ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LEXINGTON, MA · 2022
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/lexington/2022-annual-report
Yearly Traffic Safety Analysis
533 CRASHES IN
LEXINGTON, MA
2022
In Lexington, total traffic crashes rose from 438 in 2021 to 533 in 2022, a 21.7% year-over-year increase. The most notable shift in the data was the recording of one fatal crash in 2022, whereas there were no fatal crashes in the prior year. This increase in total crashes was accompanied by a 26.5% rise in the number of people injured.
533
▲ 21.7%was 438
Total Crash Events
1
Persons Killed
124
▲ 26.5%was 98
Persons Injured
43
▲ 16.2%was 37
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic safety metrics in Lexington showed a negative trend, with crashes rising from 438 in 2021 to 533 in 2022, a 21.7% increase. This upward trend was consistent across key indicators, as total injuries increased by 26.5% from 98 to 124, and the city recorded one fatality after having none in the previous year.
43
Hit-and-Run Crashes — 2022
▲ 16.2% vs prior (37)
The absolute number of hit-and-run crashes increased from 37 in 2021 to 43 in 2022. However, because total crashes rose at a faster pace, the hit-and-run rate as a percentage of all crashes saw a slight decrease. The rate trended down from 8.4% of all crashes in 2021 to 8.1% in 2022.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
4
Pedestrians Injured
9
Cyclists Injured
111
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The typical time for crashes shifted between periods. The peak day moved from Friday in 2021 (87 crashes) to Wednesday in 2022 (92 crashes). A more pronounced change occurred in the peak hour, which shifted from the 4 p.m. evening commute hour in 2021 (54 crashes) to the 8 a.m. morning commute hour in 2022 (47 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity increased year-over-year, marked by the appearance of one fatal crash in 2022 after none were recorded in 2021, raising the fatal crash rate from 0% to 0.19%. The proportion of crashes resulting in any injury also grew, accounting for 19.5% of all incidents in 2022 compared to 17.4% in 2021. While the count of serious injury crashes remained stable at four, minor and possible injury crashes both increased.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
While the top contributing factors remained largely the same, the count for several key factors increased notably. Crashes attributed to "Failed to yield right of way" grew by 50%, from 44 incidents in 2021 to 66 in 2022. The count of crashes involving drivers who "Exceeded authorized speed limit" increased from 2 to 14. "Followed too closely" remained a leading cause, with its crash count increasing by 15.8% from 76 to 88.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Environmental conditions at the time of crashes remained proportionally stable year-over-year. The share of crashes on dry road surfaces (76.0% in 2021 vs. 73.4% in 2022) and in clear weather (63.0% vs. 64.5%) saw minimal change. There was a slight increase in the proportion of crashes occurring on dark but lighted roadways, which rose from 11.9% of all crashes in 2021 to 15.9% in 2022.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The profile of vehicles and people involved in crashes showed high consistency between the two years. The top three vehicle makes involved in crashes were Toyota, Honda, and Ford in both 2021 and 2022, with only minor changes in their total counts. Similarly, the age distribution of all persons involved in crashes did not change significantly, with all age groups maintaining a consistent proportional share across both periods.
Top Vehicle Makes (950 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
116 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (991 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones shifted slightly toward lower-speed roads. The proportion of collisions in zones with posted limits of 30 mph or less increased from 27.5% in 2021 to 31.5% in 2022. Correspondingly, the share of crashes in zones of 55 mph or higher decreased from 48.3% to 46.2%. The single fatal crash recorded in 2022 occurred in a 25 mph zone.
Fatal crashes by zone: 25 mph: 1 of 54 (1.852%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: LEXINGTON, MA
- Total crash records analyzed: 533
- Total persons involved: 1,202
- Total vehicles involved: 950
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). "LEXINGTON, MA Crash Intelligence Report: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lexington/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved