ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BEDFORD, MA · 2025
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/bedford/2025-annual-report
Yearly Traffic Safety Analysis
228 CRASHES IN
BEDFORD, MA
2025
In Bedford, total crashes remained nearly stable, with 228 incidents in 2025 compared to 229 in 2024, a decrease of less than one percent. While overall collision volume was consistent, the most notable year-over-year shift was a 133% increase in crashes involving speeding, which rose from 12 to 28 incidents. Additionally, hit-and-run crashes increased from 2 to 5.
228
▼ -0.4%was 229
Total Crash Events
2
▼ -33.3%was 3
Persons Killed
52
▲ 8.3%was 48
Persons Injured
5
▲ 150.0%was 2
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.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends in Bedford were stable year-over-year, with a negligible decrease of one incident from 229 in 2024 to 228 in 2025. This represents a 0.4% decline in total collisions. Despite this stability in the total count, the number of people reported injured increased by 8.3%, from 48 to 52.
5
Hit-and-Run Crashes — 2025
▲ 150.0% vs prior (2)
The number of hit-and-run incidents in Bedford more than doubled year-over-year, increasing 150% from 2 in 2024 to 5 in 2025. Consequently, the hit-and-run rate rose from 0.9 per 100 crashes in the prior period to 2.2 in the current period, indicating a rising trend for this crash type.
Vulnerable Road User Casualties
1
Pedestrians Killed
1
Cyclists Killed
0
Motorists Killed
4
Pedestrians Injured
1
Cyclists Injured
47
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes in Bedford remained consistent year-over-year. Wednesday was the peak day for crashes in both 2025 (45 crashes) and 2024 (55 crashes), though the count on this day decreased. Similarly, the 4 p.m. hour was the most frequent time for collisions in both periods, with 26 crashes in the current year and 27 in the prior year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes saw a notable shift, with the number of fatal crashes decreasing from 3 to 2 and the number of serious injury crashes dropping from 2 to 0. Consequently, the fatal crash rate declined from 1.31 per 100 crashes in 2024 to 0.88 in 2025. Conversely, the count of minor injury crashes rose from 24 to 34, contributing to an overall increase in persons injured.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The top two contributing factors remained 'Followed too closely' and 'Failed to yield right of way' in both periods, although the count for each decreased from 57 to 50 and 46 to 39, respectively. The most significant year-over-year change was a surge in crashes attributed to 'Driving too fast for conditions,' which increased by 340% from 5 incidents in 2024 to 22 in 2025. This factor rose from the 10th most common cause to the 4th.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
While the number of crashes in daylight conditions remained identical at 171 for both years, there was a notable increase in collisions occurring on adverse road surfaces. Crashes on snowy or icy roads rose significantly from a combined 8 incidents in 2024 to 27 in 2025. This corresponds with a decrease in crashes on dry roads, which fell from 181 to 165. The share of crashes on non-dry surfaces increased from 20.5% to 27.6% year-over-year.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes were highly consistent, with Toyota (73), Honda (61-62), and Ford (35) as the top three in both periods with nearly identical counts. Regarding persons involved, there was a shift in age demographics, with the number of individuals aged 35-44 increasing from 83 to 97. Conversely, involvement decreased for younger age groups, with the 16-20 group falling from 53 to 39 and the 21-25 group falling from 61 to 48.
Top Vehicle Makes (405 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
18 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (501 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
A significant shift occurred in the speed zones where crashes happened, with collisions moving toward higher speed areas. Crashes in 40 mph zones increased from 37 to 57, and incidents in 65 mph zones rose from 19 to 28. In contrast, crashes in 30 mph zones decreased from 59 to 50. Fatal crashes also shifted, with the current year's fatalities occurring in 25 mph and 35 mph zones, while the prior year's were in 30, 35, and 65 mph zones.
Fatal crashes by zone: 25 mph: 1 of 59 (1.695%) · 35 mph: 1 of 29 (3.448%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: BEDFORD, MA
- Total crash records analyzed: 228
- Total persons involved: 524
- Total vehicles involved: 405
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). "BEDFORD, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/bedford/2025-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: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved