Yearly Traffic Safety Analysis

1,066 CRASHES IN
METHUEN, MA
2023

All metrics benchmarked against2022

In 2023, Methuen recorded 1,066 traffic crashes, a 10.7% decrease from the 1,193 crashes reported in 2022. Despite the overall reduction in collisions and a 7.7% drop in injuries from 439 to 405, the number of fatalities doubled from one in 2022 to two in 2023. The most cited contributing factor shifted from "Failed to yield right of way" in 2022 to "Followed too closely" in 2023.

1,066

-10.6%was 1,193

Total Crash Events

2

100.0%was 1

Persons Killed

405

-7.7%was 439

Persons Injured

55

7.8%was 51

Hit-and-Run Crashes

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 11 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 in Methuen shows a year-over-year decline. Total crashes decreased by 10.7%, from 1,193 in 2022 to 1,066 in 2023. Similarly, the number of people injured in these incidents fell by 7.7% from 439 to 405, though the number of fatalities increased from one to two.

55

Hit-and-Run Crashes — 2023

7.8% vs prior (51)

Despite a decrease in total collisions, the number of hit-and-run incidents increased year-over-year. The count of hit-and-run crashes rose by 7.8%, from 51 in 2022 to 55 in 2023. Consequently, the hit-and-run rate as a percentage of total crashes also increased, climbing from 4.3% in 2022 to 5.2% in 2023.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 1100.0%

9

Pedestrians Injured

Prior: 0%

2

Cyclists Injured

Prior: 0%

394

Motorists Injured

Prior: 439-10.3%

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

Temporal analysis reveals a shift in crash patterns between the two years. The peak day for crashes moved from Tuesday in 2022 (187 incidents) to Friday in 2023 (177 incidents). A more significant change occurred in the peak hour, which shifted from 3 p.m. in 2022 (101 crashes) to 12 a.m. in 2023 (123 crashes). While afternoon hours remained a common time for collisions in both periods, the concentration of incidents at midnight in 2023 marks a notable departure from the prior year's pattern.

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 declined, the severity of outcomes worsened in some respects. The fatal crash rate more than doubled, increasing from 0.08% in 2022 to 0.19% in 2023. The proportion of crashes resulting in any level of injury (serious, minor, or possible) rose from 26.3% of all crashes in 2022 to 29.1% in 2023. However, the count of serious injury crashes decreased from 20 to 14.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
100.0%prior 1
Serious Injury14serious injury crashes1.3%
-30.0%prior 20
Minor Injury158minor injury crashes14.8%
6.0%prior 149
Possible Injury138possible injury crashes12.9%
-4.8%prior 145
No Injury743no injury crashes69.7%
-14.2%prior 866

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 primary contributing factors for crashes shifted between 2022 and 2023. "Followed too closely" became the most cited factor in 2023, with its count increasing by 20.5% from 171 to 206 incidents. This displaced "Failed to yield right of way," which saw its count decrease by 11.9% from 176 to 155. Crashes attributed to "Inattention" also saw a significant reduction in count, dropping by 30.9% from 149 to 103.

Officer-Reported Primary Contributing Cause

Followed too closely206 (19.3%)20.5%prior 171
Failed to yield right of way155 (14.5%)-11.9%prior 176
No improper driving149 (14%)7.2%prior 139
Inattention103 (9.7%)-30.9%prior 149
Failure to keep in proper lane or running off road79 (7.4%)9.7%prior 72
Driving too fast for conditions73 (6.8%)1.4%prior 72
Disregarded traffic signs, signals, road markings39 (3.7%)-27.8%prior 54
Other improper action28 (2.6%)-17.6%prior 34
Distracted25 (2.3%)-49.0%prior 49
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner23 (2.2%)-28.1%prior 32

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 conditions under which crashes occurred saw some year-over-year changes. The proportion of crashes on dry roads decreased from 76.2% in 2022 to 69.9% in 2023, while crashes on wet roads increased from 17.9% to 22.1% of the total. This corresponds with a similar shift in weather conditions, as the share of crashes in clear weather decreased while the share during rain increased. Lighting conditions remained relatively stable, with approximately 67% of crashes in both years occurring during daylight.

