54 Yes
4 45 0 16.60 No
5 44 0 2.59 No
6 44 0 1.31 No
7 44 0 7.87 No
8 43 0 1.22 No
9 42 1 1.62 No
10 42 0 0.17 Yes
11 41 0 1.26 No
12 41 0 15.29 No
13 41 1 0.97 Yes
14 39 0 0.70 Yes
15 39 3 4.83 No
16 38 0 4.70 No
17 38 0 2.77 No
18 37 2 2.75 No
19 36 0 0.98 Yes
20 36 0 3.44 No

Logistic Regression

A logistic regression model was fitted to predict camera presence (binary) from crash characteristics per grid cell.

Coefficient Value Interpretation
Intercept -9.7398 Low baseline probability of camera
log(crash_count) +1.2160 More crashes = higher probability of camera
severe_ratio +2.2030 Higher severity proportion = higher probability
McFadden's pseudo R-squared 0.1533 Crash volume explains 15.3% of placement variance

Both crash volume and crash severity are positive predictors of camera presence, but together they explain only 15.3% of the variance in camera placement. Other factors — road design, infrastructure feasibility, political considerations, and potentially revenue — account for the remaining 84.7%.

Summary Scorecard

Metric Value Verdict
Point-biserial r (crashes vs cameras) +0.356 ALIGNED
Spearman rho (crash count vs camera count) +0.240 ALIGNED
Mann-Whitney U (camera vs non-camera cells) p < 0.0001 SIGNIFICANT
Chi-squared (crash quartile vs camera) p < 0.0001 SIGNIFICANT
Cramer's V (effect size) 0.232 MEDIUM
Camera coverage — top 10% crash zones 24.6% MODERATE
Camera coverage — bottom 50% crash zones 1.9% LOW (GOOD)
Top 100 hotspots within 1km of camera 28/100 POOR
Severity correlation +0.355 ALIGNED
Pseudo R-squared (logistic regression) 0.153 MODERATE

Key Gaps: Underserved High-Crash Areas

130 high-crash grid cells (top quartile, Q4) have no fixed camera nearby. The following table lists the 15 most underserved areas ranked by total crash count.

# Area LGA Key Roads Crashes Severe
1 Collingwood / Fitzroy Yarra Alexandra, Johnston, Brunswick 1,480 401
2 Parkville / Royal Park Melbourne Royal Pde, Cemetery Rd 951 310
3 Richmond / Cremorne Yarra Swan, Church, Alexandra 902 309
4 Dandenong South Dandenong Lonsdale, Clow, Stud 812 334
5 Brunswick Moreland Sydney Rd, Brunswick Rd 803 235
6 West Melbourne / Docklands Melbourne CityLink, Spencer, King 803 290
7 Flemington Melbourne Racecourse Rd, Mt Alexander 795 285
8 St Kilda / Balaclava Port Phillip St Kilda Rd, Barkly St 794 308
9 South Melbourne / Docklands Melbourne West Gate, Montague 742 265
10 Preston / Thornbury Darebin St Georges, Bell, Plenty 717 193
11 Balaclava / Caulfield Port Phillip Hotham, Carlisle, Glen Eira 680 269
12 Broadmeadows Hume Pascoe Vale, Western Ring, Camp 664 207
13 Elsternwick / Brighton Bayside Nepean, Glen Huntly, North 612 239
14 Moonee Ponds / Ascot Vale Moonee Valley Mt Alexander, Ascot Vale 590 185
15 Glenroy / Fawkner Hume Sydney, Western Ring, Metro Ring 579 163

Conclusions and Interpretation

The statistical analysis reveals a nuanced picture of camera placement in Victoria:

There IS a statistically significant relationship between camera placement and crash hotspots. Every statistical test confirms that cameras are more likely to be found in higher-crash areas than lower-crash areas. This is not random or purely revenue-driven placement.

However, the relationship is moderate, not strong. The correlation coefficients (0.24–0.36) and pseudo R-squared (0.15) indicate that crash data is one factor among several in placement decisions. Crash volume explains roughly 15% of why a camera is placed where it is.

Coverage of the worst hotspots is poor. Only 28% of the top 100 crash nodes have a camera within 1km. Three-quarters of the highest-crash grid cells have no camera. The mean distance from a major hotspot to the nearest camera is 3.14km.

Notable misalignments exist in both directions. Some of the highest-infringement cameras (Heidelberg, Melbourne Airport, St Kilda) are not major crash hotspots, while some of the worst crash areas (Collingwood, Brunswick, Flemington, West Gate) lack nearby cameras.

Crash causes at hotspots often cannot be addressed by cameras alone. The top hotspots suffer from road design complexity (West Gate/CityLink merge), tram interactions (Plenty Road), congestion (Cooper Street), and complex intersections (Springvale Junction). Speed and red-light cameras address only a subset of crash causes.

Caveats

  • Only 96 of 287 cameras could be geocoded from text descriptions. The true spatial coverage is likely higher.
  • Mobile speed cameras (~2,262 approved locations) were not included. These provide additional coverage that is not captured here.
  • Crash data spans 2014–2023 while camera locations are from January 2023. Some cameras may have been added or removed.
  • Grid resolution of 2.2km may mask fine-grained alignment at the intersection level.
  • The analysis does not account for population density, traffic volume, or road length per grid cell.

Data Sources and References

This analysis was produced by asking an AI assistant to find and analyse public Victorian Government open data. The entire process — from question to finished report — was completed in a single conversation.