ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NORTHBOROUGH, 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/northborough/2024-annual-report
Yearly Traffic Safety Analysis
307 CRASHES IN
NORTHBOROUGH, MA
2024
In Northborough, total traffic crashes increased by 6.6%, from 288 in 2023 to 307 in 2024. While fatalities dropped from one to zero and total injuries decreased from 95 to 82, the most significant year-over-year change was in hit-and-run incidents, which saw a 163.6% increase in count from 11 to 29.
307
▲ 6.6%was 288
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
82
▼ -13.7%was 95
Persons Injured
29
▲ 163.6%was 11
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. 4 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 crashes in Northborough trended upwards, increasing by 6.6% from 288 incidents in 2023 to 307 in 2024. Despite the rise in total crashes, the number of people injured decreased by 13.7% from 95 to 82. Fatalities also decreased, with zero recorded in 2024 compared to one in the prior year.
29
Hit-and-Run Crashes — 2024
▲ 163.6% vs prior (11)
Hit-and-run crashes increased significantly year-over-year. The number of hit-and-run incidents rose by 163.6%, from 11 in 2023 to 29 in 2024. Consequently, the hit-and-run rate as a percentage of total crashes more than doubled, climbing from 3.8% in the prior period to 9.4% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
81
Motorists 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. The peak day for crashes moved from Thursday (49 incidents) in 2023 to Tuesday (53 incidents) in 2024. However, the peak hour for collisions remained consistent, occurring in the 4 PM hour in both years, with 36 crashes in 2024 and 35 in 2023.
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
Crash severity decreased year-over-year, with zero fatal crashes recorded in 2024 compared to one in 2023. The proportion of non-injury crashes increased from 74.7% of all incidents in 2023 to 78.8% in 2024. Notably, there were no crashes classified with 'Serious Injury' in 2024, whereas four such crashes occurred in the prior 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 leading contributing factors remained consistent, with 'Failure to keep in proper lane' (56 crashes in 2024 vs. 55 in 2023) and 'Failed to yield right of way' (49 vs. 50) as the top two causes. However, the count of crashes attributed to 'Driving too fast for conditions' increased by 92.3%, from 13 incidents to 25. Similarly, the count for crashes involving 'Disregarded traffic signs, signals, road markings' rose by 58.8% from 17 to 27.
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
The majority of crashes in both periods occurred in clear weather and on dry roads. In 2024, 78.8% of crashes were on dry surfaces, a slight increase from 77.1% in 2023. Correspondingly, the proportion of crashes on wet roads decreased from 18.4% in 2023 to 12.7% in 2024. Crashes during daylight hours remained the predominant condition, accounting for 201 of 307 crashes in 2024 and 205 of 288 in the prior year.
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 remained Toyota, Honda, and Ford for both periods, with the count for Toyota increasing from 78 to 87 and Ford from 54 to 66. A notable shift occurred in the age demographics of persons involved in crashes; in 2023, the 45-54 age group was most represented with 108 individuals, but in 2024, the 26-34 age group became the largest at 103 individuals.
Top Vehicle Makes (523 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
95 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (666 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
There was a noticeable shift in the distribution of crashes across speed zones. Crashes in 30 mph zones increased by 56.6%, from 53 in 2023 to 83 in 2024. Conversely, crashes in 35 mph zones decreased by 35.3%, from 68 to 44. The single fatal crash in 2023 occurred in a 65 mph zone; no fatal crashes were recorded in any speed zone in 2024.
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: NORTHBOROUGH, MA
- Total crash records analyzed: 307
- Total persons involved: 711
- Total vehicles involved: 523
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). "NORTHBOROUGH, 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/northborough/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