ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SALEM, MA · FEBRUARY 2023
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/salem/february-2023-report
Monthly Traffic Safety Analysis
33 CRASHES IN
SALEM, MA
FEBRUARY 2023
Total crashes in Salem decreased significantly from 61 in February 2022 to 33 in February 2023, representing a 45.9% reduction. Concurrently, total injuries decreased from 15 to 8, a 46.7% decrease. This substantial decline in both crashes and injuries is the most notable year-over-year shift.
33
▼ -45.9%was 61
Total Crash Events
0
Persons Killed
8
▼ -46.7%was 15
Persons Injured
5
▼ -28.6%was 7
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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for Salem indicates a significant downward trend year-over-year. Total crashes decreased by 45.9%, falling from 61 in February 2022 to 33 in February 2023. This reduction is also reflected in a 46.7% decrease in total injuries, from 15 to 8.
5
Hit-and-Run Crashes — February 2023
▼ -28.6% vs prior (7)
The number of hit-and-run crashes decreased from 7 in February 2022 to 5 in February 2023. However, the hit-and-run rate increased from 11.5% of total crashes in the prior period to 15.2% in the current period, indicating an upward trend in the proportion of these incidents.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
2
Pedestrians Injured
6
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Tuesday, with 13 incidents in February 2022, to Saturday, with 7 incidents in February 2023. Similarly, the peak hour changed from 5 PM (6 crashes) in the prior period to 12 AM (8 crashes) in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes in either February 2022 or February 2023. The number of crashes resulting in minor injuries decreased from 5 to 3, while crashes with possible injuries decreased from 5 to 4. The proportion of crashes with minor injuries slightly increased from 8.2% to 9.1% of total crashes, and possible injury crashes increased from 8.2% to 12.1% of total crashes.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Most severe injury per crash record
Top Contributing Factors
The contributing factor 'Followed too closely' increased by 2 crashes, from 4 in February 2022 to 6 in February 2023, making it the most frequent factor in the current period. Conversely, 'Failed to yield right of way' decreased by 2 crashes, from 5 to 3, a 40% reduction in count. 'Failure to keep in proper lane or running off road' increased from 2 to 4 crashes, a 100% increase in count.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The number of crashes occurring in 'Clear/Clear' weather conditions decreased from 44 in February 2022 to 23 in February 2023. Crashes on 'Dry' road surfaces also saw a reduction, from 39 to 29. Similarly, crashes during 'Daylight' conditions decreased from 35 to 20, and crashes in 'Dark - lighted roadway' conditions decreased from 23 to 11.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased from 113 in February 2022 to 60 in February 2023. Honda, which was the most frequently involved make in the prior period with 28 vehicles, saw its involvement decrease to 7 vehicles. Toyota became the most frequently involved make in the current period with 10 vehicles, down from 14 in the prior period.
Top Vehicle Makes (60 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Vehicle unit records
7 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (70 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 25 mph speed zone decreased from 18 in February 2022 to 11 in February 2023. Crashes in the 30 mph speed zone also decreased, from 9 to 5. Conversely, crashes in the 1 mph speed zone increased from 1 to 3.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · 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: 2023-02-01 through 2023-02-28
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-02-01 through 2023-02-28 (28 days)
- Geographic scope: SALEM, MA
- Total crash records analyzed: 33
- Total persons involved: 77
- Total vehicles involved: 60
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). "SALEM, MA Crash Intelligence Report: February 2023." Published June 21, 2026. Reporting period: 2023-02-01 to 2023-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/salem/february-2023-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: 2023-02-01 – 2023-02-28
Generated: June 21, 2026 · All rights reserved