Weather

Clear441 (42.4%)
21.8%prior 362
Clear/Clear259 (24.9%)
-48.0%prior 498
Rain90 (8.7%)
50.0%prior 60
Cloudy74 (7.1%)
15.6%prior 64
Cloudy/Cloudy35 (3.4%)
-39.7%prior 58
Rain/Rain26 (2.5%)
-53.6%prior 56
Cloudy/Rain24 (2.3%)
-17.2%prior 29
Rain/Cloudy21 (2.0%)
320.0%prior 5
Snow16 (1.5%)
33.3%prior 12
Snow/Snow14 (1.3%)
-39.1%prior 23

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight723 (67.8%)
-10.0%prior 803
Dark - lighted roadway213 (20.0%)
-12.3%prior 243
Dark - roadway not lighted84 (7.9%)
0.0%prior 84
Dusk26 (2.4%)
-16.1%prior 31
Dawn12 (1.1%)
-29.4%prior 17
Dark - unknown roadway lighting7 (0.7%)
-30.0%prior 10
Other1 (0.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry745 (72.5%)
-18.0%prior 909
Wet236 (23.0%)
10.8%prior 213
Snow31 (3.0%)
3.3%prior 30
Ice6 (0.6%)
-71.4%prior 21
Slush5 (0.5%)
-16.7%prior 6
Other3 (0.3%)
Sand, mud, dirt, oil, gravel1 (0.1%)
Water (standing, moving)1 (0.1%)
-88.9%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The demographic profile of persons and vehicles involved in crashes remained largely stable year-over-year. The age distribution of individuals in collisions was consistent, with no significant shifts in representation among different age groups. The top five vehicle makes involved in crashes were Honda, Toyota, Ford, Chevrolet, and Nissan in both years, with their relative rankings remaining nearly identical. The total count of vehicles involved in crashes decreased from 2,245 in 2022 to 2,054 in 2023, in line with the overall reduction in collisions.

Top Vehicle Makes (2,054 vehicles)

1
HONDA436 (21.2%)
-15.5%prior 516
2
TOYOTA261 (12.7%)
-18.2%prior 319
3
FORD198 (9.6%)
-10.4%prior 221
4
NISSAN124 (6%)
-5.3%prior 131
5
CHEVROLET121 (5.9%)
-22.9%prior 157
6
ACURA85 (4.1%)
2.4%prior 83
7
JEEP83 (4%)
-1.2%prior 84
8
SUBARU67 (3.3%)
24.1%prior 54
9
HYUNDAI60 (2.9%)
-16.7%prior 72
10
BMW55 (2.7%)
44.7%prior 38

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (2,374 persons with recorded sex)

Male1,370 (57.7%)
-6.0%prior 1,458
Female1,003 (42.2%)
-18.7%prior 1,233
X / Unspecified1 (0.0%)
0.0%prior 1

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

The distribution of crashes across speed zones remained broadly similar between 2022 and 2023, with the highest number of incidents in both years occurring in 30 mph and 35 mph zones. The locations of fatal crashes, however, shifted significantly. In 2022, the single fatal crash occurred in a 50 mph zone. In 2023, the two fatal crashes took place in lower speed zones: one in a 20 mph zone and one in a 30 mph zone.

Fatal crashes by zone: 20 mph: 1 of 7 (14.286%) · 30 mph: 1 of 264 (0.379%)

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: METHUEN, MA
  • Total crash records analyzed: 1,066
  • Total persons involved: 2,611
  • Total vehicles involved: 2,054

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). "METHUEN, 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/methuen/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

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Methuen, MA Crash Report — 2023 | ThatCarHitMe.com