ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SALEM, MA · 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/2023-annual-report
Yearly Traffic Safety Analysis
514 CRASHES IN
SALEM, MA
2023
In 2023, Salem recorded 514 total vehicle crashes, a 22.3% decrease from the 662 crashes reported in 2022. Despite the overall reduction in collisions, the number of fatalities increased from 3 in 2022 to 4 in 2023. The most notable shift was the doubling of fatal crash events, which rose from 2 to 4 year-over-year.
514
▼ -22.4%was 662
Total Crash Events
4
▲ 33.3%was 3
Persons Killed
180
▼ -2.7%was 185
Persons Injured
41
▼ -16.3%was 49
Hit-and-Run Crashes
Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 25 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 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic collisions shows a significant year-over-year improvement, with total crashes falling by 22.3% from 662 in 2022 to 514 in 2023. The number of people injured also saw a slight decrease from 185 to 180. However, this positive trend did not extend to crash lethality, as total fatalities rose from 3 to 4 during the same period.
41
Hit-and-Run Crashes — 2023
▼ -16.3% vs prior (49)
The total number of hit-and-run crashes decreased from 49 in 2022 to 41 in 2023. However, due to the larger overall drop in total crashes, the hit-and-run rate as a percentage of all incidents slightly increased. In 2023, hit-and-runs constituted 8.0% of all incidents, up from 7.4% in the previous year.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
3
Motorists Killed
0
Other Killed
18
Pedestrians Injured
9
Cyclists Injured
151
Motorists Injured
2
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day of the week for crashes shifted from Monday (112 crashes) in 2022 to Friday (81 crashes) in 2023. The peak hour for collisions remained consistent at 3 p.m. in both years. However, the number of crashes during that peak hour decreased from 57 in 2022 to 37 in 2023, reflecting the overall decline in crash volume.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes decreased, the severity of those crashes worsened in 2023 compared to 2022. The number of fatal crashes doubled from 2 to 4, and the fatal crash rate increased from 0.3% to 0.8% of all collisions. The proportion of crashes resulting in an injury or fatality also increased, rising from 20.1% of all crashes in 2022 to 29.6% in 2023.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor in both periods was 'Failed to yield right of way,' which held steady at 87 incidents, though its share of total crashes increased from 13.1% to 16.9%. The count of crashes attributed to 'No improper driving' decreased by 22.4%, from 85 in 2022 to 66 in 2023. Similarly, crashes from 'Followed too closely' fell from 62 to 51, a 17.7% reduction in count.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions remained broadly similar between the two periods, with the majority of incidents in both years occurring in daylight (62.8% in 2023 vs 62.4% in 2022) and on dry roads (78.2% in 2023 vs 81.6% in 2022). There was a slight increase in the proportion of crashes on wet roads, which accounted for 16.9% of crashes in 2023, up from 12.8% in 2022. Crashes during rainy conditions also saw a proportional increase, from 8.9% to 11.7% of the total.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes shifted slightly, with Toyota (175 vehicles) overtaking Honda (150 vehicles) for the top rank in 2023, reversing their positions from 2022 when Honda led with 233 vehicles. The total number of people involved in crashes decreased by 26%, from 1,642 to 1,215. The age distribution of persons involved remained proportionally consistent, with all age groups seeing a decrease in raw numbers, led by the 26-34 age group which saw its count drop from 316 to 200.
Top Vehicle Makes (973 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
93 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,114 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones increased from 204 in 2022 to 215 in 2023 and included 2 fatal crashes, whereas there were no fatalities in this zone the prior year. Conversely, crashes in 30 mph zones saw a significant reduction, falling from 90 to 55. In 2023, one fatal crash occurred in a 45 mph zone, similar to 2022, which also recorded one fatality in that speed zone.
Fatal crashes by zone: 25 mph: 2 of 215 (0.93%) · 45 mph: 1 of 6 (16.667%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: SALEM, MA
- Total crash records analyzed: 514
- Total persons involved: 1,215
- Total vehicles involved: 973
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/salem/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved