Yearly Traffic Safety Analysis

504 CRASHES IN
DEDHAM, MA
2024

All metrics benchmarked against2023

In 2024, Dedham recorded 504 total traffic crashes, a 6.6% increase from the 473 crashes reported in 2023. While total crashes increased, the number of fatalities decreased from two to one. A notable year-over-year shift was the decrease in crashes involving suspected DUI, which fell from 13 in 2023 to 4 in 2024.

504

6.6%was 473

Total Crash Events

1

-50.0%was 2

Persons Killed

158

-7.6%was 171

Persons Injured

29

26.1%was 23

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the total number of crashes in Dedham increased by 6.6% from 473 in 2023 to 504 in 2024. Despite the rise in total collisions, the number of people injured decreased by 7.6% from 171 to 158, and fatalities fell from two to one.

29

Hit-and-Run Crashes — 2024

26.1% vs prior (23)

The number of hit-and-run incidents increased in 2024 compared to the previous year. The total count of hit-and-run crashes rose from 23 in 2023 to 29 in 2024, representing a 26.1% increase. Consequently, the hit-and-run rate, which measures the percentage of total crashes that were hit-and-runs, trended upward from 4.9% to 5.8%.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 2-50.0%

0

Other Killed

Prior: 00.0%

7

Pedestrians Injured

Prior: 3133.3%

4

Cyclists Injured

Prior: 333.3%

146

Motorists Injured

Prior: 164-11.0%

1

Other Injured

Prior: 10.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained broadly consistent year-over-year. The 5 PM hour was the peak time for crashes in both 2023 (51 crashes) and 2024 (44 crashes). In 2024, Tuesday emerged as the day with the most crashes (86), a slight shift from 2023 where Thursday (82) and Tuesday (79) were the most frequent days for collisions.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes showed a mixed but generally downward trend. The number of fatal crashes decreased from two in 2023 to one in 2024, and the proportion of crashes resulting in any injury fell from 28.1% to 24.6%. However, the count of serious injury crashes more than doubled from 4 to 10, even as crashes with minor or possible injuries decreased.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.2%
-50.0%prior 2
Serious Injury10serious injury crashes2%
150.0%prior 4
Minor Injury59minor injury crashes11.7%
-1.7%prior 60
Possible Injury55possible injury crashes10.9%
-20.3%prior 69
No Injury367no injury crashes72.8%
13.3%prior 324

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The top three contributing factors remained the same year-over-year: 'No improper driving,' 'Followed too closely,' and 'Inattention.' Crashes attributed to 'Followed too closely' increased in count by 35%, from 60 to 81, while those involving 'Inattention' rose by 50%, from 42 to 63. Conversely, crashes where 'Failed to yield right of way' was a factor decreased from 39 to 29.

Officer-Reported Primary Contributing Cause

No improper driving138 (27.4%)23.2%prior 112
Followed too closely81 (16.1%)35.0%prior 60
Inattention63 (12.5%)50.0%prior 42
Failed to yield right of way29 (5.8%)-25.6%prior 39
Failure to keep in proper lane or running off road25 (5%)-34.2%prior 38
Disregarded traffic signs, signals, road markings24 (4.8%)-11.1%prior 27
Driving too fast for conditions14 (2.8%)55.6%prior 9
Other improper action13 (2.6%)18.2%prior 11
Fatigued/asleep12 (2.4%)9.1%prior 11
Distracted9 (1.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in 2024 were more concentrated in ideal driving conditions compared to 2023. The proportion of crashes occurring in clear weather increased from 69.8% to 74.8% year-over-year. Similarly, collisions during daylight hours rose from 64.5% of all crashes in 2023 to 68.1% in 2024. The share of crashes on dry road surfaces remained stable at approximately 78% for both periods.

Weather

Clear/Clear258 (51.2%)
18.3%prior 218
Clear119 (23.6%)
6.3%prior 112
Cloudy/Cloudy19 (3.8%)
11.8%prior 17
Rain16 (3.2%)
14.3%prior 14
Rain/Cloudy15 (3.0%)
-21.1%prior 19
Cloudy/Rain14 (2.8%)
7.7%prior 13
Rain/Rain11 (2.2%)
-42.1%prior 19
Cloudy8 (1.6%)
-33.3%prior 12
Clear/Cloudy7 (1.4%)
-41.7%prior 12
Snow/Cloudy5 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight343 (68.1%)
12.5%prior 305
Dark - lighted roadway90 (17.9%)
-1.1%prior 91
Dark - roadway not lighted46 (9.1%)
2.2%prior 45
Dusk13 (2.6%)
18.2%prior 11
Dawn10 (2.0%)
-33.3%prior 15
Dark - unknown roadway lighting2 (0.4%)
-60.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry397 (78.9%)
7.0%prior 371
Wet83 (16.5%)
-9.8%prior 92
Snow14 (2.8%)
180.0%prior 5
Ice5 (1.0%)
Slush2 (0.4%)
Other1 (0.2%)
Sand, mud, dirt, oil, gravel1 (0.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The composition of vehicles and people involved in crashes showed some shifts between the two periods. While the top three vehicle makes remained Toyota, Honda, and Ford in both years, all saw an increase in their total counts. Analysis of persons involved shows the 35-44 age group experienced a notable increase, growing from 170 individuals in 2023 to 204 in 2024. The 26-34 age group remained consistently high, with 204 people involved in both years.

Top Vehicle Makes (968 vehicles)

1
TOYOTA195 (20.1%)
7.1%prior 182
2
HONDA138 (14.3%)
2.2%prior 135
3
FORD97 (10%)
22.8%prior 79
4
NISSAN51 (5.3%)
10.9%prior 46
5
CHEVROLET45 (4.6%)
-23.7%prior 59
6
SUBARU42 (4.3%)
-4.5%prior 44
7
JEEP37 (3.8%)
2.8%prior 36
8
HYUNDAI36 (3.7%)
100.0%prior 18
9
ACURA28 (2.9%)
55.6%prior 18
10
MERCEDES-BENZ23 (2.4%)
4.5%prior 22

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (1,082 persons with recorded sex)

Male628 (58.0%)
16.7%prior 538
Female454 (42.0%)
3.4%prior 439

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones shifted year-over-year. The 55 mph zone saw an increase in crashes from 126 to 147, solidifying its position as the most frequent location for collisions. In contrast, crashes in 30 mph zones decreased from 71 to 53. The single fatal crash in 2024 occurred in a 55 mph zone, whereas the two fatalities in 2023 occurred in lower speed zones of 35 mph and 40 mph.

Fatal crashes by zone: 55 mph: 1 of 147 (0.68%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: DEDHAM, MA
  • Total crash records analyzed: 504
  • Total persons involved: 1,162
  • Total vehicles involved: 968

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). "DEDHAM, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/dedham/2024-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

Dedham, MA Crash Report — 2024 | ThatCarHitMe.com