ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · HARVARD, 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/harvard/2024-annual-report
Yearly Traffic Safety Analysis
180 CRASHES IN
HARVARD, MA
2024
In 2024, Harvard recorded 180 total traffic crashes, a decrease from the 203 crashes reported in 2023, representing an 11.3% decline. Despite the overall reduction in collisions, the total number of injuries rose from 46 to 62, a 34.8% increase year-over-year. There were no fatal crashes reported in either period.
180
▼ -11.3%was 203
Total Crash Events
0
Persons Killed
62
▲ 34.8%was 46
Persons Injured
4
▼ -55.6%was 9
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
The total number of crashes in Harvard decreased by 11.3% from 203 in 2023 to 180 in 2024. However, this downward trend in collisions did not extend to crash outcomes, as the number of people injured increased by 34.8% from 46 to 62. Fatalities remained at zero for both years.
4
Hit-and-Run Crashes — 2024
▼ -55.6% vs prior (9)
Hit-and-run incidents decreased significantly year-over-year. The number of hit-and-run crashes fell by 55.6%, from 9 in 2023 to 4 in 2024. Correspondingly, the hit-and-run rate, as a percentage of total crashes, was halved from 4.4% to 2.2%.
Vulnerable Road User Casualties
0
Motorists Killed
0
Other Killed
61
Motorists Injured
1
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. In 2024, the peak day for crashes was Thursday with 38 incidents, a change from 2023 when Monday was the peak day with 37 incidents. The morning rush hour remained a key time for collisions, though the peak hour shifted slightly earlier from 7 a.m. in 2023 (20 crashes) to 6 a.m. in 2024 (16 crashes).
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
There were no fatal crashes in either 2023 or 2024. However, the proportion of crashes resulting in some form of injury increased from 29.2% in 2023 to 33.3% in 2024. The share of crashes classified as 'Serious Injury' grew from 1.5% to 2.2%, and 'Possible Injury' crashes increased from 6.9% to 10.0% of the total. Consequently, the proportion of 'No Injury' crashes decreased from 77.8% to 73.9% year-over-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
While 'No improper driving' remained the most common finding in both years, its count decreased from 42 to 35. A notable change was the significant reduction in crashes attributed to 'Driving too fast for conditions,' which fell by 45.7% from 35 incidents in 2023 to 19 in 2024. Conversely, crashes involving 'Failure to keep in proper lane or running off road' increased by 33.3% from 12 to 16 incidents. 'Followed too closely' also saw a decrease in count from 35 to 29, but it rose from the third to the second most cited factor 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
The distribution of crashes across environmental conditions showed a marked decrease in incidents during adverse winter weather. The proportion of crashes occurring on snowy roads fell from 14.3% in 2023 to 8.3% in 2024, and those during snowy weather dropped from 9.4% to 4.4%. Conversely, the share of crashes on wet roads saw a slight increase. The proportion of crashes occurring in daylight decreased from 65.0% to 60.0%, with a corresponding increase in the share of crashes happening after dark.
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 consistent, with Toyota, Honda, and Ford leading in both 2023 and 2024. Toyota's involvement increased from 50 to 59 vehicles, while Honda and Ford saw their numbers decrease. The age demographics of persons involved in crashes also shifted, with the 35-44 age group becoming the most represented cohort in 2024 (61 people), replacing the 26-34 age group which was largest in 2023 (80 people).
Top Vehicle Makes (309 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
14 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (337 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
In both years, the 55 mph speed zone accounted for the highest number of crashes, though the count in this zone decreased from 99 in 2023 to 75 in 2024. Crashes in 30 mph zones also saw a notable reduction, falling from 23 to 15 incidents. The overall pattern suggests a decrease in collisions within several key speed zones, with no fatal crashes recorded in any zone for either period.
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: HARVARD, MA
- Total crash records analyzed: 180
- Total persons involved: 375
- Total vehicles involved: 309
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). "HARVARD, 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/harvard/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