ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LITTLETON, 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/littleton/2024-annual-report
Yearly Traffic Safety Analysis
307 CRASHES IN
LITTLETON, MA
2024
In Littleton, total traffic crashes decreased by 3.5% from 318 in 2023 to 307 in 2024. Despite this overall reduction in collisions, the number of people injured increased by 13.4%, rising from 67 to 76 year-over-year. The total number of fatalities remained unchanged at three for both periods.
307
▼ -3.5%was 318
Total Crash Events
3
Persons Killed
76
▲ 13.4%was 67
Persons Injured
12
▲ 9.1%was 11
Hit-and-Run Crashes
Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 crash trends show a slight decrease in frequency but an increase in severity. Total collisions fell from 318 to 307, a 3.5% reduction. However, total injuries rose from 67 to 76, while fatalities held steady at 3, indicating that while fewer crashes occurred, those that did were more likely to result in injury.
12
Hit-and-Run Crashes — 2024
▲ 9.1% vs prior (11)
The number of hit-and-run crashes saw a slight increase, rising from 11 incidents in 2023 to 12 in 2024. This change resulted in a marginal increase in the hit-and-run rate, which grew from 3.5% of total crashes in the prior year to 3.9% in the current year. The data indicates a slight upward trend for this crash type.
Vulnerable Road User Casualties
3
Motorists Killed
76
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 remained largely consistent year-over-year. Thursday was the peak day for crashes in both 2024 (56 crashes) and 2023 (59 crashes). The peak hour for collisions shifted slightly, moving from 4 p.m. in 2023 (29 crashes) to 3 p.m. in 2024 (29 crashes), though the late afternoon commute period remained the most frequent time for incidents in both years.
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
While the number of fatal crashes was unchanged at 3 in both periods, the fatal crash rate increased slightly from 0.94% in 2023 to 0.98% in 2024 due to the lower total crash volume. The proportion of crashes resulting in an injury rose, with non-fatal injury crashes accounting for 16.9% of incidents in 2024 compared to 12.9% in 2023. Correspondingly, the share of crashes with no reported injuries decreased from 85.5% to 80.5%.
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 contributing factors remained consistent, with 'No improper driving,' 'Inattention,' and 'Followed too closely' leading in both years. The count of crashes attributed to 'Followed too closely' decreased from 51 to 45, and 'Inattention' dropped from 50 to 47. Notably, crashes due to 'Driving too fast for conditions' fell by 60% in count, from 20 incidents in 2023 to 8 in 2024, while crashes for 'Exceeded authorized speed limit' increased by 80% in count, from 5 to 9.
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 environmental conditions showed a shift towards more crashes occurring in daylight, which accounted for 74.0% of incidents in 2024 compared to 67.9% in 2023. Correspondingly, crashes in dark conditions decreased from 76 to 61. The proportions of crashes on dry versus wet road surfaces, and during clear versus adverse weather, remained relatively stable between the two periods.
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 were Toyota, Honda, and Ford in both years, with their rankings remaining stable. The age distribution of persons involved in crashes shifted, with the 45-54 age group increasing from 71 individuals in 2023 to 97 in 2024. In contrast, the number of individuals in the 26-34 and 35-44 age groups decreased over the same period.
Top Vehicle Makes (566 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
29 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (604 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
A shift occurred in the speed zones where crashes were most prevalent, moving from higher to lower speed areas. Crashes in 65 mph zones decreased from 85 to 73, and incidents in 55 mph zones fell from 49 to 34. Conversely, crashes in 35 mph zones increased from 34 to 43. In 2024, two of the three fatal crashes occurred in 35 mph zones, whereas in 2023, fatalities were recorded in 45, 55, and 65 mph zones.
Fatal crashes by zone: 35 mph: 2 of 43 (4.651%)
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: LITTLETON, MA
- Total crash records analyzed: 307
- Total persons involved: 647
- Total vehicles involved: 566
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). "LITTLETON, 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/littleton/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