ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SALEM, MA · JANUARY 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/january-2023-report
Monthly Traffic Safety Analysis
53 CRASHES IN
SALEM, MA
JANUARY 2023
Total crashes in Salem remained stable year-over-year, with 53 crashes reported in both January 2022 and January 2023. The most significant year-over-year shift was a 100% decrease in total fatalities, from 1 in the prior period to 0 in the current period. Hit-and-run crashes also saw a substantial reduction, decreasing by 66.7% from 6 to 2 incidents.
53
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
14
Persons Injured
2
▼ -66.7%was 6
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. 3 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a stable number of total crashes in Salem, with 53 incidents recorded in January 2023, identical to January 2022. Despite this stability in crash volume, there was a positive trend in safety outcomes, with a 100% decrease in total fatalities year-over-year, from 1 to 0. Total injuries remained constant at 14 for both periods.
2
Hit-and-Run Crashes — January 2023
▼ -66.7% vs prior (6)
Hit-and-run crashes decreased significantly year-over-year, from 6 incidents in January 2022 to 2 in January 2023. This represents a 66.7% decrease in the count of hit-and-run crashes, and the hit-and-run rate declined from 11.3% of all crashes to 3.8%.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
13
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes shifted from Saturday in January 2022, which had 11 crashes, to Tuesday in January 2023, with 14 crashes. The peak crash hour also changed, moving from 3 PM in the prior period (7 crashes) to 5 PM in the current period (5 crashes). This indicates a shift in the most frequent times and days for crash occurrences.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Total fatalities decreased by 100%, from 1 in January 2022 to 0 in January 2023, with no fatal crashes reported in the current period compared to 1 in the prior period. The total number of injured persons remained stable at 14 across both periods. Crashes resulting in minor injuries decreased from 3 (5.7% of crashes) to 2 (3.8% of crashes), while crashes with possible injuries increased from 9 (17% of crashes) to 10 (18.9% of crashes).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record
Top Contributing Factors
Crashes attributed to 'Failed to yield right of way' increased by 4 incidents, from 5 in January 2022 to 9 in January 2023, representing an 80% increase in count. 'Distracted' driving and 'Disregarded traffic signs, signals, road markings' both saw a 200% increase in count, rising from 1 to 3 crashes each. Conversely, crashes due to 'Followed too closely' decreased by 2 incidents, from 3 to 1, a 66.7% reduction in count.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear/Clear' weather decreased from 33 in January 2022 to 22 in January 2023, while crashes in 'Rain/Rain' conditions increased from 3 to 11. Similarly, crashes in 'Snow/Snow' conditions rose from 2 to 10. Crashes occurring in 'Dark - lighted roadway' conditions increased from 16 to 24, while those in 'Daylight' decreased from 30 to 23. Crashes on 'Dry' road surfaces decreased from 35 to 19, whereas crashes on 'Wet' surfaces increased from 6 to 18, and on 'Slush' surfaces from 1 to 5.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field
Vehicles & Demographics
The total number of persons involved in crashes decreased from 169 in January 2022 to 121 in January 2023. This reduction was significantly influenced by a decrease in persons aged 0-15 (from 45 to 6) and 16-20 (from 23 to 7). Honda remained the most frequently involved vehicle make, though its count decreased from 21 to 19, while Toyota increased from 10 to 13, and Subaru increased from 4 to 6.
Top Vehicle Makes (101 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records
7 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (114 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in 25 mph zones increased from 16 in January 2022 to 20 in January 2023, while crashes in 30 mph zones decreased from 13 to 5. In 35 mph zones, crashes increased from 2 to 5, and the fatal crash rate for this speed zone decreased from 50% (1 fatal crash out of 2) in the prior period to 0% (0 fatal crashes out of 5) in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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: 2023-01-01 through 2023-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-01-31 (31 days)
- Geographic scope: SALEM, MA
- Total crash records analyzed: 53
- Total persons involved: 121
- Total vehicles involved: 101
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: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/salem/january-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: 2023-01-01 – 2023-01-31
Generated: June 21, 2026 · All rights reserved