Yearly Traffic Safety Analysis

357 CRASHES IN
CONCORD, MA
2022

All metrics benchmarked against2021

In 2022, Concord recorded 357 total vehicle crashes, a 13.7% increase from the 314 crashes documented in 2021. While there were no fatalities in either period, the number of persons injured rose from 90 to 114. One of the most notable year-over-year changes was a 60% increase in the count of hit-and-run incidents, which grew from 10 to 16.

357

13.7%was 314

Total Crash Events

0

Persons Killed

114

26.7%was 90

Persons Injured

16

60.0%was 10

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. 9 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

Crash data for Concord indicates an upward trend year-over-year. Total crashes increased by 13.7%, rising from 314 in 2021 to 357 in 2022. Similarly, the number of individuals injured in these incidents grew by 26.7%, from 90 to 114, although no fatalities were reported in either year.

16

Hit-and-Run Crashes — 2022

60.0% vs prior (10)

The number of hit-and-run crashes in Concord increased by 60% year-over-year, rising from 10 incidents in 2021 to 16 in 2022. This represents an upward trend in the hit-and-run rate, which grew from 3.2% of all crashes in the prior year to 4.5% in the current year. The increase in both the absolute count and the proportional rate indicates a growing trend for this type of incident.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

3

Pedestrians Injured

Prior: 250.0%

5

Cyclists Injured

Prior: 9-44.4%

106

Motorists Injured

Prior: 7934.2%

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 temporal patterns of crashes in Concord remained largely consistent, with Tuesday being the peak day and 3 p.m. the peak hour in both 2021 and 2022. In 2022, Tuesday saw 68 crashes and the 3 p.m. hour saw 34, up from 52 and 30 respectively in the prior year. A notable pattern shift occurred in the weekly distribution, as weekday crashes (Monday-Friday) increased from 238 to 292, while weekend crashes (Saturday-Sunday) decreased from 76 to 65.

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

There were no fatal crashes in Concord during either 2022 or 2021. The distribution of injury severity shifted, with crashes resulting in minor injuries increasing from 46 in 2021 to 66 in 2022, representing a proportional rise from 14.6% to 18.5% of all crashes. Conversely, crashes involving serious injuries decreased from 4 to 3, and those with possible injuries fell from 25 to 17.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes0.8%
-25.0%prior 4
Minor Injury66minor injury crashes18.5%
43.5%prior 46
Possible Injury17possible injury crashes4.8%
-32.0%prior 25
No Injury262no injury crashes73.4%
12.4%prior 233

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

In 2022, the leading contributing factors were inattention (58 incidents) and following too closely (57 incidents). This marks a shift from 2021, when 'no improper driving' was the most cited factor with 88 incidents, a count that fell to 63 in 2022. The count of crashes attributed to inattention increased by 20.8% from 48 in the prior year, while those related to following too closely rose by 35.7% from 42. Crashes involving failure to keep in the proper lane also saw a significant increase in count, from 10 to 26 incidents.

Officer-Reported Primary Contributing Cause

No improper driving63 (17.6%)-28.4%prior 88
Inattention58 (16.2%)20.8%prior 48
Followed too closely57 (16%)35.7%prior 42
Failure to keep in proper lane or running off road26 (7.3%)160.0%prior 10
Failed to yield right of way26 (7.3%)36.8%prior 19
Other improper action11 (3.1%)0.0%prior 11
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner11 (3.1%)10.0%prior 10
Distracted10 (2.8%)-33.3%prior 15
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway9 (2.5%)
Over-correcting/over-steering9 (2.5%)

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

The majority of crashes in both 2022 and 2021 occurred in clear weather and daylight conditions, with proportions remaining stable year-over-year. In 2022, 73.1% of crashes happened during daylight, compared to 72.9% in 2021. There was a notable shift in road surface conditions, as crashes on wet roads increased from 34 incidents (10.8% of total) in 2021 to 54 incidents (15.1% of total) in 2022.

Weather

Clear263 (74.9%)
10.0%prior 239
Cloudy33 (9.4%)
32.0%prior 25
Rain24 (6.8%)
14.3%prior 21
Cloudy/Rain11 (3.1%)
Snow7 (2.0%)
40.0%prior 5
Snow/Blowing sand, snow2 (0.6%)
Rain/Cloudy2 (0.6%)
Snow/Rain1 (0.3%)
Blowing sand, snow1 (0.3%)
Clear/Blowing sand, snow1 (0.3%)

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

Lighting

Daylight261 (73.5%)
14.0%prior 229
Dark - lighted roadway49 (13.8%)
11.4%prior 44
Dark - roadway not lighted23 (6.5%)
-4.2%prior 24
Dusk13 (3.7%)
116.7%prior 6
Dawn7 (2.0%)
-30.0%prior 10
Dark - unknown roadway lighting2 (0.6%)

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

Road Surface

Dry284 (79.8%)
7.6%prior 264
Wet54 (15.2%)
58.8%prior 34
Snow12 (3.4%)
-7.7%prior 13
Slush5 (1.4%)
Ice1 (0.3%)

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

Vehicles & Demographics

Toyota, Honda, and Ford were consistently among the top three vehicle makes involved in crashes in both years, though their order shifted. In 2022, Ford (71 vehicles) surpassed Honda (61 vehicles) for the second-highest involvement, behind Toyota (118 vehicles). An analysis of persons involved shows a notable increase in several age groups between 2021 and 2022; the number of individuals aged 55-64 involved in crashes rose from 77 to 105, and those in the 26-34 age group increased from 105 to 126.

Top Vehicle Makes (654 vehicles)

1
TOYOTA118 (18%)
14.6%prior 103
2
FORD71 (10.9%)
51.1%prior 47
3
HONDA61 (9.3%)
-12.9%prior 70
4
CHEVROLET46 (7%)
53.3%prior 30
5
SUBARU46 (7%)
-8.0%prior 50
6
JEEP28 (4.3%)
-12.5%prior 32
7
NISSAN25 (3.8%)
31.6%prior 19
8
HYUNDAI24 (3.7%)
-4.0%prior 25
9
GMC21 (3.2%)
133.3%prior 9
10
BMW19 (2.9%)
-5.0%prior 20

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

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

Sex Distribution (668 persons with recorded sex)

Male378 (56.6%)
11.2%prior 340
Female290 (43.4%)
12.8%prior 257

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

Crashes occurred across a similar range of speed zones in both 2022 and 2021, with no fatalities recorded in any zone for either period. The most notable change was an increase in crashes within 25 mph zones, which rose from 62 incidents in 2021 to 82 in 2022. The number of crashes in other common speed zones, such as 30 mph (71 to 74) and 45 mph (77 in both years), remained relatively stable.

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: CONCORD, MA
  • Total crash records analyzed: 357
  • Total persons involved: 763
  • Total vehicles involved: 654

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). "CONCORD, 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/concord/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

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Concord, MA Crash Report — 2022 | ThatCarHitMe.com