ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WAKEFIELD, MA · 2023
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/wakefield/2023-annual-report
Yearly Traffic Safety Analysis
468 CRASHES IN
WAKEFIELD, MA
2023
In Wakefield, total traffic crashes increased by 24.8% from 375 in 2022 to 468 in 2023. This rise was accompanied by a 48.5% increase in total injuries, from 97 to 144. The most notable year-over-year change was a 137.5% increase in the number of hit-and-run incidents, which rose from 24 to 57.
468
▲ 24.8%was 375
Total Crash Events
2
▼ -33.3%was 3
Persons Killed
144
▲ 48.5%was 97
Persons Injured
57
▲ 137.5%was 24
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 33 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic safety trends in Wakefield show a notable increase in crashes year-over-year. Total reported crashes rose by 24.8%, from 375 in 2022 to 468 in 2023. While the number of fatalities decreased from 3 to 2, the number of persons injured increased by 48.5%, from 97 to 144.
57
Hit-and-Run Crashes — 2023
▲ 137.5% vs prior (24)
Hit-and-run incidents showed a significant upward trend, with the number of crashes increasing by 137.5% from 24 in 2022 to 57 in 2023. Correspondingly, the hit-and-run rate, which measures the share of all crashes that are hit-and-runs, nearly doubled from 6.4% in 2022 to 12.2% in 2023.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
12
Pedestrians Injured
5
Cyclists Injured
127
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak hour for crashes was consistent across both periods, occurring in the 5 PM hour with 41 crashes in 2023 and 45 in 2022. However, the peak day of the week shifted from Friday in 2022 (68 crashes) to Thursday in 2023 (77 crashes). Crash counts increased on most weekdays, with Monday and Thursday showing the largest absolute growth.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The fatal crash rate decreased from 0.8% in 2022 to 0.43% in 2023, corresponding to a drop from 3 to 2 fatal crashes. The proportion of crashes resulting in serious injuries also declined from 2.4% to 1.9%. In contrast, the share of crashes involving minor injuries increased from 13.1% of all incidents in 2022 to 16.0% in 2023.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The most significant shift in contributing factors was the rise in crashes attributed to "Inattention," which increased by 90.3% from 31 incidents in 2022 to 59 in 2023, moving from the fourth to the second most common factor. Crashes involving "Failed to yield right of way" also grew, with the count increasing by 51.4% from 35 to 53. While "No improper driving" was the most cited factor in both years (94 in 2023 vs. 93 in 2022), its share of total factors decreased from 24.8% to 20.1%.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The majority of crashes in both 2023 and 2022 occurred under ideal conditions: during daylight, on dry roads, and in clear weather. The proportion of crashes happening in daylight was 68.6% in 2023, compared to 72.5% in 2022. Similarly, crashes on dry roads accounted for 80.6% of incidents in 2023 versus 82.1% in 2022, indicating no significant year-over-year shift in the distribution of conditions.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained consistent, with Honda, Toyota, and Ford leading in both years. In 2023, Honda (116 vehicles) was the most frequent make, slightly ahead of Toyota (114), reversing the order from 2022 when Toyota (102) was more common than Honda (97). The number of persons involved in crashes increased across most demographics, with the largest absolute growth in the 26-34 and 35-44 age groups.
Top Vehicle Makes (916 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
138 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (944 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes were most prevalent in 30 mph zones in both periods, with counts increasing from 212 in 2022 to 222 in 2023. Crashes in 55 mph zones also saw a notable increase from 95 to 129 incidents. In 2023, both fatal crashes occurred in 30 mph zones, whereas 2022 saw two fatalities in 30 mph zones and one in a 55 mph zone.
Fatal crashes by zone: 30 mph: 2 of 222 (0.901%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.
Data Retrieval
- Access method: Arcgis_yearly Open Data API (SoQL queries)
- Data format: Structured JSON via REST API
- Record types queried: Crash events, person records, and vehicle unit records
- Date filter applied: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: WAKEFIELD, MA
- Total crash records analyzed: 468
- Total persons involved: 1,103
- Total vehicles involved: 916
Analytical Methodology
- Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
- Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
- Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
- Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
- Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
- Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
- AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.
Limitations & Disclaimers
- Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
- Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
- Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
- AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
- Percentages are calculated from reported data and are subject to rounding.
Non-Affiliation Disclosure
This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.
Data License
The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.
Corrections & Feedback
If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.
Suggested Citation
ThatCarHitMe.com (Injuria.ai). "WAKEFIELD, MA Crash Intelligence Report: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/wakefield/2023-annual-report
About the Publisher
ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.
Questions about this report's data or methodology: data@injuria.ai
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved