Monthly Traffic Safety Analysis

155 CRASHES IN
CAMBRIDGE, MA
DECEMBER 2022

All metrics benchmarked againstDecember 2021

In December 2022, CAMBRIDGE experienced 155 crashes, an increase of 15.7% compared to the 134 crashes recorded in December 2021. Total injuries also saw a slight rise from 36 to 38. The most notable shift was a 50% increase in bicycle crashes, rising from 4 to 6.

155

15.7%was 134

Total Crash Events

0

Persons Killed

38

5.6%was 36

Persons Injured

48

-9.4%was 53

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. 30 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for CAMBRIDGE indicates an upward trend year-over-year, with total crashes increasing by 15.7% from 134 in December 2021 to 155 in December 2022. Total injuries also rose by 5.6%, from 36 to 38, while fatalities remained at zero in both periods.

48

Hit-and-Run Crashes — December 2022

-9.4% vs prior (53)

Hit-and-run crashes decreased from 53 in December 2021 to 48 in December 2022, representing a 9.4% reduction. The overall hit-and-run rate also decreased from 39.6% to 31%, indicating a downward trend in these incidents.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

0

Other Killed

Prior: 00.0%

6

Pedestrians Injured

Prior: 8-25.0%

3

Cyclists Injured

Prior: 30.0%

26

Motorists Injured

Prior: 248.3%

3

Other Injured

Prior: 1200.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Thursday in December 2021, with 26 crashes, to Friday in December 2022, with 39 crashes. The peak hour for crashes also changed, moving from 6 PM with 18 crashes in the prior period to 2 PM with 17 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at zero in both December 2021 and December 2022. The proportion of serious injury crashes increased slightly from 2.2% (3 crashes) to 2.6% (4 crashes) year-over-year. Conversely, possible injury crashes decreased from 6.7% (9 crashes) in December 2021 to 3.9% (6 crashes) in December 2022.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes2.6%
33.3%prior 3
Minor Injury22minor injury crashes14.2%
10.0%prior 20
Possible Injury6possible injury crashes3.9%
-33.3%prior 9
No Injury93no injury crashes60%
22.4%prior 76

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

The contributing factor 'Failed to yield right of way' saw a significant increase, rising from 1 crash in December 2021 to 12 crashes in December 2022, an 1100% change in count. 'Disregarded traffic signs, signals, road markings' also increased substantially, from 2 crashes to 9 crashes, a 350% change in count. Conversely, 'Inattention' decreased by 60%, from 10 crashes to 4 crashes.

Officer-Reported Primary Contributing Cause

No improper driving37 (23.9%)-2.6%prior 38
Failed to yield right of way12 (7.7%)
Disregarded traffic signs, signals, road markings9 (5.8%)
Distracted6 (3.9%)
Followed too closely5 (3.2%)
Inattention4 (2.6%)-60.0%prior 10
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway4 (2.6%)
Driving too fast for conditions3 (1.9%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (1.3%)
Glare2 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring during rainy weather conditions significantly increased from 9 in December 2021 to 27 in December 2022. Similarly, crashes on wet road surfaces rose from 24 in the prior period to 49 in the current period. Crashes occurring in 'Dark - lighted roadway' conditions also increased from 45 to 61.

Weather

Clear60 (42.9%)
-7.7%prior 65
Rain27 (19.3%)
200.0%prior 9
Clear/Clear12 (8.6%)
33.3%prior 9
Cloudy10 (7.1%)
-16.7%prior 12
Unknown/Unknown5 (3.6%)
Snow5 (3.6%)
Snow/Snow4 (2.9%)
Cloudy/Rain4 (2.9%)
Rain/Rain4 (2.9%)
Cloudy/Cloudy2 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight65 (47.1%)
12.1%prior 58
Dark - lighted roadway61 (44.2%)
35.6%prior 45
Dusk4 (2.9%)
-60.0%prior 10
Other3 (2.2%)
Dark - unknown roadway lighting2 (1.4%)
Dark - roadway not lighted2 (1.4%)
Dawn1 (0.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry75 (56.0%)
-2.6%prior 77
Wet49 (36.6%)
104.2%prior 24
Snow8 (6.0%)
Ice2 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The age group 26-34 experienced a 90.6% increase in persons involved in crashes, rising from 32 in December 2021 to 61 in December 2022. Toyota became the top vehicle make involved in crashes, with its count increasing by 90% from 30 to 57, surpassing Honda which increased by 25% from 36 to 45. Ford's involvement decreased by 46.7%, from 30 to 16.

Top Vehicle Makes (264 vehicles)

1
TOYOTA57 (21.6%)
90.0%prior 30
2
HONDA45 (17%)
25.0%prior 36
3
CHEVROLET17 (6.4%)
41.7%prior 12
4
FORD16 (6.1%)
-46.7%prior 30
5
SUBARU14 (5.3%)
0.0%prior 14
6
NISSAN9 (3.4%)
28.6%prior 7
7
HYUNDAI9 (3.4%)
8
JEEP7 (2.7%)
0.0%prior 7
9
MAZDA6 (2.3%)
20.0%prior 5
10
MERCEDES-BENZ6 (2.3%)
0.0%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Vehicle unit records

72 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (238 persons with recorded sex)

Male152 (63.9%)
18.8%prior 128
Female85 (35.7%)
3.7%prior 82
R1 (0.4%)
-75.0%prior 4

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 35 mph speed zones saw a substantial increase, rising from 3 in December 2021 to 16 in December 2022, representing a 433% change in count. Crashes in 20 mph zones also increased by 47.6%, from 21 to 31. No fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 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: 2022-12-01 through 2022-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-12-01 through 2022-12-31 (31 days)
  • Geographic scope: CAMBRIDGE, MA
  • Total crash records analyzed: 155
  • Total persons involved: 315
  • Total vehicles involved: 264

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). "CAMBRIDGE, MA Crash Intelligence Report: December 2022." Published June 21, 2026. Reporting period: 2022-12-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/cambridge/december-2022-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

Cambridge, MA Crash Report — December 2022 | ThatCarHitMe.com