ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WATERTOWN, MA · 2024
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/watertown/2024-annual-report
Yearly Traffic Safety Analysis
346 CRASHES IN
WATERTOWN, MA
2024
In 2024, Watertown recorded 346 total vehicle crashes, a 4.5% increase from the 331 crashes documented in 2023. Despite the rise in total incidents, the number of reported injuries decreased by 26.7% from 131 to 96. Notably, there were zero fatalities in 2024, compared to one fatality in the prior year.
346
▲ 4.5%was 331
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
96
▼ -26.7%was 131
Persons Injured
7
▲ 75.0%was 4
Hit-and-Run Crashes
Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 5 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic crash volume in Watertown saw a slight increase of 4.5% year-over-year, rising from 331 incidents in 2023 to 346 in 2024. However, the severity of these crashes trended downward, with total injuries falling by 26.7% and fatalities dropping from one to zero. This suggests a higher frequency of lower-severity incidents in the current period.
7
Hit-and-Run Crashes — 2024
▲ 75.0% vs prior (4)
The number of hit-and-run crashes increased by 75% year-over-year, rising from 4 incidents in 2023 to 7 in 2024. Consequently, the hit-and-run rate, representing the percentage of total crashes that were hit-and-runs, also trended upward. The rate increased from 1.2% in the prior year to 2.0% in the current year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
11
Pedestrians Injured
8
Cyclists Injured
73
Motorists Injured
4
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed some shifts between the two periods. While Wednesday remained the peak day for crashes in both 2023 and 2024 with 64 incidents each, the peak hour shifted from 1 PM (29 crashes) in 2023 to the 5 PM evening commute hour (32 crashes) in 2024. Notably, the number of crashes on Saturdays more than doubled, increasing from 19 to 47, while incidents on Thursdays and Fridays saw a marked decrease.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes decreased year-over-year, as there was one fatal crash in 2023 and none in 2024. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) fell from 32.9% of all crashes in 2023 to 22.9% in 2024. Correspondingly, the share of property-damage-only crashes increased from 65% of all incidents in the prior year to 75.7% in the current year.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
The top three contributing factors remained consistent across both years: 'Failed to yield right of way,' 'Inattention,' and 'No improper driving.' While the leading factor, 'Failed to yield right of way,' saw a slight decrease in count from 84 to 81 incidents, crashes attributed to 'Inattention' increased by 15.3% (from 59 to 68). The count of crashes involving an erratic or reckless manner of driving more than doubled, rising from 4 in 2023 to 9 in 2024.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions were broadly similar year-over-year, with the majority of incidents in both periods occurring in daylight (70.8% in 2024 vs. 71.3% in 2023) and on dry roads (81.8% in 2024 vs. 77.9% in 2023). There was a notable decrease in the proportion of crashes happening on wet road surfaces, which fell from 20.2% of all crashes in 2023 to 14.5% in 2024. Crashes during rainy conditions also saw a proportional decline.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained the same across both periods with relatively stable involvement counts. Analysis of persons involved in crashes shows a notable demographic shift. While the 26-34 age group was the most represented in both periods, the number of individuals aged 65 and older involved in crashes increased by 40%, from 82 in 2023 to 115 in 2024.
Top Vehicle Makes (656 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
69 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (744 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones remained highly consistent, with the vast majority of incidents in both years occurring in 30 mph zones (318 crashes in 2024 vs. 303 in 2023). There was no significant shift of crashes into higher or lower speed zones. The single fatality recorded in 2023 occurred in a 30 mph zone; in 2024, there were no fatal crashes in any speed zone.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: WATERTOWN, MA
- Total crash records analyzed: 346
- Total persons involved: 816
- Total vehicles involved: 656
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). "WATERTOWN, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/watertown/2024-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: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved