ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · ARLINGTON, 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/arlington/2023-annual-report
Yearly Traffic Safety Analysis
368 CRASHES IN
ARLINGTON, MA
2023
In 2023, Arlington recorded 368 total traffic crashes, a slight increase from the 363 crashes documented in 2022, representing a 1.4% year-over-year rise. While overall crash numbers remained relatively stable, the most significant change was a sharp increase in bicycle-involved incidents. The number of bicycle crashes more than tripled, rising from 5 in 2022 to 18 in 2023.
368
▲ 1.4%was 363
Total Crash Events
0
Persons Killed
79
▲ 8.2%was 73
Persons Injured
38
▼ -13.6%was 44
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. 16 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
Overall traffic crash trends in Arlington showed a slight increase between 2022 and 2023. The total number of crashes rose by 1.4%, from 363 to 368. Similarly, the number of persons injured in these incidents increased by 8.2%, from 73 in 2022 to 79 in 2023, while fatalities remained at zero for both years.
38
Hit-and-Run Crashes — 2023
▼ -13.6% vs prior (44)
The incidence of hit-and-run crashes in Arlington showed a downward trend from 2022 to 2023. The total number of hit-and-run incidents decreased from 44 to 38. This corresponds to a drop in the hit-and-run rate, which fell from 12.1% of all crashes in 2022 to 10.3% in 2023.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
7
Pedestrians Injured
15
Cyclists Injured
55
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 temporal patterns of crashes saw minor shifts between the two periods. In 2023, the peak day for crashes was Wednesday with 69 incidents, a change from 2022 when Tuesday was the peak day with 71 incidents. The busiest hour for crashes also shifted slightly earlier, from the 5 p.m. hour in 2022 (48 crashes) to the 4 p.m. hour in 2023 (39 crashes).
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
Crash severity distribution remained broadly similar year-over-year, with no fatal crashes recorded in either 2022 or 2023. The count of serious injury crashes was unchanged at 6 for both periods. However, there was a shift within non-serious injury categories; crashes resulting in minor injuries increased from 40 to 48, while those with possible injuries decreased from 20 to 16. The proportion of crashes resulting in no injury at all rose from 74.4% in 2022 to 76.6% 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 years was 'No improper driving', with its count remaining stable at 145 crashes in 2023 compared to 149 in 2022. 'Failed to yield right of way' was the second most common factor in both periods, though its count decreased by 25% from 36 incidents in 2022 to 27 in 2023. Notably, crashes attributed to 'Followed too closely' increased by 90% in count, rising from 10 to 19 incidents, which moved it to the third most frequent factor in 2023.
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
The distribution of crashes by lighting conditions remained consistent, with approximately 70% of incidents in both 2022 and 2023 occurring during daylight hours. However, there was a notable shift in conditions related to precipitation. The number of crashes on wet roads increased from 54 to 73, and incidents during rainy weather rose from 26 to 49. Conversely, crashes attributed to snowy conditions decreased, falling from 19 in 2022 to 9 in 2023.
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 were consistent year-over-year, with Toyota, Honda, and Ford being the most common in both 2022 and 2023. The total number of vehicles involved in incidents saw a slight decrease from 634 to 620. Analysis of persons involved shows a shift in age demographics, with a notable increase in the number of individuals aged 65 and older (from 80 to 112) and those aged 26-34 (from 102 to 128).
Top Vehicle Makes (620 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
125 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (675 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 continued to be the most frequent, although their count decreased from 248 in 2022 to 234 in 2023. There were notable shifts in other speed zones, with crashes in 30 MPH zones increasing significantly from 40 to 71, and incidents in 55 MPH zones more than doubling from 11 to 25. Conversely, the number of crashes in 35 MPH zones saw a sharp decline from 24 to 10. No fatal crashes were recorded in any speed zone during either period.
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: ARLINGTON, MA
- Total crash records analyzed: 368
- Total persons involved: 802
- Total vehicles involved: 620
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). "ARLINGTON, 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/arlington/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